@article{cole_baes_eaglen_lawlor_maltecca_ortega_vanraden_2025, title={Invited review: Management of genetic defects in dairy cattle populations}, url={https://doi.org/10.3168/jds.2024-26035}, DOI={10.3168/jds.2024-26035}, journal={Journal of Dairy Science}, author={Cole, John B. and Baes, Christine F. and Eaglen, Sophie A.E. and Lawlor, Thomas J. and Maltecca, Christian and Ortega, M. Sofía and VanRaden, Paul M.}, year={2025}, month={Feb} }
 @article{scott_haile-mariam_tiezzi_berg_maltecca_pryce_2025, title={Optimizing genetic diversity in Australian Holsteins and Jerseys: A comparative analysis of whole-genome and regional inbreeding depression effects}, volume={108}, ISSN={["1525-3198"]}, url={https://doi.org/10.3168/jds.2024-25341}, DOI={10.3168/jds.2024-25341}, number={3}, journal={JOURNAL OF DAIRY SCIENCE}, author={Scott, B. A. and Haile-Mariam, M. and Tiezzi, F. and Berg, I. and Maltecca, C. and Pryce, J. E.}, year={2025}, month={Mar}, pages={2658–2668} }
 @article{mancin_maltecca_huang_mantovani_tiezzi_2024, title={A first characterization of the microbiota-resilience link in swine}, volume={12}, ISSN={2049-2618}, url={http://dx.doi.org/10.1186/s40168-024-01771-7}, DOI={10.1186/s40168-024-01771-7}, abstractNote={Abstract Background The gut microbiome plays a crucial role in understanding complex biological mechanisms, including host resilience to stressors. Investigating the microbiota-resilience link in animals and plants holds relevance in addressing challenges like adaptation of agricultural species to a warming environment. This study aims to characterize the microbiota-resilience connection in swine. As resilience is not directly observable, we estimated it using four distinct indicators based on daily feed consumption variability, assuming animals with greater intake variation may face challenges in maintaining stable physiological status. These indicators were analyzed both as linear and categorical variables. In our first set of analyses, we explored the microbiota-resilience link using PERMANOVA, α-diversity analysis, and discriminant analysis. Additionally, we quantified the ratio of estimated microbiota variance to total phenotypic variance (microbiability). Finally, we conducted a Partial Least Squares-Discriminant Analysis (PLS-DA) to assess the classification performance of the microbiota with indicators expressed in classes. Results This study offers four key insights. Firstly, among all indicators, two effectively captured resilience. Secondly, our analyses revealed robust relationship between microbial composition and resilience in terms of both composition and richness. We found decreased α-diversity in less-resilient animals, while specific amplicon sequence variants (ASVs) and KEGG pathways associated with inflammatory responses were negatively linked to resilience. Thirdly, considering resilience indicators in classes, we observed significant differences in microbial composition primarily in animals with lower resilience. Lastly, our study indicates that gut microbial composition can serve as a reliable biomarker for distinguishing individuals with lower resilience. Conclusion Our comprehensive analyses have highlighted the host-microbiota and resilience connection, contributing valuable insights to the existing scientific knowledge. The practical implications of PLS-DA and microbiability results are noteworthy. PLS-DA suggests that host-microbiota interactions could be utilized as biomarkers for monitoring resilience. Furthermore, the microbiability findings show that leveraging host-microbiota insights may improve the identification of resilient animals, supporting their adaptive capacity in response to changing environmental conditions. These practical implications offer promising avenues for enhancing animal well-being and adaptation strategies in the context of environmental challenges faced by livestock populations.}, number={1}, journal={Microbiome}, publisher={Springer Science and Business Media LLC}, author={Mancin, Enrico and Maltecca, Christian and Huang, Yi Jian and Mantovani, Roberto and Tiezzi, Francesco}, year={2024}, month={Mar} }
 @article{mancin_maltecca_jiang_huang_tiezzi_2024, title={Capturing resilience from phenotypic deviations: a case study using feed consumption and whole genome data in pigs}, volume={25}, ISSN={["1471-2164"]}, DOI={10.1186/s12864-024-11052-0}, number={1}, journal={BMC GENOMICS}, author={Mancin, Enrico and Maltecca, Christian and Jiang, Jicaj and Huang, Yi Jian and Tiezzi, Francesco}, year={2024}, month={Nov} }
 @article{lozada-soto_maltecca_jiang_cole_vanraden_tiezzi_2024, title={Effects of germplasm exchange strategies on genetic gain, homozygosity, and genetic diversity in dairy stud populations: A simulation study}, volume={107}, ISSN={["1525-3198"]}, url={https://doi.org/10.3168/jds.2024-24992}, DOI={10.3168/jds.2024-24992}, number={12}, journal={JOURNAL OF DAIRY SCIENCE}, author={Lozada-Soto, Emmanuel A. and Maltecca, Christian and Jiang, Jicai and Cole, John B. and Vanraden, Paul M. and Tiezzi, Francesco}, year={2024}, month={Dec}, pages={11149–11163} }
 @article{sullivan_brito_schinckel_byrd_hernandez_diggs_maltecca_tiezzi_johnson_2024, title={Estimating the impact of genomic selection for thermotolerance and in utero heat stress on piglet body weight from birth to bacon}, volume={102}, ISSN={["1525-3163"]}, DOI={10.1093/jas/skae234.233}, abstractNote={Abstract The maternal response to heat stress (HS) has detrimental effects on swine offspring in the form of in utero heat stress (IUHS). Long-term effects of IUHS include reduced postnatal growth performance and lean muscle deposition. As such, developing mitigation strategies that reduce IUHS is essential to maximize economic return. Therefore, the study objective was to investigate whether genomic selection for HS tolerance (TOL) or HS sensitivity (SEN) would impact the postnatal growth of IUHS and in utero thermoneutral (IUTN) pigs from birth to market. We hypothesized that the IUHS pigs born to TOL dams would have improved growth relative to IUHS pigs born to SEN dams, but overall, IUHS pigs would have reduced growth compared with IUTN pigs. Pregnant gilts (n = 15 TOL and 13 SEN) were exposed to thermoneutral (TN; 17-20ºC; n = 7 TOL and 6 SEN) or heat stress (HS; cycling 26 to 36ºC; n = 8 TOL and 7 SEN) conditions from d 6 to 70 of gestation, and then all gilts were exposed to TN conditions until farrowing. Thirty-six offspring (barrows) were selected to represent each possible treatment combination: TOL+IUTN (n = 9), TOL+IUHS (n = 9), SEN+IUTN (n = 9), and SEN+IUHS (n = 9). Barrows were group-housed and fed a corn-soybean based diet that was provided with water ad libitum. Body weight (BW) of all barrows was measured at d 1, 21, 28, 35, 42, 49, 56, 84, 105, 126, 147, 168, and 189 of life to evaluate growth from birth (d 1) to market (d 189). Data were analyzed using the PROC GLIMMIX procedure of SAS with pig as the experimental unit and random effects being pen and litter. Body weights for each treatment group were fitted to a generalized Michaelis-Menten (GMM) function using the Nonlinear MIXED procedure of SAS with pig specific random effects for mature BW. On d 1, 126, and 168, IUHS pigs tended to have reduced BW (P < 0.10; 1.19 ± 0.07, 58.37 ± 4.94, 91.18 ± 7.26 kg, respectively) when compared with IUTN pigs (1.32 ± 0.07, 68.95 ± 4.94, 106.25 ± 7.26 kg, respectively). Overall, final BW was reduced (P = 0.04) for IUHS (111.08 ± 7.91 kg) versus IUTN (128.79 ± 7.91 kg) pigs, and estimated days to a market weight of 125 kg tended to be greater (P = 0.06) for IUHS (219.8 ± 10.8 d) versus IUTN (188.1 ± 10.21 d) barrows. The GMM model predicted that IUHS+TOL and IUTN+TOL barrows had different shaped BW growth curves with decreased age to achieve one-half of their predicted mature BW than IUHS+SEN and IUTN+SEN barrows. In conclusion, IUHS resulted in reduced growth rates overall; however, TOL improved some aspects of growth.}, journal={JOURNAL OF ANIMAL SCIENCE}, author={Sullivan, Caitlyn R. and Brito, Luiz F. and Schinckel, Allan P. and Byrd, MaryKate H. and Hernandez, Rick O. and Diggs, Shelby L. and Maltecca, Christian and Tiezzi, Francesco and Johnson, Jay S.}, year={2024}, month={Sep}, pages={199–200} }
 @article{hernandez_brito_byrd_musa_tiezzi_maltecca_johnson_2024, title={Evaluating the interaction between divergent genomic selection for heat stress tolerance in the F1 generation and in utero heat stress on piglet growth performance following weaning and transport}, volume={102}, ISSN={["1525-3163"]}, DOI={10.1093/jas/skae234.360}, abstractNote={Abstract In utero heat stress (IUHS) has long-term negative-effects on pigs during postnatal life. Specifically, IUHS pigs display greater stress responses following weaning and transport and have reduced growth performance among other issues. Although efforts have been made to mitigate IUHS through management and nutrition, the use of genomic selection to improve heat stress tolerance (TOL) in gestating sows and reduce the impacts of IUHS on developing offspring has not been extensively studied. Therefore, the study objective was to determine whether genomic selection for TOL or heat stress sensitivity (SEN) in F1 gilts would impact the postnatal growth performance of IUHS piglets following weaning and transport. We hypothesized that IUHS piglets derived from TOL gilts would have improved growth performance relative to IUHS piglets derived from SEN gilts, but that overall, IUHS piglets would have reduced growth performance when compared with IUTN piglets following weaning and transport. Twenty-eight TOL (n = 15) and SEN (n = 13) pregnant gilts were exposed to either thermoneutral (TN; 17 to 20ºC; n = 7 TOL and 6 SEN) or heat stress (HS; cycling 26 to 36ºC; n = 8 TOL and 7 SEN) conditions from d 6 to 70 of gestation, and then all gilts were exposed to TN conditions until farrowing. At weaning, mixed sex piglets were selected from each litter resulting in the following treatment combinations: TOL+ in utero thermoneutral (IUTN; n = 60), TOL+IUHS (n = 60), SEN+IUTN (n = 59), and SEN+IUHS (n = 58). Piglets were then transported for 12 h to simulate commercial conditions and then group housed in 40 nursery pens (n = 6 pigs/pen) for 5 wk. All pigs were fed a standard nursery diet containing primarily corn and soybean meal and feed and water were provided ad libitum. Body weights (BW) and feed disappearance were measured on d 1, 7, 14, 21, 28, and 35 post-weaning and transport and used to calculate average daily gain (ADG), average daily feed intake (ADFI), and Gain:Feed. Data were analyzed in R using a generalized linear mixed model with pen as the experimental unit for ADFI and Gain:Feed, and individual piglet was the experimental unit for BW and ADG. Overall, IUHS piglets had a reduction (P < 0.01) in ADG (6.3%), Gain:Feed (4.4%), and final BW (2.5%) when compared with IUTN piglets. Additionally, TOL piglets had an overall improvement (P < 0.01) in ADG (6.0%), ADFI (13.6%), and final BW (3.9%), but a decrease in Gain:Feed (5.8%) when compared with SEN piglets. No genomic by in utero treatment effects were observed with any comparison. In conclusion, IUHS had a negative impact and selection for TOL had a generally positive impact on postnatal growth performance, but genomic selection for TOL or SEN did not interact with IUHS or IUTN in the F1 generation.}, journal={JOURNAL OF ANIMAL SCIENCE}, author={Hernandez, Rick O. and Brito, Luiz F. and Byrd, MaryKate H. and Musa, Jacob and Tiezzi, Francesco and Maltecca, Christian and Johnson, Jay S.}, year={2024}, month={Sep}, pages={316–317} }
 @article{wen_johnson_gloria_araujo_maskal_hartman_de carvalho_rocha_huang_tiezzi_et al._2024, title={Genetic parameters for novel climatic resilience indicators derived from automatically-recorded vaginal temperature in lactating sows under heat stress conditions}, volume={56}, ISSN={1297-9686}, url={http://dx.doi.org/10.1186/s12711-024-00908-4}, DOI={10.1186/s12711-024-00908-4}, number={1}, journal={Genetics Selection Evolution}, publisher={Springer Science and Business Media LLC}, author={Wen, Hui and Johnson, Jay S. and Gloria, Leonardo S. and Araujo, Andre C. and Maskal, Jacob M. and Hartman, Sharlene Olivette and de Carvalho, Felipe E. and Rocha, Artur Oliveira and Huang, Yijian and Tiezzi, Francesco and et al.}, year={2024}, month={Jun} }
 @article{callegaro_tiezzi_maltecca_fabbri_bozzi_2024, title={Genetic parameters of functional longevity and associated traits in Italian Charolais and Limousine breeds}, volume={102}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skae354}, DOI={10.1093/jas/skae354}, abstractNote={This study aimed to estimate the genetic parameters of stayability (STAY) at different calvings using a single-step genomic best linear unbiased prediction (ssGBLUP) approach, comparing Gaussian-linear and threshold models in Italian Charolais and Limousine beef cattle. It also examined the genetic relationship between STAY and other traits to identify potential indicators of longevity and assessed the impact of STAY selection on economically important traits. STAY, a key trait for farm profitability, is defined as the probability of a cow surviving and remaining productive in the herd until a determined age. We evaluated STAY from the second to third calving and subsequent intervals (e.g., STAY23, STAY78), along with two fertility traits and several conformation traits. Data included 47,362 Limousine cows and 9,174 Charolais cows from 2,471 to 1,774 herds, respectively, born between 1977 and 2023. Analyses were performed fitting univariate threshold and Gaussian-linear animal models to estimate genetic parameters for STAY traits (STAY2 to STAY8) using ssGBLUP. Also, bivariate models were used to estimate genetic correlations between STAY and fertility and conformation traits. Heritabilities for STAY ranged from 0.13 to 0.11 and from 0.21 to 0.14 for Limousine, and from 0.14 to 0.11 and from 0.21 to 0.19 for Charolais, using Gaussian-linear and threshold models, respectively. Significant re-ranking of genotyped sires based on STAY traits was observed, particularly for more distant calvings (STAY8) compared to earlier ones (STAY3), indicating that STAY traits are genetically distinct. Genetic correlations were positive between STAY and conformation traits for Limousine. In Charolais, many traits were uncorrelated, but some conformation traits showed positive correlations, except for rump convexity, which had negative correlations with STAY. In conclusion, the heritability estimates of STAY suggests that genetic improvement for longevity in Limousine and Charolais herds is feasible. Selecting sires with consistently high genomic breeding values for STAY across early and late calvings highlights the importance of long-term longevity. Genetic correlations indicate that selection based on conformation traits could enhance herd survival by improving cow resilience for the Limousine. Instead for the Charolais some conformation traits showed positive correlations with STAY, while rump convexity had negative association, potentially affecting longevity.}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Callegaro, Simone and Tiezzi, Francesco and Maltecca, Christian and Fabbri, Maria Chiara and Bozzi, Riccardo}, year={2024}, month={Nov} }
 @article{wen_johnson_mulim_araujo_de carvalho_rocha_huang_tiezzi_maltecca_schinckel_et al._2024, title={Genomic regions and biological mechanisms underlying climatic resilience traits derived from automatically-recorded vaginal temperature in lactating sows under heat stress conditions}, volume={15}, ISSN={1664-8021}, url={http://dx.doi.org/10.3389/fgene.2024.1498380}, DOI={10.3389/fgene.2024.1498380}, abstractNote={Climate change poses a growing threat to the livestock industry, impacting animal productivity, animal welfare, and farm management practices. Thus, enhancing livestock climatic resilience (CR) is becoming a key priority in various breeding programs. CR can be defined as the ability of an animal to be minimally affected or rapidly return to euthermia under thermally stressful conditions. The primary study objectives were to perform genome-wide association studies for 12 CR indicators derived from variability in longitudinal vaginal temperature in lactating sows under heat stress conditions. A total of 31 single nucleotide polymorphisms (SNPs) located on nine chromosomes were considered as significantly associated with nine CR indicators based on different thresholds. Among them, only two SNPs were simultaneously identified for different CR indicators, SSC6:16,449,770 bp and SSC7:39,254,889 bp. These results highlighted the polygenic nature of CR indicators with small effects distributed across different chromosomes. Furthermore, we identified 434 positional genes associated with CR. Key candidate genes include SLC3A2 , STX5 , POLR2G , and GANAB , which were previously related to heat stress responses, protein folding, and cholesterol metabolism. Furthermore, the enriched KEGG pathways and Gene Ontology (GO) terms associated with these candidate genes are linked to stress responses, immune and inflammatory responses, neural system, and DNA damage and repair. The most enriched quantitative trait loci are related to “Meat and Carcass”, followed by “Production”, “Reproduction”, “Health”, and “Exterior (conformation and appearance)” traits. Multiple genomic regions were identified associated with different CR indicators, which reveals that CR is a highly polygenic trait with small effect sizes distributed across the genome. Many heat tolerance or HS related genes in our study, such as HSP90AB1 , DMGDH , and HOMER1 , have been identified. The complexity of CR encompasses a range of adaptive responses, from behavioral to cellular. These results highlight the possibility of selecting more heat-tolerant individuals based on the identified SNP for CR indicators.}, journal={Frontiers in Genetics}, publisher={Frontiers Media SA}, author={Wen, Hui and Johnson, Jay S. and Mulim, Henrique A. and Araujo, Andre C. and De Carvalho, Felipe E. and Rocha, Artur O. and Huang, Yijian and Tiezzi, Francesco and Maltecca, Christian and Schinckel, Allan P. and et al.}, year={2024}, month={Nov} }
 @article{déru_tiezzi_van raden_lozada-soto_toghiani_maltecca_2024, title={Imputation accuracy from low- to medium-density SNP chips for US crossbred dairy cattle}, volume={107}, ISSN={0022-0302}, url={http://dx.doi.org/10.3168/jds.2023-23250}, DOI={10.3168/jds.2023-23250}, abstractNote={This study aimed at evaluating the quality of imputation accuracy (IA) by marker (IA}, number={1}, journal={Journal of Dairy Science}, publisher={American Dairy Science Association}, author={Déru, Vanille and Tiezzi, Francesco and Van Raden, Paul M. and Lozada-Soto, Emmanuel A. and Toghiani, Sajjad and Maltecca, Christian}, year={2024}, month={Jan}, pages={398–411} }
 @article{musa_byrd_casey_brito_suarez-trujillo_schinckel_maltecca_tiezzi_johnson_2024, title={Influence of early gestational heat stress on biomarkers of mammary gland development in replacement gilts genomically selected for thermotolerance or thermosensitivity}, volume={102}, ISSN={["1525-3163"]}, DOI={10.1093/jas/skae102.378}, abstractNote={Abstract Heat stress (HS) alters physiological and metabolic processes in lactating sows leading to decreased milk production, which is a major factor limiting growth and survivability of piglets. While genomic selection for thermotolerance may be a viable solution to alleviate the negative effects of HS on pig welfare, it may be linked to a reduction in milk production and subsequently, litter growth performance. Therefore, the study objective was to evaluate the effects of genomic selection for thermotolerance and its interaction with early gestational HS on biomarkers of mammary gland development in replacement gilts. We hypothesized that HS exposure as well as genomic selection for thermotolerance would negatively affect mammary epithelial proliferation rate. A total of 36 Landrace (33%) × Large White (67%) crossbred gilts divergently selected for thermotolerance (TOL; n = 18) or thermosensitivity (SEN; n = 18) were balanced by body weight, bred to a single Duroc sire, and then exposed to either thermoneutral (TN; constant 17 to 22°C) or cyclical HS (26 to 36°C) conditions until d 65 of gestation. From d 66 of gestation until farrowing, all pregnant gilts were exposed to TN conditions. Of the 36 total gilts bred, only 28 became pregnant yielding 15 HS gilts (n = 8 SEN and 7 TOL) and 13 TN gilts (n = 7 SEN and 6 TOL). On d 105 of gestation, a mammary biopsy was taken from all gilts, mammary tissue was placed into 10% buffered formalin, and KI67 immunohistochemical staining was performed to identify proliferating mammary epithelial cells (MEC). Proliferating and non-proliferating MEC populations were counted using ImageJ tool. Data were analyzed using PROC GLM in SAS 9.4 with individual gilt as the experimental unit. Overall, early gestational HS decreased (P < 0.01; 16.53 ± 2.12%) the percent of proliferating MEC relative to those kept under TN conditions (26.42 ± 2.39%). However, no effect of genomic line divergence was detected with any comparison (P > 0.05). In conclusion, early gestation HS, but not genomic selection for thermotolerance, had a negative impact on biomarkers of mammary gland development in replacement gilts. These data may suggest that early gestation HS could have a reductive effect on milk production capacity, which may negatively affect litter growth.}, journal={JOURNAL OF ANIMAL SCIENCE}, author={Musa, Jacob and Byrd, Mary Kate and Casey, Theresa M. and Brito, Luiz F. F. and Suarez-Trujillo, Aridany and Schinckel, Allan P. and Maltecca, Christian and Tiezzi, Francesco and Johnson, Jay S.}, year={2024}, month={May}, pages={331–332} }
 @article{chiara fabbri_tiezzi_crovetti_maltecca_bozzi_2024, title={Investigation of cosmopolitan and local Italian beef cattle breeds uncover common patterns of heterozygosity}, volume={3}, ISSN={1751-7311}, url={http://dx.doi.org/10.1016/j.animal.2024.101142}, DOI={10.1016/j.animal.2024.101142}, abstractNote={The analysis of livestock heterozygosity is less common compared to the study of homozygous patterns. Heterozygous-Rich Regions (HRRs) may harbor significant loci for functional traits such as immune response, survival rate, and fertility. For this reason, this study was conducted to investigate and characterize the heterozygosity patterns of four beef cattle breeds, which included two cosmopolitan breeds (Limousine and Charolaise) and two local breeds (Sarda and Sardo Bruna). Our analysis identified regions with a high degree of heterozygosity using a consecutive runs approach, the Tajima D test, nucleotide diversity estimation, and Hardy Weinberg equilibrium test. These regions exhibited recurrent heterozygosity peaks and were consistently found on specific chromosomes across all breeds, specifically autosomes 15, 16, 20, and 23. The cosmopolitan and Sardo Bruna breeds also displayed peaks on autosomes 2 and 21, respectively. Thirty-five top runs shared by more than 25% of the populations were identified. These genomic fragments encompassed 18 genes, two of which are directly linked to male fertility, while four are associated with lactation. Two other genes play roles in survival and immune response. Our study also detected a region related to growth and carcass traits in Limousine breed. Our analysis of heterozygosity-rich regions revealed particular segments of the cattle genome linked to various functional traits. It appears that balancing selection is occurring in specific regions within the four examined breeds, and unexpectedly, they are common across cosmopolitan and local breeds. The genes identified hold potential for applications in breeding programs and conservation studies to investigate the phenotypes associated with these heterozygous genotypes. In addition, Tajima D test, Nucleotide diversity, and Hardy Weinberg equilibrium test confirmed the presence of heterozygous fragments found with Heterozygous-Rich Regions analysis.}, journal={animal}, publisher={Elsevier BV}, author={Chiara Fabbri, Maria and Tiezzi, Francesco and Crovetti, Alessandro and Maltecca, Christian and Bozzi, Riccardo}, year={2024}, month={Mar}, pages={101142} }
 @article{tiezzi_schwab_shull_maltecca_2024, title={Multiple-trait genomic prediction for swine meat quality traits using gut microbiome features as a correlated trait}, volume={7}, ISSN={["1439-0388"]}, url={https://doi.org/10.1111/jbg.12887}, DOI={10.1111/jbg.12887}, abstractNote={Abstract Traits such as meat quality and composition are becoming valuable in modern pork production; however, they are difficult to include in genetic evaluations because of the high phenotyping costs. Combining genomic information with multiple‐trait indirect selection with cheaper indicator traits is an alternative for continued cost‐effective genetic improvement. Additionally, gut microbiome information is becoming more affordable to measure using targeted rRNA sequencing, and its applications in animal breeding are becoming relevant. In this paper, we investigated the usefulness of microbial information as a correlated trait in selecting meat quality in swine. This study incorporated phenotypic data encompassing marbling, colour, tenderness, loin muscle and backfat depth, along with the characterization of gut (rectal) microbiota through 16S rRNA sequencing at three distinct time points of the animal's growth curve. Genetic progress estimation and cross‐validation were employed to evaluate the utility of utilizing host genomic and gut microbiota information for selecting expensive‐to‐record traits in crossbred individuals. Initial steps involved variance components estimation using multiple‐trait models on a training dataset, where the top 25 associated operational taxonomic units (OTU) for each meat quality trait and time point were included. The second step compared the predictive ability of multiple‐trait models incorporating different numbers of OTU with single‐trait models in a validation set. Results demonstrated the advantage of including genomic information for some traits, while in some instances, gut microbial information proved advantageous, namely, for marbling and pH. The study suggests further investigation into the shared genetic architecture between microbial features and traits, considering microbial data's compositional and high‐dimensional nature. This research proposes a straightforward method to enhance swine breeding programs for improving costly‐to‐record traits like meat quality by incorporating gut microbiome information.}, journal={JOURNAL OF ANIMAL BREEDING AND GENETICS}, author={Tiezzi, Francesco and Schwab, Clint and Shull, Caleb and Maltecca, Christian}, year={2024}, month={Jul} }
 @article{déru_tiezzi_carillier-jacquin_blanchet_cauquil_zemb_bouquet_maltecca_gilbert_2024, title={The potential of microbiota information to better predict efficiency traits in growing pigs fed a conventional and a high-fiber diet}, volume={56}, ISSN={1297-9686}, url={http://dx.doi.org/10.1186/s12711-023-00865-4}, DOI={10.1186/s12711-023-00865-4}, abstractNote={Abstract Background Improving pigs’ ability to digest diets with an increased dietary fiber content is a lever to improve feed efficiency and limit feed costs in pig production. The aim of this study was to determine whether information on the gut microbiota and host genetics can contribute to predict digestive efficiency (DE, i.e. digestibility coefficients of energy, organic matter, and nitrogen), feed efficiency (FE, i.e. feed conversion ratio and residual feed intake), average daily gain, and daily feed intake phenotypes. Data were available for 1082 pigs fed a conventional or high-fiber diet. Fecal samples were collected at 16 weeks, and DE was estimated using near‑infrared spectrometry. A cross-validation approach was used to predict traits within the same diet, for the opposite diet, and for a combination of both diets, by implementing three models, i.e. with only genomic (Gen), only microbiota (Micro), and both genomic and microbiota information (Micro+Gen). The predictive ability with and without sharing common sires and breeding environment was also evaluated. Prediction accuracy of the phenotypes was calculated as the correlation between model prediction and phenotype adjusted for fixed effects. Results Prediction accuracies of the three models were low to moderate (< 0.47) for growth and FE traits and not significantly different between models. In contrast, for DE traits, prediction accuracies of model Gen were low (< 0.30) and those of models Micro and Micro+Gen were moderate to high (> 0.52). Prediction accuracies were not affected by the stratification of diets in the reference and validation sets and were in the same order of magnitude within the same diet, for the opposite diet, and for the combination of both diets. Prediction accuracies of the three models were significantly higher when pigs in the reference and validation populations shared common sires and breeding environment than when they did not ( P < 0.001). Conclusions The microbiota is a relevant source of information to predict DE regardless of the diet, but not to predict growth and FE traits for which prediction accuracies were similar to those obtained with genomic information only. Further analyses on larger datasets and more diverse diets should be carried out to complement and consolidate these results.}, number={1}, journal={Genetics Selection Evolution}, publisher={Springer Science and Business Media LLC}, author={Déru, Vanille and Tiezzi, Francesco and Carillier-Jacquin, Céline and Blanchet, Benoit and Cauquil, Laurent and Zemb, Olivier and Bouquet, Alban and Maltecca, Christian and Gilbert, Hélène}, year={2024}, month={Jan} }
 @article{ackerson_kuhn_gondro_jiang_maltecca_rohrer_rosen_smith_tuggle_huang_2024, title={Trio-binning Assemblies of the Duroc and Landrace swine breeds}, volume={102}, ISSN={["1525-3163"]}, DOI={10.1093/jas/skae234.589}, abstractNote={Abstract The current swine reference genome, based on a single Duroc individual, has contributed to many significant advances but also carries limitations in many applications. Reference alleles receive substantial bias from alignment-based approaches. Additionally, structural DNA variants are difficult to identify and represent relative to the linear reference genome. As a result, DNA variants more likely to affect complex quantitative traits are understudied and warrant further investigation. In this study, a trio-binning approach was utilized to produce haplotype-resolved assemblies of a Duroc x Landrace hybrid. The Duroc sire and Landrace dam were sequenced via short-read technology (Illumina), and the hybrid offspring was sequenced via long-read technology (PacBio HiFi). A total of 117 Gb HiFi data was produced, equivalent to approximately 47X coverage of the swine genome. Two highly contiguous and high-quality assemblies were produced using hifiasm. The assembled maternal (Landrace) and paternal (Duroc) genomes had a contig N50 of 76.7 Mb and 55.0 Mb, respectively, both of which surpass that of the current reference genome Sscrofa11.1 (48.2 Mb). Furthermore, the maternal and paternal genomes contained 113.6 Mb and 116.0 Mb of novel sequences relative to the reference, respectively. The Benchmarking Universal Single-Copy Orthologue (BUSCO) scores were approximately 96%, indicating high completeness. The computed assembly QVs were found to be >Q40. Compared with short-read technology, whole genome mapping identified substantially more large SVs (> 50bp). These haplotype-resolved assemblies and additional existing assemblies will serve as the basis for the production of a swine pangenome.}, journal={JOURNAL OF ANIMAL SCIENCE}, author={Ackerson, Leland K. and Kuhn, Kristen and Gondro, Cedric and Jiang, Jicai and Maltecca, Christian and Rohrer, Gary A. and Rosen, Benjamin D. and Smith, Timothy and Tuggle, Christopher K. and Huang, Wen}, year={2024}, month={Sep}, pages={523–523} }
 @article{ackerson_kuhn_gondro_jiang_maltecca_rohrer_rosen_smith_tuggle_huang_2024, title={Trio-binning Assemblies of the Duroc and Landrace swine breeds}, volume={102}, ISSN={["1525-3163"]}, DOI={10.1093/jas/skae234.5}, journal={JOURNAL OF ANIMAL SCIENCE}, author={Ackerson, Leland K. and Kuhn, Kristen and Gondro, Cedric and Jiang, Jicai and Maltecca, Christian and Rohrer, Gary A. and Rosen, Benjamin D. and Smith, Timothy and Tuggle, Christopher K. and Huang, Wen}, year={2024}, month={Sep}, pages={523–523} }
 @article{obari_makanjuola_schenkel_miglior_maltecca_baes_2023, title={121 Quantifying Genetic Relationships to Maintain Genetic Diversity in the Canadian Dairy Population}, volume={101}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skad281.019}, DOI={10.1093/jas/skad281.019}, abstractNote={Abstract Intensive selection pressure coupled with the use of a limited number of high genetic merit sires has resulted in reduced effective population size and increased levels of inbreeding in dairy cattle populations. The increased rate of inbreeding amplifies the frequency of recessive deleterious alleles, possibly leading to inbreeding depression in economically important traits, such as disease resistance, fertility and production. To better understand the relationship of available and active breeding sires to Canadian herds, quantifying genetic relationships between these groups could prove insightful. The genetic relationship value (R-value) represents the average number of alleles identical by descent shared between an animal and a reference population and is currently estimated based on pedigree information. Furthermore, the average R-value indicates the relationship of a sire to the rest of the population. Estimating the R-value between sires and individual herds may offer a more refined tool for producers to select sires that are less related to their specific herds. The aim of this study was to quantify and characterize R-values between individual sires and individual herds within the Canadian Holstein population. To quantify R-values, a dataset comprised of 11,914 sires born between 1953 and 2020 and 584,740 active cows born between 1997 and 2022 from 5,592 herds was considered. Active cows were defined as those currently alive, on milk recording, and actively contributing to the national milk inventory. Active sires were considered those used to breed the active cows. All data were provided by Lactanet Canada and analyses were carried out on PEDIG using the par2 program for relationship estimation. Results indicate that herd-level R-values (i.e., R-values estimated using individual sires and individual herds) showed variation. The R-value of individual sires to individual herds containing active cows ranged from 0.43% to 32.38. This result indicates that outbred sires are available to individual farmers when the population is considered at the herd level, and that the development of herd-level R-values may provide a selection tool which promotes the use of less related sires. Next steps include developing and estimating genomic R-values and comparing them with the pedigree-based R-values for the genotyped Canadian Holstein population. By better understanding of the relationship of available breeding sires to individual Canadian herds, a simple tool for producers to identify sires which are less related to their herds can be developed.}, number={Supplement_3}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Obari, Christiana O and Makanjuola, Bayode O and Schenkel, Flavio S and Miglior, Filippo and Maltecca, Christian and Baes, Christine F}, year={2023}, month={Nov}, pages={15–16} }
 @article{wang_maltecca_tiezzi_huang_jiang_2023, title={123 Benchmarking of Artificial Neural Network Models for Genomic Prediction of Quantitative Traits in Pigs}, volume={101}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skad281.022}, DOI={10.1093/jas/skad281.022}, abstractNote={Abstract Artificial neural networks (ANN) are a type of machine learning model that has been applied to various genomic problems, with the ability to learn non-linear relationships and model high-dimensional data. ANNs also have the potential in genomic prediction by capturing the intricate relationship between genetic variants and phenotypes. However, there is currently a limited effort to investigate the performance and feasibility of ANNs for pig genomic predictions. In this study, we evaluated the predictive performance of TensorFlow’s ANN models with one-layer, two-layer, and three-layer structures (with zero, one, and two hidden layers, respectively), in comparison with five linear methods, including GBLUP, LDAK, BayesR, SLEMM and scikit-learn’s ridge regression using data of six quantitative traits including off-test body weight (WT), off-test back fat thickness (BF), off-test loin muscle depth (MS), number of piglets born alive (NBA), number of piglets born dead (NBD), and number of piglets weaned (NW). Furthermore, we assessed the computational efficiency of ANNs on both CPU and GPU. The benchmarking was based on cross-validations of 26,190 genotyped pigs. We employed hyperband tuning to optimize the hyper-parameters and select the best model among one-layer, two-layer, and three-layer structures. Results showed that the one-layer structure, which is equivalent to ridge regression, yielded the best performance comparable to that of GBLUP. Using the optimal hyper-parameters for two-layer and three-layer structures, ANNs underperformed GBLUP in terms of accuracy. Of the five linear methods, BayesR and SLEMM performed similarly and the best, followed by LDAK, scikit-learn’s ridge regression, and GBLUP. Moreover, SLEMM was the fastest, which completed training with 21k individuals and 30k SNPs in 2.6 minutes. Compared with CPUs, GPUs exhibited a comparable computational speed for one-layer ANN but offered significant gains in computational efficiency for multi-layer ANNs. Based on our analysis of optimal hyper-parameters for two-layer ANN with BF, we found that using a GPU can lead to a five-fold increase in processing speed compared with using a conventional CPU, but it is still slower than GBLUP. In addition, hyper-parameter tuning (particularly for L2 regularization and the number of dense units in hidden layers) is critical for improving the genomic prediction accuracy in pigs. In conclusion, we found ANN with up to three layers could not improve genomic predictions compared with routine linear methods for pig quantitative traits.}, number={Supplement_3}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Wang, Junjian and Maltecca, Christian and Tiezzi, Francesco and Huang, Yijian and Jiang, Jicai}, year={2023}, month={Nov}, pages={17–18} }
 @article{gluck_bowman_layton_stuska_maltecca_pratt-phillips_2023, title={3 A comparison of the equine fecal microbiome within different horse populations}, volume={124}, ISSN={0737-0806}, url={http://dx.doi.org/10.1016/j.jevs.2023.104305}, DOI={10.1016/j.jevs.2023.104305}, abstractNote={The equine fecal microbiome may vary across horse populations due to the diversity of the habitual diet. The purpose of this study was to assess and compare the microbial population of different horse populations, specifically the differences between feral versus domesticated populations. Samples were collected from 3 different populations of horses: horses from the Shackleford Banks (n = 24), a feral horse population living on the Outer Banks of North Carolina who eat native grasses such as Spartina marsh and island grasses; horses from the NCSU Equine Educational Unit (n = 18) that are predominantly kept on cool season mixed pastures and may be supplemented with hay and concentrates from time to time; and finally, privately owned horses (n = 36) that are fed mixed diets consisting of pasture, hay and concentrates. Horses were monitored and samples were collected immediately following a void by swabbing the middle of the void. Swabs were placed in a tube containing 500 uL DNA/RNA shield (Zymo Research, Irvine, CA) and were sent to the Emerging Technology Center (Purina Animal Nutrition, Gray Summit, MO) where they were stored at −80°C and then the V3 and V4 regions of the 16S rRNA gene were sequenced following the Illumina 16S Protocol (San Diego, CA). Samples were processed, filtered and trimmed through DADA2 using the QIIME2 pipeline. Statistical analysis was performed in R(Version 4.1.1) and a P-value of ≤0.05 was considered significant. After processing to eliminate samples with low sampling depth (<20,085), 78 total samples across the 3 populations were analyzed. For the results, when testing α diversity with Shannon's Index, a Kruskal-Wallis rank sum test revealed a significant difference between all populations (P = 0.01). There was a visual distinction between the Shackleford Banks population compared with the others when utilizing Bray-Curtis to assess β diversity. Additionally, an apparent significant difference between all populations using the PERMANOVA UniFrac test (P < 0.001) was observed. The 3 most predominant bacterial phylum seen across all populations were Firmicutes, Bacteroidetes and Spirochaetes. The top 5 phyla observed in the Shackleford Banks population were Firmicutes, Bacteroidetes, Spirochaetes, Kiritimatiellaeota and Fibrobacteres. Based on the results, there is a distinctive separation in microbial diversity between these horse populations, specifically between the Shackleford Banks horses versus the NCSU and privately owned horses. This separation is likely due to the habitual diet of these specific horse populations influencing the composition of their microbiome within the hindgut.}, journal={Journal of Equine Veterinary Science}, publisher={Elsevier BV}, author={Gluck, C. and Bowman, M. and Layton, J. and Stuska, S. and Maltecca, C. and Pratt-Phillips, S.}, year={2023}, month={May}, pages={104305} }
 @article{byrd_brito_wen_freitas_hartman_maskal_huang_tiezzi_maltecca_schinckel_et al._2023, title={381 Evaluating Indirect Measures of Milk Production in Heat-Stressed Lactating Sows Genomically Selected for Improved Thermotolerance}, volume={101}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skad281.371}, DOI={10.1093/jas/skad281.371}, abstractNote={Abstract Lactating sows are among the most heat stress sensitive population in the swine herd. Exposure to high ambient temperatures causes a marked decrease in production measures and welfare in lactating sows and may have a negative downstream impact on piglet growth due to impaired sow lactogenesis. Genomic selection for improved heat stress (HS) tolerance may be a viable method to reduce the negative impacts of HS on lactating sows and their offspring. However, selection for HS tolerance is generally associated with decreased productivity. Therefore, the study objective was to evaluate the impact of genomic selection for thermotolerance on indirect measures of sow milk production under HS conditions. We hypothesized that heat tolerant sows would have a decrease in indirect measures of milk production when compared with heat sensitive sows. A total of 20 multiparous lactating sows (Large White x Landrace; parity = 4.85 ± 0.75) divergently genomically selected for heat tolerance (TOL; n = 11) or heat sensitivity (SEN; n = 9) were subjected to cyclic HS temperatures (28 to 32° C) from day 2.5 ± 1.0 post-farrowing until weaning (d 21.3 ± 1.1). On d 4, 8, 14, and 18 of lactation, indirect calorimetry was used to estimate total heat production (THP) on an individual sow basis following previously published methods by our group. In addition, sow respiration rate (RR) was measured daily at 0800, 1200, 1600, and 2000 h. All data were analyzed using PROC GLIMMIX in SAS 9.4 with individual sow included as the experimental unit. It was determined that TOL sows had an overall increase (P = 0.04; 5.68 ± 0.24 kcal·sow· kg BW0.75(-1)) in THP when compared with SEN sows (4.74 ± 0.18 kcal·sow· kg BW0.75(-1)). A treatment by day effect was observed where THP was greater (P < 0.01; 88%) for TOL sows on d 4 of lactation when compared with SEN sows. Preliminary evidence suggests that TOL sows had an overall increase (P = 0.01) in RR (78 ± 5 bpm) when compared with SEN sows (73 ± 5 bpm). Taken together, these data suggest that genomic selection methods used to increase lactating sow thermotolerance may have had a positive impact on indirect measures of lactogenesis under HS conditions.}, number={Supplement_3}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Byrd, MaryKate and Brito, Luiz F F and Wen, Hui and Freitas, Pedro H F and Hartman, Sharlene and Maskal, Jacob M and Huang, Yijian and Tiezzi, Francesco and Maltecca, Christian and Schinckel, Allan P and et al.}, year={2023}, month={Nov}, pages={311–312} }
 @misc{mancin_maltecca_huang_mantovani_tiezzi_2023, title={A first characterization of the Microbiota-Resilience Link in Swine}, url={http://dx.doi.org/10.21203/rs.3.rs-3236814/v1}, DOI={10.21203/rs.3.rs-3236814/v1}, abstractNote={Abstract Background The gut microbiome plays a crucial role in understanding complex biological mechanisms, including host resilience to stressors. Investigating the microbiota-resilience link in animals and plants holds relevance in addressing challenges like adaptation of agricultural species to a warming environment. This study aims to characterize the microbiota-resilience connection in swine. As resilience is not directly observable, we estimated it using four distinct indicators based on daily feed consumption variability, assuming animals with greater intake variation may face challenges in maintaining stable physiological status. These indicators were analyzed both as linear and categorical variables. In our first set of analyses, we explored the microbiota-resilience link using PERMANOVA, α-diversity analysis, and Discriminant Analysis. Additionally, we quantified the ratio of estimated microbiota variance to total phenotypic variance (microability). Finally, we conducted a Partial Least Squares Discriminant Analysis (PLS-DA) to assess the classification performance of the microbiota with indicators expressed in classes. Results This study offers four key insights. Firstly, among all indicators, two effectively captured resilience. Secondly, our analyses revealed robust relationship between microbial composition and resilience in terms of both composition and richness. We found decreased α-diversity in less resilient animals, while specific Amplicon Sequence Variants (ASVs) and KEGG pathways associated with inflammatory responses were negatively linked to resilience. Thirdly, considering resilience indicators in classes, we observed significant differences in microbial composition primarily in animals with lower resilience. Lastly, our study indicates that gut microbial composition can serve as a reliable biomarker for distinguishing individuals with lower resilience. Conclusion Our comprehensive analyses have highlighted the host-microbiota and resilience connection, contributing valuable insights to the existing scientific knowledge. The practical implications of PLS-DA and microability results are noteworthy. PLS-DA suggests that host-microbiota interactions could be utilized as biomarkers for monitoring resilience. Furthermore, the microability findings show that leveraging host-microbiota insights may improve the selection of resilient animals, supporting their adaptive capacity in response to changing environmental conditions. These practical implications offer promising avenues for enhancing animal well-being and adaptation strategies in the context of environmental challenges faced by livestock populations.}, publisher={Research Square Platform LLC}, author={Mancin, Enrico and Maltecca, Christian and Huang, Yi Jian and Mantovani, Roberto and Tiezzi, Francesco}, year={2023}, month={Aug} }
 @article{eudy_odle_lin_maltecca_walter_mcnulty_fellner_jacobi_2023, title={Dietary Prebiotic Oligosaccharides and Arachidonate Alter the Fecal Microbiota and Mucosal Lipid Composition of Suckling Pigs}, volume={153}, ISSN={["1541-6100"]}, url={https://doi.org/10.1016/j.tjnut.2023.06.019}, DOI={10.1016/j.tjnut.2023.06.019}, abstractNote={Early intestinal development is important to infant vitality, and optimal formula composition can promote gut health. The objectives were to evaluate the effects of arachidonate (ARA) and/or prebiotic oligosaccharide (PRE) supplementation in formula on the development of the microbial ecosystem and colonic health parameters. Newborn piglets were fed 4 formulas containing ARA [0.5 compared with 2.5% of dietary fatty acids (FAs)] and PRE (0 compared with 8 g/L, containing a 1:1 mixture of galactooligosaccharides and polydextrose) in a 2 x 2 factorial design for 22 d. Fecal samples were collected weekly and analyzed for relative microbial abundance. Intestinal samples were collected on day 22 and analyzed for mucosal FAs, pH, and short-chain FAs (SCFAs). PRE supplementation significantly increased genera within Bacteroidetes and Firmicutes, including Anaerostipes, Mitsuokella, Prevotella, Clostridium IV, and Bulleidia, and resulted in progressive separation from controls as determined by Principal Coordinates Analysis. Concentrations of SCFA increased from 70.98 to 87.37 mM, with an accompanying reduction in colonic pH. ARA supplementation increased the ARA content of the colonic mucosa from 2.35–5.34% of total FAs. PRE supplementation also altered mucosal FA composition, resulting in increased linoleic acid (11.52–16.33% of total FAs) and ARA (2.35–5.16% of total FAs). Prebiotic supplementation during the first 22 d of life altered the gut microbiota of piglets and increased the abundance of specific bacterial genera. These changes correlated with increased SCFA, which may benefit intestinal development. Although dietary ARA did not alter the microbiota, it increased the ARA content of the colonic mucosa, which may support intestinal development and epithelial repair. Prebiotic supplementation also increased unsaturation of FAs in the colonic mucosa. Although the mechanism requires further investigation, it may be related to altered microbial ecology or biohydrogenation of FA.}, number={8}, journal={JOURNAL OF NUTRITION}, author={Eudy, Brandon J. and Odle, Jack and Lin, Xi and Maltecca, Christian and Walter, Kathleen R. and McNulty, Nathan P. and Fellner, Vivek and Jacobi, Sheila K.}, year={2023}, month={Aug}, pages={2249–2262} }
 @article{kuthyar_diaz_avalos-villatoro_maltecca_tiezzi_dunn_reese_2023, title={Domestication shapes the pig gut microbiome and immune traits from the scale of lineage to population}, volume={36}, ISSN={1420-9101 1010-061X}, url={http://dx.doi.org/10.1111/jeb.14227}, DOI={10.1111/jeb.14227}, abstractNote={Animal ecology and evolution have long been known to shape host physiology, but more recently, the gut microbiome has been identified as a mediator between animal ecology and evolution and health. The gut microbiome has been shown to differ between wild and domestic animals, but the role of these differences for domestic animal evolution remains unknown. Gut microbiome responses to new animal genotypes and local environmental change during domestication may promote specific host phenotypes that are adaptive (or not) to the domestic environment. Because the gut microbiome supports host immune function, understanding the effects of animal ecology and evolution on the gut microbiome and immune phenotypes is critical. We investigated how domestication affects the gut microbiome and host immune state in multiple pig populations across five domestication contexts representing domestication status and current living conditions: free-ranging wild, captive wild, free-ranging domestic, captive domestic in research or industrial settings. We observed that domestication context explained much of the variation in gut microbiome composition, pathogen abundances and immune markers, yet the main differences in the repertoire of metabolic genes found in the gut microbiome were between the wild and domestic genetic lineages. We also documented population-level effects within domestication contexts, demonstrating that fine scale environmental variation also shaped host and microbe features. Our findings highlight that understanding which gut microbiome and immune traits respond to host genetic lineage and/or scales of local ecology could inform targeted interventions that manipulate the gut microbiome to achieve beneficial health outcomes.}, number={12}, journal={Journal of Evolutionary Biology}, publisher={Oxford University Press (OUP)}, author={Kuthyar, Sahana and Diaz, Jessica and Avalos-Villatoro, Fabiola and Maltecca, Christian and Tiezzi, Francesco and Dunn, Robert R. and Reese, Aspen T.}, year={2023}, month={Dec}, pages={1695–1711} }
 @article{johnson_wen_freitas_maskal_hartman_byrd_graham_ceja_tiezzi_maltecca_et al._2023, title={Evaluating phenotypes associated with heat tolerance and identifying moderate and severe heat stress thresholds in lactating sows housed in mechanically or naturally ventilated barns during the summer under commercial conditions}, volume={101}, ISSN={["1525-3163"]}, url={https://doi.org/10.1093/jas/skad129}, DOI={10.1093/jas/skad129}, abstractNote={An accurate understanding of heat stress (HS) temperatures and phenotypes that indicate HS tolerance is necessary to improve swine HS resilience. Therefore, the study objectives were 1) to identify phenotypes indicative of HS tolerance, and 2) to determine moderate and severe HS threshold temperatures in lactating sows. Multiparous (4.10 ± 1.48) lactating sows and their litters (11.10 ± 2.33 piglets/litter) were housed in naturally ventilated (n = 1,015) or mechanically ventilated (n = 630) barns at a commercial sow farm in Maple Hill, NC, USA between June 9 and July 24, 2021. In-barn dry bulb temperatures (TDB) and relative humidity were continuously recorded for naturally ventilated (26.38 ± 1.21 °C and 83.38 ± 5.40%, respectively) and mechanically ventilated (26.91 ± 1.80 °C and 77.13 ± 7.06%, respectively) barns using data recorders. Sows were phenotyped between lactation days 11.28 ± 3.08 and 14.25 ± 3.26. Thermoregulatory measures were obtained daily at 0800, 1200, 1600, and 2000 h and included respiration rate, and ear, shoulder, rump, and tail skin temperatures. Vaginal temperatures (TV) were recorded in 10 min intervals using data recorders. Anatomical characteristics were recorded, including ear area and length, visual and caliper-assessed body condition scores, and a visually assessed and subjective hair density score. Data were analyzed using PROC MIXED to evaluate the temporal pattern of thermoregulatory responses, phenotype correlations were based on mixed model analyses, and moderate and severe HS inflection points were established by fitting TV as the dependent variable in a cubic function against TDB. Statistical analyses were conducted separately for sows housed in mechanically or naturally ventilated barns because the sow groups were not housed in each facility type simultaneously. The temporal pattern of thermoregulatory responses was similar for naturally and mechanically ventilated barns and several thermoregulatory and anatomical measures were significantly correlated with one another (P < 0.05), including all anatomical measures as well as skin temperatures, respiration rates, and TV. For sows housed in naturally and mechanically ventilated facilities, moderate HS threshold TDB were 27.36 and 26.69 °C, respectively, and severe HS threshold TDB were 29.45 and 30.60 °C, respectively. In summary, this study provides new information on the variability of HS tolerance phenotypes and environmental conditions that constitute HS in commercially housed lactating sows.Climate change and the associated increase in global temperatures have a well-described negative impact on swine production. Therefore, improving swine heat stress resilience is of utmost importance to reduce the deleterious effects of heat stress on swine health, performance, and welfare. Genomic selection for heat stress resilience may be a viable strategy to improve swine productivity in a changing climate. However, identifying environmental conditions that constitute heat stress and deriving novel traits that can be easily collected on farm and provide accurate and precise predictions of heat stress tolerance is a necessary step. The present study demonstrated that housing conditions had a limited influence on heat stress tolerance phenotypes, several anatomical and thermoregulatory measures were correlated, and housing conditions impacted heat stress threshold temperatures. Results from this study may be applied to large-scale phenotyping initiatives to develop or refine genomic selection indexes for heat stress resilience in pigs.}, journal={JOURNAL OF ANIMAL SCIENCE}, author={Johnson, Jay S. and Wen, Hui and Freitas, Pedro H. F. and Maskal, Jacob M. and Hartman, Sharlene O. and Byrd, MaryKate and Graham, Jason R. and Ceja, Guadalupe and Tiezzi, Francesco and Maltecca, Christian and et al.}, year={2023}, month={Jan} }
 @article{freitas_johnson_wen_maskal_tiezzi_maltecca_huang_dedecker_schinckel_brito_2023, title={Genetic parameters for automatically-measured vaginal temperature, respiration efficiency, and other thermotolerance indicators measured on lactating sows under heat stress conditions}, volume={55}, ISSN={1297-9686}, url={http://dx.doi.org/10.1186/s12711-023-00842-x}, DOI={10.1186/s12711-023-00842-x}, abstractNote={Abstract Background Genetic selection based on direct indicators of heat stress could capture additional mechanisms that are involved in heat stress response and enable more accurate selection for more heat-tolerant individuals. Therefore, the main objectives of this study were to estimate genetic parameters for various heat stress indicators in a commercial population of Landrace × Large White lactating sows measured under heat stress conditions. The main indicators evaluated were: skin surface temperatures (SST), automatically-recorded vaginal temperature (T V ), respiration rate (RR), panting score (PS), body condition score (BCS), hair density (HD), body size (BS), ear size, and respiration efficiency (R eff ). Results Traits based on T V presented moderate heritability estimates, ranging from 0.15 ± 0.02 to 0.29 ± 0.05. Low heritability estimates were found for SST traits (from 0.04 ± 0.01 to 0.06 ± 0.01), RR (0.06 ± 0.01), PS (0.05 0.01), and R eff (0.03 ± 0.01). Moderate to high heritability values were estimated for BCS (0.29 ± 0.04 for caliper measurements and 0.25 ± 0.04 for visual assessments), HD (0.25 ± 0.05), BS (0.33 ± 0.05), ear area (EA; 0.40 ± 0.09), and ear length (EL; 0.32 ± 0.07). High genetic correlations were estimated among SST traits (> 0.78) and among T V traits (> 0.75). Similarly, high genetic correlations were also estimated for RR with PS (0.87 ± 0.02), with BCS measures (0.92 ± 0.04), and with ear measures (0.95 ± 0.03). Low to moderate positive genetic correlations were estimated between SST and T V (from 0.25 ± 0.04 to 0.76 ± 0.07). Low genetic correlations were estimated between T V and BCS (from − 0.01 ± 0.08 to 0.06 ± 0.07). Respiration efficiency was estimated to be positively and moderately correlated with RR (0.36 ± 0.04), PS (0.56 ± 0.03), and BCS (0.56 ± 0.05 for caliper measurements and 0.50 ± 0.05 for the visual assessments). All other trait combinations were lowly genetically correlated. Conclusions A comprehensive landscape of heritabilities and genetic correlations for various thermotolerance indicators in lactating sows were estimated. All traits evaluated are under genetic control and heritable, with different magnitudes, indicating that genetic progress is possible for all of them. The genetic correlation estimates provide evidence for the complex relationships between these traits and confirm the importance of a sub-index of thermotolerance traits to improve heat tolerance in pigs.}, number={1}, journal={Genetics Selection Evolution}, publisher={Springer Science and Business Media LLC}, author={Freitas, Pedro H. F. and Johnson, Jay S. and Wen, Hui and Maskal, Jacob M. and Tiezzi, Francesco and Maltecca, Christian and Huang, Yijian and DeDecker, Ashley E. and Schinckel, Allan P. and Brito, Luiz F.}, year={2023}, month={Sep} }
 @article{van kaam_ablondi_maltecca_cassandro_2023, title={Inbreeding becomes a serious issue}, number={59}, journal={Interbull Bulletin}, author={van Kaam, J.B. and Ablondi, M. and Maltecca, C. and Cassandro, M.}, year={2023}, pages={101–104} }
 @article{lozada-soto_gaddis_tiezzi_jiang_ma_toghiani_van raden_maltecca_2023, title={Inbreeding depression for producer-recorded udder, metabolic, and reproductive diseases in US dairy cattle}, volume={107}, ISSN={0022-0302}, url={http://dx.doi.org/10.3168/jds.2023-23909}, DOI={10.3168/jds.2023-23909}, abstractNote={This study leveraged a growing dataset of producer-recorded phenotypes for mastitis, reproductive diseases (metritis and retained placenta), and metabolic diseases (ketosis, milk fever, and displaced abomasum) to investigate the potential presence of inbreeding depression for these disease traits. Phenotypic, pedigree, and genomic information were obtained for 354,043 and 68,292 US Holstein and Jersey cows, respectively. Total inbreeding coefficients were calculated using both pedigree and genomic information; the latter included inbreeding estimates obtained using a genomic relationship matrix and runs of homozygosity. We also generated inbreeding coefficients based on the generational inbreeding for recent and old pedigree inbreeding, for different run-of-homozygosity length classes, and for recent and old homozygous-by-descent segment-based inbreeding. Estimates on the liability scale revealed significant evidence of inbreeding depression for reproductive-disease traits, with an increase in total pedigree and genomic inbreeding showing a notable effect for recent inbreeding. However, we found inconsistent evidence for inbreeding depression for mastitis or any metabolic diseases. Notably, in Holsteins, the probability of developing displaced abomasum decreased with inbreeding, particularly for older inbreeding. Estimates of disease probability for cows with low, average, and high inbreeding levels did not significantly differ across any inbreeding coefficient and trait combination, indicating that although inbreeding may affect disease incidence, it likely plays a smaller role compared with management and environmental factors.}, number={5}, journal={Journal of Dairy Science}, publisher={American Dairy Science Association}, author={Lozada-Soto, Emmanuel A. and Gaddis, Kristen L. Parker and Tiezzi, Francesco and Jiang, Jicai and Ma, Li and Toghiani, Sajjad and Van Raden, Paul M. and Maltecca, Christian}, year={2023}, month={Dec}, pages={3032–3046} }
 @article{gonzalez-recio_martinez-alvaro_tiezzi_saborio-montero_maltecca_roehe_2023, title={Invited review: Novel methods and perspectives for modulating the rumen microbiome through selective breeding as a means to improve complex traits: Implications for methane emissions in cattle}, volume={269}, ISSN={["1878-0490"]}, DOI={10.1016/j.livsci.2023.105171}, abstractNote={The rumen microbiome is responsible for methane emission in ruminants. The study of microbes in the rumen has attracted great interest in the last decade. High-throughput sequencing technologies have been key in expanding the knowledge of the microorganisms that populate the rumen through metagenomic studies. There is substantial evidence that the composition of the rumen microbiota is influenced by host genotype. Therefore, modulation of the microbiota poses an important tool for breeding for lower emissions in large and small ruminants. The main challenges of metagenomic studies are addressed and some solutions are proposed when available, including the incorporation of metagenomic information into statistical models regularly used in animal breeding. To incorporate microbiome information into breeding programs, the particularities of the rumen microbiome must be considered, from sampling to inclusion in selection indices. The latest advances in this area are discussed in this review.}, journal={LIVESTOCK SCIENCE}, author={Gonzalez-Recio, O. and Martinez-Alvaro, M. and Tiezzi, Francesco and Saborio-Montero, A. and Maltecca, C. and Roehe, R.}, year={2023}, month={Mar} }
 @article{wen_johnson_freitas_maskal_gloria_araujo_pedrosa_tiezzi_maltecca_huang_et al._2023, title={Longitudinal genomic analyses of automatically-recorded vaginal temperature in lactating sows under heat stress conditions based on random regression models}, volume={55}, ISSN={["1297-9686"]}, DOI={10.1186/s12711-023-00868-1}, abstractNote={Automatic and continuous recording of vaginal temperature (TV) using wearable sensors causes minimal disruptions to animal behavior and can generate data that enable the evaluation of temporal body temperature variation under heat stress (HS) conditions. However, the genetic basis of TV in lactating sows from a longitudinal perspective is still unknown. The objectives of this study were to define statistical models and estimate genetic parameters for TV in lactating sows using random regression models, and identify genomic regions and candidate genes associated with HS indicators derived from automatically-recorded TV.Heritability estimates for TV ranged from 0.14 to 0.20 over time (throughout the day and measurement period) and from 0.09 to 0.18 along environmental gradients (EG, - 3.5 to 2.2, which correspond to dew point values from 14.87 to 28.19 ˚C). Repeatability estimates of TV over time and along EG ranged from 0.57 to 0.66 and from 0.54 to 0.77, respectively. TV measured from 12h00 to 16h00 had moderately high estimates of heritability (0.20) and repeatability (0.64), indicating that this period might be the most suitable for recording TV for genetic selection purposes. Significant genotype-by-environment interactions (GxE) were observed and the moderately high estimates of genetic correlations between pairs of extreme EG indicate potential re-ranking of selection candidates across EG. Two important genomic regions on chromosomes 10 (59.370-59.998 Mb) and16 (21.548-21.966 Mb) were identified. These regions harbor the genes CDC123, CAMK1d, SEC61A2, and NUDT5 that are associated with immunity, protein transport, and energy metabolism. Across the four time-periods, respectively 12, 13, 16, and 10 associated genomic regions across 14 chromosomes were identified for TV. For the three EG classes, respectively 18, 15, and 14 associated genomic windows were identified for TV, respectively. Each time-period and EG class had uniquely enriched genes with identified specific biological functions, including regulation of the nervous system, metabolism and hormone production.TV is a heritable trait with substantial additive genetic variation and represents a promising indicator trait to select pigs for improved heat tolerance. Moderate GxE for TV exist, indicating potential re-ranking of selection candidates across EG. TV is a highly polygenic trait regulated by a complex interplay of physiological, cellular and behavioral mechanisms.}, number={1}, journal={GENETICS SELECTION EVOLUTION}, author={Wen, Hui and Johnson, Jay S. and Freitas, Pedro H. F. and Maskal, Jacob M. and Gloria, Leonardo S. and Araujo, Andre C. and Pedrosa, Victor B. and Tiezzi, Francesco and Maltecca, Christian and Huang, Yijian and et al.}, year={2023}, month={Dec} }
 @article{wang_man_wang_odle_maltecca_lin_2023, title={MicroRNA and mRNA sequencing analyses reveal key hepatic metabolic and signaling pathways responsive to maternal undernutrition in full-term fetal pigs}, volume={116}, ISSN={["1873-4847"]}, url={https://doi.org/10.1016/j.jnutbio.2023.109312}, DOI={10.1016/j.jnutbio.2023.109312}, abstractNote={Maternal undernutrition is highly prevalent in developing countries, leading to severe fetus/infant mortality, intrauterine growth restriction, stunting, and severe wasting. However, the potential impairments of maternal undernutrition to metabolic pathways in offspring are not defined completely. In this study, 2 groups of pregnant domestic pigs received nutritionally balanced gestation diets with or without 50% feed intake restriction from 0 to 35 gestation days and 70% from 35 to 114 gestation days. Full-term fetuses were collected via C-section on day 113/114 of gestation. MicroRNA and mRNA deep sequencing were analyzed using the Illumina GAIIx system on fetal liver samples. The mRNA-miRNA correlation and associated signaling pathways were analyzed via CLC Genomics Workbench and Ingenuity Pathway Analysis Software. A total of 1189 and 34 differentially expressed mRNA and miRNAs were identified between full-nutrition (F) and restricted-nutrition (R) groups. The correlation analyses showed that metabolic and signaling pathways such as oxidative phosphorylation, death receptor signaling, neuroinflammation signaling pathway, and estrogen receptor signaling pathways were significantly modified, and the gene modifications in these pathways were associated with the miRNA changes induced by the maternal undernutrition. For example, the upregulated (P<.05) oxidative phosphorylation pathway in R group was validated using RT-qPCR, and the correlational analysis indicated that miR-221, 103, 107, 184, and 4497 correlate with their target genes NDUFA1, NDUFA11, NDUFB10 and NDUFS7 in this pathway. These results provide the framework for further understanding maternal malnutrition's negative impacts on hepatic metabolic pathways via miRNA-mRNA interactions in full-term fetal pigs.}, journal={JOURNAL OF NUTRITIONAL BIOCHEMISTRY}, author={Wang, Feng and Man, Chaolai and Wang, Xiaoqiu and Odle, Jack and Maltecca, Christian and Lin, Xi}, year={2023}, month={Jun} }
 @article{cheng_maltecca_vanraden_jeffrey r. o'connell_ma_jiang_2023, title={SLEMM: million-scale genomic predictions with window-based SNP weighting}, volume={39}, ISSN={["1367-4811"]}, url={https://doi.org/10.1093/bioinformatics/btad127}, DOI={10.1093/bioinformatics/btad127}, abstractNote={The amount of genomic data is increasing exponentially. Using many genotyped and phenotyped individuals for genomic prediction is appealing yet challenging.We present SLEMM (short for Stochastic-Lanczos-Expedited Mixed Models), a new software tool, to address the computational challenge. SLEMM builds on an efficient implementation of the stochastic Lanczos algorithm for REML in a framework of mixed models. We further implement SNP weighting in SLEMM to improve its predictions. Extensive analyses on seven public datasets, covering 19 polygenic traits in three plant and three livestock species, showed that SLEMM with SNP weighting had overall the best predictive ability among a variety of genomic prediction methods including GCTA's empirical BLUP, BayesR, KAML, and LDAK's BOLT and BayesR models. We also compared the methods using nine dairy traits of ∼300k genotyped cows. All had overall similar prediction accuracies, except that KAML failed to process the data. Additional simulation analyses on up to 3 million individuals and 1 million SNPs showed that SLEMM was advantageous over counterparts as for computational performance. Overall, SLEMM can do million-scale genomic predictions with an accuracy comparable to BayesR.The software is available at https://github.com/jiang18/slemm.}, number={3}, journal={BIOINFORMATICS}, author={Cheng, Jian and Maltecca, Christian and VanRaden, Paul M. and Jeffrey R. O'Connell and Ma, Li and Jiang, Jicai}, editor={Schwartz, RussellEditor}, year={2023}, month={Mar} }
 @misc{lozada-soto_tiezzi_cole_vanraden_maltecca_2022, title={188. Patterns of inbreeding and selection using runs of homozygosity in North American dairy cattle}, url={http://dx.doi.org/10.3920/978-90-8686-940-4_188}, DOI={10.3920/978-90-8686-940-4_188}, abstractNote={The main objective of this study was to leverage genomic information to ascertain patterns of inbreeding and selection in five North American dairy cattle populations. We obtained genotypes for over 4 million individuals of the Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey breeds. Inbreeding based on runs of homozygosity was calculated in each population. The average inbreeding ranged from 0.11 for Ayrshire to 0.17 for Jersey. We calculated a coefficient of homozygosity for each marker. Highly homozygous markers were joined into larger genomic segments of interest that ranged from 0.08 to 7.83 Mb in length and spanned 14 chromosomes across breeds. Annotation of genes and QTLs in the highly homozygous regions revealed selection for economically important traits, notably for udder and cow health, productive life, and reproductive traits. We found differences across breeds on inbreeding load, genomic regions of high inbreeding, and selection signatures.}, journal={Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP)}, publisher={Wageningen Academic Publishers}, author={Lozada-Soto, E.A. and Tiezzi, F. and Cole, J.B. and VanRaden, P.M. and Maltecca, C.}, year={2022}, month={Dec} }
 @misc{cheng_cheng_fernando_maltecca_ma_dekkers_jiang_2022, title={354. A variational Bayes method for genomic prediction increases accuracy and computing speed}, url={http://dx.doi.org/10.3920/978-90-8686-940-4_354}, DOI={10.3920/978-90-8686-940-4_354}, abstractNote={Various Bayesian methods have been developed to improve genetic prediction of complex traits. Although Bayesian methods such as Bayes-B have been proven to outperform genomic best linear unbiased prediction for prediction of complex traits, especially those that have major genes, they are usually implemented using Markov chain Monte Carlo, which is time consuming and has convergence issues when the number of markers is greater than the number of individuals. To address this issue, we developed a computationally efficient variational Bayes (Bayes-VB) method that can flexibly partition the whole genome markers into many groups. Our approach can improve the accuracy of genomic predictions compared to Bayes-B and is much faster, as illustrated here for growth rate of pigs under a disease challenge.}, journal={Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP)}, publisher={Wageningen Academic Publishers}, author={Cheng, J. and Cheng, H. and Fernando, R. and Maltecca, C. and Ma, L. and Dekkers, J. and Jiang, J.}, year={2022}, month={Dec} }
 @article{johnson_brito_maltecca_tiezzi_2022, title={400 Improving Heat Stress Resilience to Reduce the Negative Effects of pre- and Postnatal Heat Stress in Swine}, volume={100}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skac247.093}, DOI={10.1093/jas/skac247.093}, abstractNote={Abstract Heat stress will become a more substantial issue for swine production as global temperatures continue to rise. Efforts have been made to mitigate the negative impacts of heat stress through advances in genetic selection, nutrition, precision technology, and management practices. However, heat stress is still a limiting factor to swine production that must be addressed. A key aspect to mitigating heat stress-related production losses in swine may be developing better management approaches and genetic selection techniques to improve heat stress resilience. This is especially true for heat stress sensitive populations such as gestating and lactating sows. Gestating and lactating sows are considered heat stress sensitive due to greater metabolic heat production resulting from fetal growth and milk production, respectively. Additionally, swine offspring are adversely affected by in utero heat stress, which imprints long-term negative phenotypes that reduce health and productivity regardless of environmental conditions. To address these concerns, swine producers have implemented a variety of cooling and management strategies to mitigate heat stress. However, the recommended or perceived heat stress temperature thresholds for implementation may not accurately reflect the thermal requirements of modern swine with current genetics or at variable gestation stages leading to ineffective use. As such, a re-evaluation of heat stress thresholds for modern swine is warranted. Additionally, genetic selection for productivity traits (e.g., litter sizes and lean growth) has increased metabolic heat production resulting in increased heat stress sensitivity. Therefore, the ability to perform large-scale measures of and implement heat stress tolerance traits within breeding populations may be advantageous for producers to improve heat stress resilience within their herds. Taken together, as heat stress becomes a greater issue for the swine industry, it will become increasingly important to develop sustainable approaches that improve swine heat stress resilience while maintaining or improving producer profitability.}, number={Supplement_3}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Johnson, Jay S and Brito, Luiz Fernando F and Maltecca, Christian and Tiezzi, Francesco}, year={2022}, month={Sep}, pages={47–48} }
 @misc{maltecca_jiang_fix_schwab_shull_tiezzi_2022, title={406. Compressing microbiota information using an autoencoder to predict growth traits in swine}, url={http://dx.doi.org/10.3920/978-90-8686-940-4_406}, DOI={10.3920/978-90-8686-940-4_406}, abstractNote={Microbial composition represents a promising tool in precision farming. In the current paper, we evaluated the power of fecal microbial composition to predict growth performance across swine farming systems. We used different dimensionality reductions to select microbial features to be included in the predictive models, ranging from random selection to selection based on association to data compression using a sparse autoencoder. We compared these methods with a model including all information available. We found that microbial information can predict performances for growth and fat deposition. We found that in most cases, the use of all microbial information resulted in the highest predicting performance regardless of the trait or the populations used for training and prediction. Our results suggest that including all available microbial information might be the best option when using it to predict performance.}, journal={Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP)}, publisher={Wageningen Academic Publishers}, author={Maltecca, C. and Jiang, J. and Fix, J. and Schwab, C. and Shull, C. and Tiezzi, F.}, year={2022}, month={Dec} }
 @misc{déru_tiezzi_carillier-jacquin_blanchet_cauquil_zemb_bouquet_maltecca_gilbert_2022, title={506. Can microbial data improve prediction of breeding values of efficiency traits in pigs fed conventional or fiber diets?}, url={http://dx.doi.org/10.3920/978-90-8686-940-4_506}, DOI={10.3920/978-90-8686-940-4_506}, abstractNote={Recently, digestive efficiency (DE) was proposed as a trait of interest to improve feed efficiency (FE) in pigs, especially when they are fed with alternative feeding resources. Both are influenced by the host genetics, and also by the gut microbiota composition. The goal of this study was to quantify the impact of faecal microbial information on the prediction accuracies of genomic estimated breeding values (GEBVs) of FE and DE traits for pigs fed conventional or fiber diets. For DE traits, gains in prediction accuracy of GEBVs were increased by about 18% when microbial information was included in linear mixed models. In addition, these gains of prediction accuracy were very similar in both diets. For FE traits, no improvement was observed. Thus, the addition of microbial information in breeding programs is promising to better estimate GEBVs for DE traits.}, journal={Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP)}, publisher={Wageningen Academic Publishers}, author={Déru, V. and Tiezzi, F. and Carillier-Jacquin, C. and Blanchet, B. and Cauquil, L. and Zemb, O. and Bouquet, A. and Maltecca, C. and Gilbert, H.}, year={2022}, month={Dec} }
 @article{dewitt_guedira_murphy_marshall_mergoum_maltecca_brown-guedira_2022, title={A network modeling approach provides insights into the environment-specific yield architecture of wheat}, volume={5}, ISSN={["1943-2631"]}, url={https://doi.org/10.1093/genetics/iyac076}, DOI={10.1093/genetics/iyac076}, abstractNote={Abstract Wheat (Triticum aestivum) yield is impacted by a diversity of developmental processes which interact with the environment during plant growth. This complex genetic architecture complicates identifying quantitative trait loci that can be used to improve yield. Trait data collected on individual processes or components of yield have simpler genetic bases and can be used to model how quantitative trait loci generate yield variation. The objectives of this experiment were to identify quantitative trait loci affecting spike yield, evaluate how their effects on spike yield proceed from effects on component phenotypes, and to understand how the genetic basis of spike yield variation changes between environments. A 358 F5:6 recombinant inbred line population developed from the cross of LA-95135 and SS-MPV-57 was evaluated in 2 replications at 5 locations over the 2018 and 2019 seasons. The parents were 2 soft red winter wheat cultivars differing in flowering, plant height, and yield component characters. Data on yield components and plant growth were used to assemble a structural equation model to characterize the relationships between quantitative trait loci, yield components, and overall spike yield. The effects of major quantitative trait loci on spike yield varied by environment, and their effects on total spike yield were proportionally smaller than their effects on component traits. This typically resulted from contrasting effects on component traits, where an increase in traits associated with kernel number was generally associated with a decrease in traits related to kernel size. In all, the complete set of identified quantitative trait loci was sufficient to explain most of the spike yield variation observed within each environment. Still, the relative importance of individual quantitative trait loci varied dramatically. Path analysis based on coefficients estimated through structural equation model demonstrated that these variations in effects resulted from both different effects of quantitative trait loci on phenotypes and environment-by-environment differences in the effects of phenotypes on one another, providing a conceptual model for yield genotype-by-environment interactions in wheat.}, number={3}, journal={GENETICS}, author={DeWitt, Noah and Guedira, Mohammed and Murphy, Joseph Paul and Marshall, David and Mergoum, Mohamed and Maltecca, Christian and Brown-Guedira, Gina}, editor={Juenger, TEditor}, year={2022}, month={May} }
 @inproceedings{he_maltecca_howard_huang_gray_tiezzi_2022, title={Comparing methods to summarize gut microbiota composition in estimating microbiability of host phenotypes in swine}, DOI={10.3920/978-90-8686-940-4_501}, abstractNote={This study aimed to investigate eight approaches (four kernel functions, two distance methods, and two ordination methods) for creating covariance matrices to summarize microbiome information among animals and assess their performance in estimating trait microbiability in three commercial swine breeds. We collected rectal swabs and measured several growth and carcass composition traits on 651 pigs (Duroc: n=205; Landrace: n=226; Large White: n=220) at market weight. Based on the matrix used, microbiability estimates ranged from 0.07 to 0.21 and 0.12 to 0.53 for Duroc, 0.03 to 0.21 and 0.05 to 0.44 for Landrace, and 0.02 to 0.24 and 0.05 to 0.52 for Large White pigs averaged over traits in the model with sire, pen, and microbiome, and model with only microbiome, respectively. We observed differences in the contribution to trait microbiability estimation across the eight matrices.}, booktitle={Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP):  Technical and Species Orientated Innovations in Animal Breeding, and Contribution of Genetics to Solving Societal Challenges}, publisher={Wageningen Academic Publishers}, author={He, Y. and Maltecca, C. and Howard, J. and Huang, Y. and Gray, K. and Tiezzi, F.}, editor={Veerkamp, R.F. and de Haas, Y.Editors}, year={2022}, pages={2081–2084} }
 @article{lozada-soto_maltecca_cole_van raden_tiezzi_2022, title={Current state of inbreeding, genetic diversity, and selection history in all major breeds of US dairy cattle}, volume={105}, number={Supplement 1}, journal={Journal of Dairy Science}, author={Lozada-Soto, E.A. and Maltecca, C. and Cole, J.B. and Van Raden, P.M. and Tiezzi, F.}, year={2022}, pages={188–188} }
 @inproceedings{freebern_shen_jiang_maltecca_cole_liu_ma_2022, title={Effect of Temperature and Maternal Age on Recombination Rate in Cattle}, booktitle={Proceedings of the Plant and Animal Genome XXIX Conference}, publisher={PAG}, author={Freebern, E. and Shen, B. and Jiang, J. and Maltecca, C. and Cole, J. and Liu, G. and Ma, L.}, year={2022} }
 @article{he_tiezzi_jiang_howard_huang_gray_choi_maltecca_2022, title={Exploring methods to summarize gut microbiota composition for microbiability estimation and phenotypic prediction in swine}, volume={100}, ISSN={["1525-3163"]}, url={https://doi.org/10.1093/jas/skac231}, DOI={10.1093/jas/skac231}, abstractNote={Abstract The microbial composition resemblance among individuals in a group can be summarized in a square covariance matrix and fitted in linear models. We investigated eight approaches to create the matrix that quantified the resemblance between animals based on the gut microbiota composition. We aimed to compare the performance of different methods in estimating trait microbiability and predicting growth and body composition traits in three pig breeds. This study included 651 purebred boars from either breed: Duroc (n = 205), Landrace (n = 226), and Large White (n = 220). Growth and body composition traits, including body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content, were measured on live animals at the market weight (156 ± 2.5 d of age). Rectal swabs were taken from each animal at 158 ± 4 d of age and subjected to 16S rRNA gene sequencing. Eight methods were used to create the microbial similarity matrices, including 4 kernel functions (Linear Kernel, LK; Polynomial Kernel, PK; Gaussian Kernel, GK; Arc-cosine Kernel with one hidden layer, AK1), 2 dissimilarity methods (Bray-Curtis, BC; Jaccard, JA), and 2 ordination methods (Metric Multidimensional Scaling, MDS; Detrended Correspondence analysis, DCA). Based on the matrix used, microbiability estimates ranged from 0.07 to 0.21 and 0.12 to 0.53 for Duroc, 0.03 to 0.21 and 0.05 to 0.44 for Landrace, and 0.02 to 0.24 and 0.05 to 0.52 for Large White pigs averaged over traits in the model with sire, pen, and microbiome, and model with the only microbiome, respectively. The GK, JA, BC, and AK1 obtained greater microbiability estimates than the remaining methods across traits and breeds. Predictions were made within each breed group using four-fold cross-validation based on the relatedness of sires in each breed group. The prediction accuracy ranged from 0.03 to 0.18 for BW, 0.08 to 0.31 for BF, 0.21 to 0.48 for LD, and 0.04 to 0.16 for IMF when averaged across breeds. The BC, MDS, LK, and JA achieved better accuracy than other methods in most predictions. Overall, the PK and DCA exhibited the worst performance compared to other microbiability estimation and prediction methods. The current study shows how alternative approaches summarized the resemblance of gut microbiota composition among animals and contributed this information to variance component estimation and phenotypic prediction in swine.}, number={9}, journal={JOURNAL OF ANIMAL SCIENCE}, author={He, Yuqing and Tiezzi, Francesco and Jiang, Jicai and Howard, Jeremy and Huang, Yijian and Gray, Kent and Choi, Jung-Woo and Maltecca, Christian}, year={2022}, month={Sep} }
 @article{he_tiezzi_howard_huang_gray_maltecca_2022, title={Exploring the role of gut microbiota in host feeding behavior among breeds in swine}, volume={22}, ISSN={["1471-2180"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85122218151&partnerID=MN8TOARS}, DOI={10.1186/s12866-021-02409-6}, abstractNote={Abstract Background The interplay between the gut microbiota and feeding behavior has consequences for host metabolism and health. The present study aimed to explore gut microbiota overall influence on feeding behavior traits and to identify specific microbes associated with the traits in three commercial swine breeds at three growth stages. Feeding behavior measures were obtained from 651 pigs of three breeds (Duroc, Landrace, and Large White) from an average 73 to 163 days of age. Seven feeding behavior traits covered the information of feed intake, feeder occupation time, feeding rate, and the number of visits to the feeder. Rectal swabs were collected from each pig at 73 ± 3, 123 ± 4, and 158 ± 4 days of age. DNA was extracted and subjected to 16 S rRNA gene sequencing. Results Differences in feeding behavior traits among breeds during each period were found. The proportion of phenotypic variances of feeding behavior explained by the gut microbial composition was small to moderate (ranged from 0.09 to 0.31). A total of 21, 10, and 35 amplicon sequence variants were found to be significantly (q-value < 0.05) associated with feeding behavior traits for Duroc, Landrace, and Large White across the three sampling time points. The identified amplicon sequence variants were annotated to five phyla, with Firmicutes being the most abundant. Those amplicon sequence variants were assigned to 28 genera, mainly including Christensenellaceae _R-7_group, Ruminococcaceae _UCG-004, Dorea, Ruminococcaceae _UCG-014, and Marvinbryantia . Conclusions This study demonstrated the importance of the gut microbial composition in interacting with the host feeding behavior and identified multiple archaea and bacteria associated with feeding behavior measures in pigs from either Duroc, Landrace, or Large White breeds at three growth stages. Our study provides insight into the interaction between gut microbiota and feeding behavior and highlights the genetic background and age effects in swine microbial studies.}, number={1}, journal={BMC MICROBIOLOGY}, author={He, Yuqing and Tiezzi, Francesco and Howard, Jeremy and Huang, Yijian and Gray, Kent and Maltecca, Christian}, year={2022}, month={Jan} }
 @article{lozada-soto_tiezzi_jiang_cole_vanraden_maltecca_2022, title={Genomic characterization of autozygosity and recent inbreeding trends in all major breeds of US dairy cattle}, volume={105}, ISSN={["1525-3198"]}, url={https://doi.org/10.3168/jds.2022-22116}, DOI={10.3168/jds.2022-22116}, abstractNote={Maintaining a genetically diverse dairy cattle population is critical to preserving adaptability to future breeding goals and avoiding declines in fitness. This study characterized the genomic landscape of autozygosity and assessed trends in genetic diversity in 5 breeds of US dairy cattle. We analyzed a sizable genomic data set containing 4,173,679 pedigreed and genotyped animals of the Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey breeds. Runs of homozygosity (ROH) of 2 Mb or longer in length were identified in each animal. The within-breed means for number and the combined length of ROH were highest in Jerseys (62.66 ± 8.29 ROH and 426.24 ± 83.40 Mb, respectively; mean ± SD) and lowest in Ayrshires (37.24 ± 8.27 ROH and 265.05 ± 85.00 Mb, respectively). Short ROH were the most abundant, but moderate to large ROH made up the largest proportion of genome autozygosity in all breeds. In addition, we identified ROH islands in each breed. This revealed selection patterns for milk production, productive life, health, and reproduction in most breeds and evidence for parallel selective pressure for loci on chromosome 6 between Ayrshire and Brown Swiss and for loci on chromosome 20 between Holstein and Jersey. We calculated inbreeding coefficients using 3 different approaches, pedigree-based (FPED), marker-based using a genomic relationship matrix (FGRM), and segment-based using ROH (FROH). The average inbreeding coefficient ranged from 0.06 in Ayrshires and Brown Swiss to 0.08 in Jerseys and Holsteins using FPED, from 0.22 in Holsteins to 0.29 in Guernsey and Jerseys using FGRM, and from 0.11 in Ayrshires to 0.17 in Jerseys using FROH. In addition, the effective population size at past generations (5-100 generations ago), the yearly rate of inbreeding, and the effective population size in 3 recent periods (2000-2009, 2010-2014, and 2015-2018) were determined in each breed to ascertain current and historical trends of genetic diversity. We found a historical trend of decreasing effective population size in the last 100 generations in all breeds and breed differences in the effect of the recent implementation of genomic selection on inbreeding accumulation.}, number={11}, journal={JOURNAL OF DAIRY SCIENCE}, author={Lozada-Soto, Emmanuel A. and Tiezzi, Francesco and Jiang, Jicai and Cole, John B. and VanRaden, Paul M. and Maltecca, Christian}, year={2022}, month={Nov}, pages={8956–8971} }
 @misc{tiezzi_maltecca_2022, title={Genotype by Environment Interactions in Livestock Farming}, ISBN={9781071624593 9781071624609}, ISSN={2629-2378 2629-2386}, url={http://dx.doi.org/10.1007/978-1-0716-2460-9_1115}, DOI={10.1007/978-1-0716-2460-9_1115}, journal={Encyclopedia of Sustainability Science and Technology Series}, publisher={Springer US}, author={Tiezzi, Francesco and Maltecca, Christian}, year={2022}, month={Nov}, pages={77–97} }
 @article{lozada-soto_lourenco_maltecca_fix_schwab_shull_tiezzi_2022, title={Genotyping and phenotyping strategies for genetic improvement of meat quality and carcass composition in swine}, volume={54}, ISSN={["1297-9686"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85131500498&partnerID=MN8TOARS}, DOI={10.1186/s12711-022-00736-4}, abstractNote={Meat quality and composition traits have become valuable in modern pork production; however, genetic improvement has been slow due to high phenotyping costs. Combining genomic information with multi-trait indirect selection based on cheaper indicator traits is an alternative for continued cost-effective genetic improvement.Data from an ongoing breeding program were used in this study. Phenotypic and genomic information was collected on three-way crossbred and purebred Duroc animals belonging to 28 half-sib families. We applied different methods to assess the value of using purebred and crossbred information (both genomic and phenotypic) to predict expensive-to-record traits measured on crossbred individuals. Estimation of multi-trait variance components set the basis for comparing the different scenarios, together with a fourfold cross-validation approach to validate the phenotyping schemes under four genotyping strategies.The benefit of including genomic information for multi-trait prediction depended on the breeding goal trait, the indicator traits included, and the source of genomic information. While some traits benefitted significantly from genotyping crossbreds (e.g., loin intramuscular fat content, backfat depth, and belly weight), multi-trait prediction was advantageous for some traits even in the absence of genomic information (e.g., loin muscle weight, subjective color, and subjective firmness).Our results show the value of using different sources of phenotypic and genomic information. For most of the traits studied, including crossbred genomic information was more beneficial than performing multi-trait prediction. Thus, we recommend including crossbred individuals in the reference population when these are phenotyped for the breeding objective.}, number={1}, journal={GENETICS SELECTION EVOLUTION}, author={Lozada-Soto, Emmanuel Andre and Lourenco, Daniela and Maltecca, Christian and Fix, Justin and Schwab, Clint and Shull, Caleb and Tiezzi, Francesco}, year={2022}, month={Jun} }
 @inproceedings{he_tiezzi_howard_huang_gray_maltecca_2022, title={Gut Microbiota Associated with Host Feeding Behavior and Microbial Prediction of Growth and Carcass Traits in Swine}, booktitle={Proceedings of the Plant and Animal Genome XXIX Conference}, author={He, Yuqing and Tiezzi, F. and Howard, J.T. and Huang, Y. and Gray, K. and Maltecca, C.}, year={2022} }
 @article{deru_tiezzi_carillier-jacquin_blanchet_cauquil_zemb_bouquet_maltecca_gilbert_2022, title={Gut microbiota and host genetics contribute to the phenotypic variation of digestive and feed efficiency traits in growing pigs fed a conventional and a high fiber diet}, volume={54}, ISSN={["1297-9686"]}, url={https://doi.org/10.1186/s12711-022-00742-6}, DOI={10.1186/s12711-022-00742-6}, abstractNote={Abstract Background Breeding pigs that can efficiently digest alternative diets with increased fiber content is a viable strategy to mitigate the feed cost in pig production. This study aimed at determining the contribution of the gut microbiota and host genetics to the phenotypic variability of digestive efficiency (DE) traits, such as digestibility coefficients of energy, organic matter and nitrogen, feed efficiency (FE) traits (feed conversion ratio and residual feed intake) and growth traits (average daily gain and daily feed intake). Data were available for 791 pigs fed a conventional diet and 735 of their full-sibs fed a high-fiber diet. Fecal samples were collected at 16 weeks of age to sequence the V3–V4 regions of the 16S ribosomal RNA gene and predict DE with near-infrared spectrometry. The proportions of phenotypic variance explained by the microbiota (microbiability) were estimated under three OTU filtering scenarios. Then, microbiability and heritability were estimated independently (models Micro and Gen) and jointly (model Micro+Gen) using a Bayesian approach for all traits. Breeding values were estimated in models Gen and Micro+Gen. Results Differences in microbiability estimates were significant between the two extreme filtering scenarios (14,366 and 803 OTU) within diets, but only for all DE. With the intermediate filtering scenario (2399 OTU) and for DE, microbiability was higher (> 0.44) than heritability (< 0.32) under both diets. For two of the DE traits, microbiability was significantly higher under the high-fiber diet (0.67 ± 0.06 and 0.68 ± 0.06) than under the conventional diet (0.44 ± 0.06). For growth and FE, heritability was higher (from 0.26 ± 0.06 to 0.44 ± 0.07) than microbiability (from 0.17 ± 0.05 to 0.35 ± 0.06). Microbiability and heritability estimates obtained with the Micro+Gen model did not significantly differ from those with the Micro and Gen models for all traits. Finally, based on their estimated breeding values, pigs ranked differently between the Gen and Micro+Gen models, only for the DE traits under both diets. Conclusions The microbiota explained a significant proportion of the phenotypic variance of the DE traits, which was even larger than that explained by the host genetics. Thus, the use of microbiota information could improve the selection of DE traits, and to a lesser extent, of growth and FE traits. In addition, our results show that, at least for DE traits, filtering OTU is an important step and influences the microbiability.}, number={1}, journal={GENETICS SELECTION EVOLUTION}, publisher={Springer Science and Business Media LLC}, author={Deru, Vanille and Tiezzi, Francesco and Carillier-Jacquin, Celine and Blanchet, Benoit and Cauquil, Laurent and Zemb, Olivier and Bouquet, Alban and Maltecca, Christian and Gilbert, Helene}, year={2022}, month={Jul} }
 @article{jiang_cheng_maltecca_ma_van raden_o'connell_2022, title={Mixed-model GWAS on milk production traits of 1.16 M genotyped Holstein cattle}, volume={105}, number={Supplement 1}, journal={Journal of Dairy Science}, author={Jiang, J. and Cheng, J. and Maltecca, C. and Ma, L. and Van Raden, P.M. and O'Connell, J.R.}, year={2022}, pages={19–19} }
 @article{cheng_maltecca_van raden_o'connell_ma_jiang_2022, title={SLEMM: Million-scale genomic best linear unbiased predictions with window-based SNP weighting}, volume={105}, number={Supplement 1}, journal={Journal of Dairy Science}, publisher={ELSEVIER SCIENCE INC STE}, author={Cheng, J. and Maltecca, C. and Van Raden, P.M. and O'Connell, J.R. and Ma, L. and Jiang, J.}, year={2022}, pages={19–19} }
 @article{cheng_tiezzi_howard_maltecca_jiang_2022, title={The Addition of Epistatic Genetic Effects Increases Genomic Prediction Accuracy for Reproduction and Production Traits in Duroc Pigs Using Genomic Models}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85133195660&partnerID=MN8TOARS}, DOI={10.21203/rs.3.rs-1182452}, journal={ResearchSquare}, author={Cheng, J. and Tiezzi, F. and Howard, J. and Maltecca, C. and Jiang, J.}, year={2022} }
 @article{he_tiezzi_jiang_howard_huang_gray_choi_maltecca_2022, title={Use of Host Feeding Behavior and Gut Microbiome Data in Estimating Variance Components and Predicting Growth and Body Composition Traits in Swine}, volume={13}, ISSN={["2073-4425"]}, url={https://doi.org/10.3390/genes13050767}, DOI={10.3390/genes13050767}, abstractNote={The purpose of this study was to investigate the use of feeding behavior in conjunction with gut microbiome sampled at two growth stages in predicting growth and body composition traits of finishing pigs. Six hundred and fifty-one purebred boars of three breeds: Duroc (DR), Landrace (LR), and Large White (LW), were studied. Feeding activities were recorded individually from 99 to 163 days of age. The 16S rRNA gene sequences were obtained from each pig at 123 ± 4 and 158 ± 4 days of age. When pigs reached market weight, body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content were measured on live animals. Three models including feeding behavior (Model_FB), gut microbiota (Model_M), or both (Model_FB_M) as predictors, were investigated. Prediction accuracies were evaluated through cross-validation across genetic backgrounds using the leave-one-breed-out strategy and across rearing environments using the leave-one-room-out approach. The proportions of phenotypic variance of growth and body composition traits explained by feeding behavior ranged from 0.02 to 0.30, and from 0.20 to 0.52 when using gut microbiota composition. Overall prediction accuracy (averaged over traits and time points) of phenotypes was 0.24 and 0.33 for Model_FB, 0.27 and 0.19 for Model_M, and 0.40 and 0.35 for Model_FB_M for the across-breed and across-room scenarios, respectively. This study shows how feeding behavior and gut microbiota composition provide non-redundant information in predicting growth in swine.}, number={5}, journal={GENES}, publisher={MDPI AG}, author={He, Yuqing and Tiezzi, Francesco and Jiang, Jicai and Howard, Jeremy T. and Huang, Yijian and Gray, Kent and Choi, Jung-Woo and Maltecca, Christian}, year={2022}, month={May} }
 @article{tiezzi_maltecca_2021, title={25 Gut Microbiome Information Enables Additional Discovery in Genome-wide Association Studies in Swine}, volume={99}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skab235.018}, DOI={10.1093/jas/skab235.018}, abstractNote={Abstract Several studies have highlighted the relevance of gut microbiome composition in shaping fat deposition in mammals. In contrast, other studies have highlighted how the host genome can control the abundance of individual species in the gut microbiota’s make-up. There is the need to incorporate the different ‘-omics’ data (host genome, gut microbiome, high-throughput phenotyping) in a model that allows to extract information beyond the simple sum of each component’s contribution. We propose a systematic approach to detect host genomic variants that control the gut microbiome, which in turn contributes to the host fat deposition, when this latter is based on multiple phenotypic measures. Using a dataset that included longitudinal records of fat deposition on 1,180 pigs, we implemented a mediation test to describe how fat deposition in swine (Sus scrofa) is affected by the host genotype and the gut microbiome. The phenotypic outcome was described both by measured and latent variables, taking advantage of structural equation modeling. We also implemented a ‘traditional’ genome-wide association analysis, testing the (total) effect of host genomic variants on the phenotype. Results for all models were validated using both bootstrapping and permutation tests. The models identified several host genomic features having microbiome-mediated effects on fat deposition. Our work demonstrates how the host genome can affect the phenotypic trait by inducing a change in gut microbiome composition that leads to a change in the phenotype. The host genomic features identified through the mediation analysis do not entirely overlap the group of features identified by traditional GWAS. Microbiome-mediated analyses can help understand the genetic determination of complex phenotypes. The host genomic features that exert a mediated effect could not be identified by traditional genome-wide association analysis. These can contribute to filling the missing heritability gap and provide further insights into the host genome – gut microbiome interplay.}, number={Supplement_3}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Tiezzi, Francesco and Maltecca, Christian}, year={2021}, month={Oct}, pages={10–10} }
 @article{lozada-soto_tiezzi_lu_miller_cole_maltecca_2021, title={29 Effects of Recent and Ancient Inbreeding on Growth in American Angus Cattle}, volume={99}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skab235.023}, DOI={10.1093/jas/skab235.023}, abstractNote={Abstract The accumulation of inbreeding can lead to an unfavorable change in the phenotypic value of individuals for traits related to fitness, also known as inbreeding depression. However, inbreeding accumulated at a more distant past (ancient inbreeding) is expected to have a smaller depressive effect than that accumulated more recently due to the loss of detrimental alleles caused by purifying selection. Therefore, the aim of this study was to quantify the inbreeding depression caused by recent and ancient inbreeding for birth weight, weaning weight, and post-weaning gain. Pedigree and genomic information were obtained from Angus Genetics, Inc. (St. Joseph, MO) for 569,364 individuals from the American Angus breed. Pedigree inbreeding and genomic inbreeding based on runs of homozygosity (ROH) were estimated using the SNP1101 software. Model-based genomic inbreeding based on the probability a marker is part of a homozygous-by-descent segment (HBD) was estimated using the RZooROH in R. The generational cutoffs for designating inbreeding as recent was that acquired 5 generations ago or sooner for pedigree, 6.25 generations ago or sooner for ROH, and 8 generations ago or sooner for HBD inbreeding. The effect of a 1% increase in inbreeding was modeled in males and females using a linear mixed model approach. Recent pedigree inbreeding was found to decrease birth weight by 0.04 and 0.03 kg, decrease weaning weight by 0.50 and 0.48 kg, and decrease post-weaning gain by 0.62 and 0.32 kg, in males and females respectively. Ancient pedigree inbreeding was generally found to have no effect on growth. For genomic inbreeding, when both recent and ancient inbreeding had a detrimental effect on growth, recent inbreeding generally had a larger effect. The results of this study demonstrate that inbreeding accumulated recently should be quantified and managed in beef cattle populations to avoid economic losses.}, number={Supplement_3}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Lozada-Soto, Emmanuel A and Tiezzi, Francesco and Lu, Duc and Miller, Stephen P and Cole, John B and Maltecca, Christian}, year={2021}, month={Oct}, pages={14–14} }
 @article{maltecca_tiezzi_2021, title={53 Awardee Talk: Implications of the Gut Microbiome for Genetic Improvement of Swine}, volume={99}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skab235.049}, DOI={10.1093/jas/skab235.049}, abstractNote={Abstract With the universal adoption of genomic selection as a breeding standard, livestock farming is about to enter a new chapter in which deep phenotyping and multi-omics technologies will enhance the use of molecular data in the selection process. Microbiome composition represents a promising tool in this arena, serving simultaneously as a benchmark of environmental challenge, a predictor of animal physiological status, and a direct target for host selection. Our group has been researching the application of the gut microbiome in swine production, with a particular focus on growth and feed efficiency. We investigated how the microbiome is shaped in different production systems by comparing the microbiome composition of growing/finishing pigs raised in bio-secure nucleus farms and commercial facilities. Results suggested a strong impact of gut microbiome composition on pork production efficiency and a consistent effect of several microbial features across different systems. We found that microbial features associated with animal growth are heritable and identified host genomic markers contributing to their relative abundance. We have found that the microbiota composition is different across breeds but stable within breeds (Duroc, Large White, Landrace). Taxa differently represented among the breeds are also associated with feed efficiency and behavior. We estimated the ‘microbiability’ of meat and carcass quality traits. We found that the gut microbiome composition leaves an identifiable ‘footprint’ on the animal tissue deposition. Following these findings, we elucidated the complex relationship between genomic and microbial variation using structural equation modeling to test a causal path between the host genotype, the gut microbiome composition, and fat deposition. We identified direct and mediated (through microbiome) genomic variants affecting host trait expression, highlighting the potential for the ‘second genome’ to contribute to the recovery of genetic variance.}, number={Supplement_3}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Maltecca, Christian and Tiezzi, Francesco}, year={2021}, month={Oct}, pages={29–29} }
 @article{shen_freebern_jiang_maltecca_cole_liu_ma_2021, title={Effect of Temperature and Maternal Age on Recombination Rate in Cattle}, volume={12}, ISSN={["1664-8021"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85111940376&partnerID=MN8TOARS}, DOI={10.3389/fgene.2021.682718}, abstractNote={Meiotic recombination is a fundamental biological process that facilitates meiotic division and promotes genetic diversity. Recombination is phenotypically plastic and affected by both intrinsic and extrinsic factors. The effect of maternal age on recombination rates has been characterized in a wide range of species, but the effect’s direction remains inconclusive. Additionally, the characterization of temperature effects on recombination has been limited to model organisms. Here we seek to comprehensively determine the impact of genetic and environmental factors on recombination rate in dairy cattle. Using a large cattle pedigree, we identified maternal recombination events within 305,545 three-generation families. By comparing recombination rate between parents of different ages, we found a quadratic trend between maternal age and recombination rate in cattle. In contrast to either an increasing or decreasing trend in humans, cattle recombination rate decreased with maternal age until 65 months and then increased afterward. Combining recombination data with temperature information from public databases, we found a positive correlation between environmental temperature during fetal development of offspring and recombination rate in female parents. Finally, we fitted a full recombination rate model on all related factors, including genetics, maternal age, and environmental temperatures. Based on the final model, we confirmed the effect of maternal age and environmental temperature during fetal development of offspring on recombination rate with an estimated heritability of 10% ( SE = 0.03) in cattle. Collectively, we characterized the maternal age and temperature effects on recombination rate and suggested the adaptation of meiotic recombination to environmental stimuli in cattle. Our results provided first-hand information regarding the plastic nature of meiotic recombination in a mammalian species.}, journal={FRONTIERS IN GENETICS}, author={Shen, Botong and Freebern, Ellen and Jiang, Jicai and Maltecca, Christian and Cole, John B. and Liu, George E. and Ma, Li}, year={2021}, month={Jul} }
 @article{usala_macciotta_bergamaschi_maltecca_fix_schwab_shull_tiezzi_2021, title={Genetic Parameters for Tolerance to Heat Stress in Crossbred Swine Carcass Traits}, volume={11}, ISSN={["1664-8021"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85101217596&partnerID=MN8TOARS}, DOI={10.3389/fgene.2020.612815}, abstractNote={Data for loin and backfat depth, as well as carcass growth of 126,051 three-way crossbred pigs raised between 2015 and 2019, were combined with climate records of air temperature, relative humidity, and temperature–humidity index. Environmental covariates with the largest impact on the studied traits were incorporated in a random regression model that also included genomic information. Genetic control of tolerance to heat stress and the presence of genotype by environment interaction were detected. Its magnitude was more substantial for loin depth and carcass growth, but all the traits studied showed a different impact of heat stress and different magnitude of genotype by environment interaction. For backfat depth, heritability was larger under comfortable conditions (no heat stress), as compared to heat stress conditions. Genetic correlations between extreme values of environmental conditions were lower (∼0.5 to negative) for growth and loin depth. Based on the solutions obtained from the model, sires were ranked on their breeding value for general performance and tolerance to heat stress. Antagonism between overall performance and tolerance to heat stress was moderate. Still, the models tested can provide valuable information to identify genetic material that is resilient and can perform equally when environmental conditions change. Overall, the results obtained from this study suggest the existence of genotype by environment interaction for carcass traits, as a possible genetic contributor to heat tolerance in swine.}, journal={FRONTIERS IN GENETICS}, author={Usala, Maria and Macciotta, Nicolo Pietro Paolo and Bergamaschi, Matteo and Maltecca, Christian and Fix, Justin and Schwab, Clint and Shull, Caleb and Tiezzi, Francesco}, year={2021}, month={Feb} }
 @article{fabbri_dadousis_tiezzi_maltecca_lozada-soto_biffani_bozzi_2021, title={Genetic diversity and population history of eight Italian beef cattle breeds using measures of autozygosity}, volume={16}, ISSN={["1932-6203"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85117913573&partnerID=MN8TOARS}, DOI={10.1371/journal.pone.0248087}, abstractNote={In the present study, GeneSeek GGP-LDv4 33k single nucleotide polymorphism chip was used to detect runs of homozygosity (ROH) in eight Italian beef cattle breeds, six breeds with distribution limited to Tuscany (Calvana, Mucca Pisana, Pontremolese) or Sardinia (Sarda, Sardo Bruna and Sardo Modicana) and two cosmopolitan breeds (Charolais and Limousine). ROH detection analyses were used to estimate autozygosity and inbreeding and to identify genomic regions with high frequency of ROH, which might reflect selection signatures. Comparative analysis among breeds revealed differences in length and distribution of ROH and inbreeding levels. The Charolais, Limousine, Sarda, and Sardo Bruna breeds were found to have a high frequency of short ROH (~ 15.000); Calvana and Mucca Pisana presented also runs longer than 16 Mbp. The highest level of average genomic inbreeding was observed in Tuscan breeds, around 0.3, while Sardinian and cosmopolitan breeds showed values around 0.2. The population structure and genetic distances were analyzed through principal component and multidimensional scaling analyses, and resulted in a clear separation among the breeds, with clusters related to productive purposes. The frequency of ROH occurrence revealed eight breed-specific genomic regions where genes of potential selective and conservative interest are located (e.g. MYOG , CHI3L1 , CHIT1 (BTA16), TIMELESS , APOF , OR10P1 , OR6C4 , OR2AP1 , OR6C2 , OR6C68 , CACNG2 (BTA5), COL5A2 and COL3A1 (BTA2)). In all breeds, we found the largest proportion of homozygous by descent segments to be those that represent inbreeding events that occurred around 32 generations ago, with Tuscan breeds also having a significant proportion of segments relating to more recent inbreeding.}, number={10}, journal={PLOS ONE}, author={Fabbri, Maria Chiara and Dadousis, Christos and Tiezzi, Francesco and Maltecca, Christian and Lozada-Soto, Emmanuel and Biffani, Stefano and Bozzi, Riccardo}, year={2021}, month={Oct} }
 @article{tiezzi_fix_schwab_shull_maltecca_2021, title={Gut microbiome mediates host genomic effects on phenotypes: a case study with fat deposition in pigs}, volume={19}, ISSN={["2001-0370"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85098984285&partnerID=MN8TOARS}, DOI={10.1016/j.csbj.2020.12.038}, abstractNote={A large number of studies have highlighted the importance of gut microbiome composition in shaping fat deposition in mammals. Several studies have also highlighted how host genome controls the abundance of certain species that make up the gut microbiota. We propose a systematic approach to infer how the host genome can control the gut microbiome, which in turn contributes to the host phenotype determination. We implemented a mediation test that can be applied to measured and latent dependent variables to describe fat deposition in swine (Sus scrofa). In this study, we identify several host genomic features having a microbiome-mediated effects on fat deposition. This demonstrates how the host genome can affect the phenotypic trait by inducing a change in gut microbiome composition that leads to a change in the phenotype. Host genomic variants identified through our analysis are different than the ones detected in a traditional genome-wide association study. In addition, the use of latent dependent variables allows for the discovery of additional host genomic features that do not show a significant effect on the measured variables. Microbiome-mediated host genomic effects can help understand the genetic determination of fat deposition. Since their contribution to the overall genetic variance is usually not included in association studies, they can contribute to filling the missing heritability gap and provide further insights into the host genome – gut microbiome interplay. Further studies should focus on the portability of these effects to other populations as well as their preservation when pro-/pre-/anti-biotics are used (i.e. remediation).}, journal={COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL}, author={Tiezzi, Francesco and Fix, Justin and Schwab, Clint and Shull, Caleb and Maltecca, Christian}, year={2021}, pages={530–544} }
 @article{makanjuola_maltecca_miglior_marras_abdalla_schenkel_baes_2021, title={Identification of unique ROH regions with unfavorable effects on production and fertility traits in Canadian Holsteins}, volume={53}, ISSN={["1297-9686"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85113777952&partnerID=MN8TOARS}, DOI={10.1186/s12711-021-00660-z}, abstractNote={Abstract Background The advent of genomic information and the reduction in the cost of genotyping have led to the use of genomic information to estimate genomic inbreeding as an alternative to pedigree inbreeding. Using genomic measures, effects of genomic inbreeding on production and fertility traits have been observed. However, there have been limited studies on the specific genomic regions causing the observed negative association with the trait of interest. Our aim was to identify unique run of homozygosity (ROH) genotypes present within a given genomic window that display negative associations with production and fertility traits and to quantify the effects of these identified ROH genotypes. Methods In total, 50,575 genotypes based on a 50K single nucleotide polymorphism (SNP) array and 259,871 pedigree records were available. Of these 50,575 genotypes, 46,430 cows with phenotypic records for production and fertility traits and having a first calving date between 2008 and 2018 were available. Unique ROH genotypes identified using a sliding-window approach were fitted into an animal mixed model as fixed effects to determine their effect on production and fertility traits. Results In total, 133 and 34 unique ROH genotypes with unfavorable effects were identified for production and fertility traits, respectively, at a 1% genome-wise false discovery rate. Most of these ROH regions were located on bovine chromosomes 8, 13, 14 and 19 for both production and fertility traits. For production traits, the average of all the unfavorably identified unique ROH genotypes effects were estimated to decrease milk yield by 247.30 kg, fat yield by 11.46 kg and protein yield by 8.11 kg. Similarly, for fertility traits, an average 4.81-day extension in first service to conception, a 0.16 increase in number of services, and a − 0.07 incidence in 56-day non-return rate were observed. Furthermore, a ROH region located on bovine chromosome 19 was identified that, when homozygous, had a negative effect on production traits. Signatures of selection proximate to this region have implicated GH1 as a potential candidate gene, which encodes the growth hormone that binds the growth hormone receptor. This observed negative effect could be a consequence of unfavorable alleles in linkage disequilibrium with favorable alleles. Conclusions ROH genotypes with unfavorable effects on production and fertility traits were identified within and across multiple traits on most chromosomes. These identified ROH genotypes could be included in mate selection programs to minimize their frequency in future generations.}, number={1}, journal={GENETICS SELECTION EVOLUTION}, author={Makanjuola, Bayode O. and Maltecca, Christian and Miglior, Filippo and Marras, Gabriele and Abdalla, Emhimad A. and Schenkel, Flavio S. and Baes, Christine F.}, year={2021}, month={Aug} }
 @article{wang_maltecca_wang_odle_xi_2021, title={MicroRNA and mRNA Sequencing Analyses Reveal Key Hepatic Metabolic Pathways Responsive to Maternal Malnutrition in Full-Term Fetal Pigs}, volume={5}, ISSN={2475-2991}, url={http://dx.doi.org/10.1093/cdn/nzab046_126}, DOI={10.1093/cdn/nzab046_126}, abstractNote={Maternal and infant undernutrition is highly prevalent in developing countries, leading to serious fetus/infant mortality, intrauterine growth restriction, stunting, and severe wasting. However, the effects of maternal undernutrition have generally focused on the reduced maternal nutrient supply to the fetus. The potential impairment of fetal metabolic pathways has not been well studied. Pregnant gilts (Landrace x Yorkshire x Duroc) received the NRC gestation diet with (n = 4) or without (n = 4) 50% intake restriction at insemination day and 70% for the following gestation period. Full term fetuses were obtained via C-section, two piglets were selected from each gilt in both groups and subject to hepatic tissue collection. MicroRNA and mRNA deep sequencing analysis was performed using the Illumina GAIIx system. The mRNA-miRNA correlation and associated signaling pathways were analyzed via CLC workbench, Ingenuity Pathway Analysis Software. A total of 42 differentially expressed miRNAs were identified between intake-restriction and full-nutrition group. Among of these, mir-206, mir-133b, mir-1246, mir-1843 and mir-7139 are the most downregulated and mir-10b, mir-708 and mir-222 are the most upregulated miRNAs. A total of 1215 mRNAs were identified to differentially expressed between two groups. Two metabolic pathways: retinol biosynthesis and oxidative phosphorylation were significantly modified, and the modification was associated with the miRNA changes induced by the maternal feed restriction. Briefly, the retinol biosynthesis pathway was upregulated (p < 0.01), in which those differential expressed mir-221, mir-4492, mir-1281 and mir-4492 were predicted targeting genes AADAC, CES3, PNPLA3 and RDH13 in the pathway. The oxidative phosphorylation pathway was upregulated (p < 0.05), and those differential expressed mir-1843, mir-222 and mir-184 were predicted targeting genes ATP5F1C, NDUFA1, NDUFB10, and NDUFS7 in this pathway. These results provide the framework for further understanding of negative impact of maternal malnutrition on hepatic metabolic pathways via miRNA-RNA interactions in full-term fetal pigs. Supported in part by the Bill and Melinda Gates Foundation (GCE OPP1061037) and by the North Carolina Agricultural Research Service.}, number={Supplement_2}, journal={Current Developments in Nutrition}, publisher={Elsevier BV}, author={Wang, Feng and Maltecca, Christian and Wang, Xiaoqiu and Odle, Jack and Xi, Lin}, year={2021}, month={Jun}, pages={829} }
 @article{khanal_maltecca_schwab_fix_tiezzi_2021, title={Microbiability of meat quality and carcass composition traits in swine}, volume={138}, url={https://doi.org/10.1111/jbg.12504}, DOI={10.1111/jbg.12504}, abstractNote={Abstract The impact of gut microbiome composition was investigated at different stages of production (weaning, Mid‐test and Off‐test) on meat quality and carcass composition traits of 1,123 three‐way crossbred pigs. Data were analysed using linear mixed models which included the fixed effects of dam line, contemporary group and gender as well as the random effects of pen, animal and microbiome information at different stages. The contribution of the microbiome to all traits was prominent although it varied over time, increasing from weaning to Off‐test for most traits. Microbiability estimates of carcass composition traits were greater than that of meat quality traits. Among all of the traits analysed, belly weight (BEL) had a higher microbiability estimate (0.29 ± 0.04). Adding microbiome information did not affect the estimates of genomic heritability of meat quality traits but affected the estimates of carcass composition traits. Fat depth had a greater decrease (10%) in genomic heritability at Off‐test. High microbial correlations were found among different traits, particularly with traits related to fat deposition with a decrease in the genomic correlation up to 20% for loin weight and BEL. This suggested that genomic correlation was partially contributed by genetic similarity of microbiome composition. The results indicated that better understanding of microbial composition could aid the improvement of complex traits, particularly the carcass composition traits in swine by inclusion of microbiome information in the genetic evaluation process.}, number={2}, journal={Journal of Animal Breeding and Genetics}, author={Khanal, Piush and Maltecca, Christian and Schwab, Clint and Fix, Justin and Tiezzi, Francesco}, year={2021}, month={Mar}, pages={223–236} }
 @article{maltecca_dunn_he_mcnulty_schillebeeckx_schwab_shull_fix_tiezzi_2021, title={Microbial composition differs between production systems and is associated with growth performance and carcass quality in pigs}, volume={3}, ISSN={["2524-4671"]}, url={https://doi.org/10.1186/s42523-021-00118-z}, DOI={10.1186/s42523-021-00118-z}, abstractNote={The role of the microbiome in livestock production has been highlighted in recent research. Currently, little is known about the microbiome's impact across different systems of production in swine, particularly between selection nucleus and commercial populations. In this paper, we investigated fecal microbial composition in nucleus versus commercial systems at different time points.We identified microbial OTUs associated with growth and carcass composition in each of the two populations, as well as the subset common to both. The two systems were represented by individuals with sizeable microbial diversity at weaning. At later times microbial composition varied between commercial and nucleus, with species of the genus Lactobacillus more prominent in the nucleus population. In the commercial populations, OTUs of the genera Lactobacillus and Peptococcus were associated with an increase in both growth rate and fatness. In the nucleus population, members of the genus Succinivibrio were negatively correlated with all growth and carcass traits, while OTUs of the genus Roseburia had a positive association with growth parameters. Lactobacillus and Peptococcus OTUs showed consistent effects for fat deposition and daily gain in both nucleus and commercial populations. Similarly, OTUs of the Blautia genus were positively associated with daily gain and fat deposition. In contrast, an increase in the abundance of the Bacteroides genus was negatively associated with growth performance parameters.The current study provides a first characterization of microbial communities' value throughout the pork production systems. It also provides information for incorporating microbial composition into the selection process in the quest for affordable and sustainable protein production in swine.}, number={1}, journal={ANIMAL MICROBIOME}, publisher={Springer Science and Business Media LLC}, author={Maltecca, Christian and Dunn, Rob and He, Yuqing and McNulty, Nathan P. and Schillebeeckx, Constantino and Schwab, Clint and Shull, Caleb and Fix, Justin and Tiezzi, Francesco}, year={2021}, month={Aug} }
 @inproceedings{déru_tiezzi_carillier-jacquin_blanchet_cauquil_zemb_maltecca_bouquet_gilbert_2021, title={Microbiome and genetic contribution to the phenotypic variation of digestive efficiency in pig. 72}, volume={27}, booktitle={Annual Meeting of the European Federation of Animal Science (EAAP)}, publisher={Wageningen Academic Publishers}, author={Déru, Vanille and Tiezzi, F. and Carillier-Jacquin, C. and Blanchet, B. and Cauquil, L. and Zemb, O. and Maltecca, C. and Bouquet, A. and Gilbert, H.}, year={2021}, pages={571} }
 @article{jiang_o’neill_maltecca_fix_crum_schwab_tiezzi_2021, title={PSXII-12 Partitioning direct and maternal genetic effects into additive and non-additive components for growth and maternal traits in Yorkshire pigs}, volume={99}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skab235.459}, DOI={10.1093/jas/skab235.459}, abstractNote={Abstract This study investigates how much direct and maternal non-additive genetic effects contribute to growth and maternal traits in swine. We analyzed a sample of 19,475 genotyped Yorkshire pigs from Acuity Ag Solutions, LLC (Carlyle, IL). Approximately 50K SNPs were kept after quality control, and missing genotypes were then imputed using findhap.f90. The genotypes were used to construct genomic relationship matrices (GRMs) corresponding to additive (A), dominance (D), and additive-by-additive epistasis (E) effects for both direct and maternal effects. The GRMs were subsequently employed as covariance structure matrices in a linear mixed model consisting of eight random components, namely three direct genetic effects (Ad, Dd, and Ed), three maternal genetic effects (Am, Dm, and Em), maternal environmental effect, and common litter environmental effect. We estimated these variance components (VCs) for six growth traits (birth weight, average daily gain, back fat, and loin area) and six maternal traits of a sow (total number of piglets born, number of piglets born alive, average weight of piglets at birth, average weight of piglets weaned) using REML in MMAP (https://mmap.github.io/). As shown in Table 1, we found significant (P< 0.05) direct dominance and epistasis VCs for all six growth traits. Additionally, direct epistasis effects explained a larger proportion of phenotypic variation than direct dominance for all growth traits (0.04–0.12 vs. 0.01–0.04). In contrast, direct non-additive VCs were not significant for any maternal trait except for epistasis in average weight of piglets weaned. As for maternal non-additive effects, we only discovered significant additive VC in birth weight and average daily gain and significant epistasis VC in back fat (P< 0.05). Other maternal genetic VCs were largely negligible. In summary, direct dominance and epistasis effects play a prominent role in growth traits of Yorkshire pigs.}, number={Supplement_3}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Jiang, Jicai and O’Neill, Shauneen and Maltecca, Christian and Fix, Justin and Crum, Tamar and Schwab, Clint and Tiezzi, Francesco}, year={2021}, month={Oct}, pages={251–252} }
 @misc{he_maltecca_tiezzi_2021, title={Potential Use of Gut Microbiota Composition as a Biomarker of Heat Stress in Monogastric Species: A Review}, volume={11}, ISSN={["2076-2615"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85108092136&partnerID=MN8TOARS}, DOI={10.3390/ani11061833}, abstractNote={Heat stress is a current challenge for livestock production, and its impact could dramatically increase if global temperatures continue to climb. Exposure of agricultural animals to high ambient temperatures and humidity would lead to substantial economic losses because it compromises animal performance, productivity, health, and welfare. The gut microbiota plays essential roles in nutrient absorption, energy balance, and immune defenses through profound symbiotic interactions with the host. The homeostasis of those diverse gut microorganisms is critical for the host’s overall health and welfare status and also is sensitive to environmental stressors, like heat stress, reflected in altered composition and functionality. This article aims to summarize the research progress on the interactions between heat stress and gut microbiome and discuss the potential use of the gut microbiota composition as a biomarker of heat stress in monogastric animal species. A comprehensive understanding of the gut microbiota’s role in responding to or regulating physiological activities induced by heat stress would contribute to developing mitigation strategies.}, number={6}, journal={ANIMALS}, publisher={MDPI AG}, author={He, Yuqing and Maltecca, Christian and Tiezzi, Francesco}, year={2021}, month={Jun} }
 @article{he_tiezzi_howard_maltecca_2021, title={Predicting body weight in growing pigs from feeding behavior data using machine learning algorithms}, volume={184}, ISSN={["1872-7107"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85104932146&partnerID=MN8TOARS}, DOI={10.1016/j.compag.2021.106085}, abstractNote={A timely and accurate estimation of body weight in finishing pigs is critical in determining profits by allowing pork producers to make informed marketing decisions on group-housed pigs while reducing labor and feed costs. This study investigated the usefulness of feeding behavior data in predicting the body weight of pigs at the finishing stage. We obtained data on 655 pigs of three breeds (Duroc, Landrace, and Large White) from 75 to 166 days of age. Feeding behavior, feed intake, and body weight information were recorded when a pig visited the Feed Intake Recording Equipment in each pen. Data collected from 75 to 158 days of age were split into six slices of 14 days each and used to calibrate predictive models. LASSO regression and two machine learning algorithms (Random Forest and Long Short-term Memory network) were selected to forecast the body weight of pigs aged from 159 to 166 days using four scenarios: individual-informed predictive scenario, individual- and group-informed predictive scenario, breed-specific individual- and group-informed predictive scenario, and group-informed predictive scenario. We developed four models for each scenario: Model_Age included only age, Model_FB included only feeding behavior variables, Model_Age_FB and Model_Age_FB_FI added feeding behavior and feed intake measures on the basis of Model_Age as predictors. Pearson's correlation, root mean squared error, and binary diagnostic tests were used to assess predictive performance. The greatest correlation was 0.87, and the highest accuracy was 0.89 for the individual-informed prediction, while they were 0.84 and 0.85 for the individual- and group-informed predictions, respectively. The least root mean squared error of both scenarios was about 10 kg. The best prediction performed by Model_FB had a correlation of 0.83, an accuracy of 0.74, and a root mean squared error of 14.3 kg in the individual-informed prediction. The effect of the addition of feeding behavior and feed intake data varied across algorithms and scenarios from a small to moderate improvement in predictive performance. We also found differences in predictive performance associated with the time slices or pigs used in the training set, the algorithm employed, and the breed group considered. Overall, this study's findings connect the dynamics of feeding behavior to body growth and provide a promising picture of the involvement of feeding behavior data in predicting the body weight of group-housed pigs.}, journal={COMPUTERS AND ELECTRONICS IN AGRICULTURE}, author={He, Yuqing and Tiezzi, Francesco and Howard, Jeremy and Maltecca, Christian}, year={2021}, month={May} }
 @article{lozada-soto_maltecca_lu_miller_cole_tiezzi_2021, title={Trends in genetic diversity and the effect of inbreeding in American Angus cattle under genomic selection}, volume={53}, ISSN={["1297-9686"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85108104621&partnerID=MN8TOARS}, DOI={10.1186/s12711-021-00644-z}, abstractNote={Abstract Background While the adoption of genomic evaluations in livestock has increased genetic gain rates, its effects on genetic diversity and accumulation of inbreeding have raised concerns in cattle populations. Increased inbreeding may affect fitness and decrease the mean performance for economically important traits, such as fertility and growth in beef cattle, with the age of inbreeding having a possible effect on the magnitude of inbreeding depression. The purpose of this study was to determine changes in genetic diversity as a result of the implementation of genomic selection in Angus cattle and quantify potential inbreeding depression effects of total pedigree and genomic inbreeding, and also to investigate the impact of recent and ancient inbreeding. Results We found that the yearly rate of inbreeding accumulation remained similar in sires and decreased significantly in dams since the implementation of genomic selection. Other measures such as effective population size and the effective number of chromosome segments show little evidence of a detrimental effect of using genomic selection strategies on the genetic diversity of beef cattle. We also quantified pedigree and genomic inbreeding depression for fertility and growth. While inbreeding did not affect fertility, an increase in pedigree or genomic inbreeding was associated with decreased birth weight, weaning weight, and post-weaning gain in both sexes. We also measured the impact of the age of inbreeding and found that recent inbreeding had a larger depressive effect on growth than ancient inbreeding. Conclusions In this study, we sought to quantify and understand the possible consequences of genomic selection on the genetic diversity of American Angus cattle. In both sires and dams, we found that, generally, genomic selection resulted in decreased rates of pedigree and genomic inbreeding accumulation and increased or sustained effective population sizes and number of independently segregating chromosome segments. We also found significant depressive effects of inbreeding accumulation on economically important growth traits, particularly with genomic and recent inbreeding.}, number={1}, journal={GENETICS SELECTION EVOLUTION}, author={Lozada-Soto, Emmanuel A. and Maltecca, Christian and Lu, Duc and Miller, Stephen and Cole, John B. and Tiezzi, Francesco}, year={2021}, month={Jun} }
 @inproceedings{dewitt_guedira_lyerly_ward_murphy_marshall_santantonio_griffey_boyles_mergoum_et al._2021, title={Unpacking the Yield Effects of Major Heading Date Alleles in Wheat through Joint Analysis of Historic Breeding Panels and Their Climates}, booktitle={ASA, CSSA, SSSA International Annual Meeting}, author={DeWitt, N. and Guedira, M. and Lyerly, J. and Ward, B.P. and Murphy, J.P. and Marshall, D. and Santantonio, N. and Griffey, C.A. and Boyles, R.E. and Mergoum, M. and et al.}, year={2021} }
 @article{cole_eaglen_maltecca_mulder_pryce_2020, title={16 Opportunities and challenges from deep-phenotyping of dairy cattle}, volume={98}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skaa278.010}, DOI={10.1093/jas/skaa278.010}, abstractNote={Abstract Genetic selection has been a very successful tool for the long-term improvement of livestock populations, and the rapid adoption of genomic selection over the last decade has doubled the rate of gain in some populations. However, the full expression of genetic potential requires that animals are placed in environments that support such performance. Increasingly complex dairy cattle production systems require that all aspects of animal performance are measured across individuals’ lifetimes. Selection emphasis is shifting away from traits related to animal productivity towards those related to efficient resource utilization and increased animal welfare. However, phenotypes for many of these new traits are difficult or expensive to measure, or both. This is driving interest in sensor-based systems that provide continuous measurements of the farm environment, individual animal performance, and detailed milk composition. The goal of phenomics is to provide information for making decisions related to on-farm management, as well as genetic improvement. However, many challenges accompany these new technologies, including a lack of standardization, the need for high-speed Internet connections, increased computational requirements, and training to integrate these tools with more traditional management tools. There also is a lack of translational research on the use of these data for real-time precision management. We will identify opportunities and challenges associated with phenomics and discuss on-farm applications of these new tools.}, number={Supplement_4}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Cole, John B and Eaglen, Sophie A E and Maltecca, Christian and Mulder, Han A and Pryce, Jennie}, year={2020}, month={Nov}, pages={5–6} }
 @article{he_tiezzi_maltecca_2020, title={249 Predicting body weight of finishing pigs using machine and deep learning algorithms}, volume={98}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skaa278.324}, DOI={10.1093/jas/skaa278.324}, abstractNote={Abstract Understanding and exploiting feeding patterns in swine could allow a reduced feed waste and minimized sorting losses. The objectives of this study were to evaluate the ability to predict whether a pig reached a target weight at finishing by using several algorithms and to compare the prediction using varying amounts of data during the growing period. Data were collected on 655 pigs from 75 to 166 days of age. Pigs were housed with 8 to 15 pigs and a Feed Intake Recording Equipment in each pen. Feed consumption, occupation time, and body weight per visit were recorded when a pig visited the feeder. Lasso Regression (LS), a machine learning algorithm: Random Forest (RF), and a deep learning algorithm: Long-short Term Memory (LSTM) network, were used to forecast whether pigs can reach 129 kg at the finishing stage (159–166 d). Times of visits, a sum of feed consumption, a sum of occupation time in the feeder every day, and age were used as predictors. Data were split into 6 slices by 14 days and used to calibrate the models and their predictive ability was tested with data corresponding to the last 8 days of the study period. The greatest correlation coefficients were 0.799, 0.828, and 0.868 using slice 6 (145–158 d) to train the LS, RF, and LSTM, respectively. The LS and LSTM algorithms had a smaller root mean squared error, 0.863 and 0.895 compared to the RF with 1.375 in the prediction. Overall, LS and LSTM performed best. Predictions using data closest to the finishing stage proved better. This study connects the dynamics of feeding behavior and feed intake data to growth using prediction methods that will hopefully accelerate the mainstream application of electronic feeders in pig production systems.}, number={Supplement_4}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={He, Yuqing and Tiezzi, Francesco and Maltecca, Christian}, year={2020}, month={Nov}, pages={176–176} }
 @article{lozada-soto_tiezzi_lu_miller_cole_maltecca_2020, title={30 Inbreeding in American Angus cattle before and after the implementation of genomic selection}, volume={98}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skaa278.026}, DOI={10.1093/jas/skaa278.026}, abstractNote={Abstract The aim of this study was to characterize the American Angus cattle population in terms of changes to the inbreeding rate (ΔF) and effective population size (Ne) before and after the implementation of genomic selection (GS). Genomic information (89,206 SNPs) was obtained for 25,960 bulls and 134,962 cows born between the years 2000 and 2017. Bulls and cows were independently grouped into two groups based on year of birth, pre-GS (2000–2009), and post-GS (2010–2017). Genomic inbreeding (FGRM) was calculated assuming fixed allele frequencies (0.5). Inbreeding based on runs of homozygosity (FROH) was calculated using software SNP1101 (Sargolzaei, 2014). The yearly ΔF for each group was estimated by regressing the inbreeding coefficients on year of birth. The generation intervals (L) were calculated for each of the four pathways of selection at both time periods (pre-GS and post-GS), where the mean of the sires of sires and dams of sires pathways was taken to be the generation interval for the bulls and the mean of the sires of dams and dams of dams pathways was taken to be the generation interval for the cows. The L and ΔF of the three inbreeding coefficients were used to estimate the Ne. Estimates of ΔF and Ne for both sexes at the two time periods can be found in table 1. In both sexes, ΔFROH decreased and NeROH increased from pre-GS to post-GS. For bulls, ΔFGRM and NeGRM did not change, and for cows, ΔFGRM decreased and NeGRM increased from pre-GS to post-GS. These results suggest that the implementation of genomic selection in Angus cattle has not caused the increased inbreeding rates and reduced effective population sizes seen in other species, but instead has been beneficial for the preservation of genetic diversity.}, number={Supplement_4}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Lozada-Soto, Emmanuel A and Tiezzi, Francesco and Lu, Duc and Miller, Stephen P and Cole, John B and Maltecca, Christian}, year={2020}, month={Nov}, pages={14–14} }
 @article{tiezzi_bergamaschi_howard_maltecca_2020, title={43 Feed efficiency and behavior are associated with gut microbiome in three breeds of pigs}, volume={98}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skaa278.044}, DOI={10.1093/jas/skaa278.044}, abstractNote={Abstract Feed efficiency and behavior are important traits in the pork industry for economic, welfare, and environmental aspects. The gut microbiota plays an important role in nutrient digestibility and it is likely to influence these traits. The aim of this study was to characterize the feed efficiency, feeding behavior and gut microbiome relationships of pigs belonging to three different breeds. Individual body weight, feed intake and rate of Duroc (n = 222), Landrace (n = 244), and Large White (n = 221) pigs were recorded. Rectal fecal samples were collected from each animal at three time points (T1, start of trial; T2, mid-trial; T3, end of trial) and used for microbiome 16S rRNA gene sequencing. Individual feed intake and body weight were edited to obtain average daily gain (ADG), average daily feed intake (ADFI), feed conversion ratio (FCR), average daily feeding rate (ADFR), average feed intake per visit (AFIV), average daily number of visits to feeder (ANVD), average daily occupation time (AOTD), average occupation time per visit (AOTV). The impact of gut microbiome on the traits studied was present and seemed to depend on the breed and the time point of recording. At T1, Oscillibacter and Phascolarctobacterium had negative impact on ANVD and ADFI in Duroc, Ruminococcus had negative impact on ADFI in Landrace and Parvimonas, Escherichia and Anaerovibrio had negative impact on ADFI and ANVD in Large White. At T2, Lactobacillus showed a positive impact on ADFR in Landrace and on ADFI in Large White. At T3, Ruminococcus, Faecalibacterium and Dorea had a positive impact on ADFI in Duroc, Staphylococcous had positive impact on ADFR in Landrace and Peptoniphilus had negative impact on ADFI in Large White. Gut microbiome may have an heterogenous impact on the regulation of feeding behavior and feed efficiency depending on the host genotype.}, number={Supplement_4}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Tiezzi, Francesco and Bergamaschi, Matteo and Howard, Jeremy and Maltecca, Christian}, year={2020}, month={Nov}, pages={24–24} }
 @article{lozada-soto_maltecca_anderson_tiezzi_2020, title={Analysis of milk leukocyte differential measures for use in management practices for decreased mastitis incidence}, volume={103}, url={https://doi.org/10.3168/jds.2019-16355}, DOI={10.3168/jds.2019-16355}, abstractNote={The aim of this study was to assess the usefulness of measures derived from milk leukocyte differential (MLD) in practices that improve fresh cow mastitis monitoring and decrease mastitis incidence. Quarter milk samples were collected from Holstein and Jersey cows on d 4 and 11 postcalving. Samples were analyzed using MLD, whereby cell counts and quarter infection diagnosis were obtained. Measures derived from MLD included cell scores (total leukocyte, neutrophil, macrophage, and lymphocyte scores), cell proportions (neutrophil, macrophage, and lymphocyte percentages), cell thresholds (total leukocyte, neutrophil, macrophage, and lymphocyte thresholds), and MLD diagnosis at different threshold settings (A, B, and C). Microbiological culturing of milk samples was used to determine infection status to compare the MLD diagnosis and serve as an indicator of infection. Measures derived from the microbiological analysis included occurrence of major pathogens, minor pathogens, and infection. Data analysis was based on a linear mixed model, which was used on all measures for the estimation of the fixed effects of breed, lactation number, day of sample collection, time of sampling, and quarter location, and the random effects of animal and week of sampling. All the fixed effects studied were significant for one or more of the analyzed measures. The results of this study showed that MLD-derived measures justify further study on their use for management practices for mastitis screening and prevention in early lactation.}, number={1}, journal={Journal of Dairy Science}, publisher={American Dairy Science Association}, author={Lozada-Soto, E. and Maltecca, C. and Anderson, K. and Tiezzi, F.}, year={2020}, month={Jan}, pages={572–582} }
 @article{makanjuola_miglior_abdalla_maltecca_schenkel_baes_2020, title={Effect of genomic selection on rate of inbreeding and coancestry and effective population size of Holstein and Jersey cattle populations}, volume={103}, url={https://doi.org/10.3168/jds.2019-18013}, DOI={10.3168/jds.2019-18013}, abstractNote={Genetic diversity in livestock populations is a significant contributor to the sustainability of animal production. Also, genetic diversity allows animal production to become more responsive to environmental changes and market demands. The loss of genetic diversity can result in a plateau in production and may also result in loss of fitness or viability in animal production. In this study, we investigated the rate of inbreeding (ΔF), rate of coancestry (Δf), and effective population size (Ne) as important quantitative indicators of genetic diversity and evaluated the effect of the recent implementation of genomic selection on the loss of genetic diversity in North American Holstein and Jersey dairy cattle. To estimate the rate of inbreeding and coancestry, inbreeding and coancestry coefficients were calculated using the traditional pedigree method and genomic methods estimated from segment- and marker-based approaches. Furthermore, we estimated Ne from the rate of inbreeding and coancestry and extent of linkage disequilibrium. A total of 205,755 and 89,238 pedigreed and genotyped animals born between 1990 and 2018 inclusively were available for Holsteins and Jerseys, respectively. The estimated average pedigree inbreeding coefficients were 7.74 and 7.20% for Holsteins and Jerseys, respectively. The corresponding values for the segment and marker-by-marker genomic inbreeding coefficients were 13.61, 15.64, and 31.40% for Holsteins and 21.16, 22.54, and 42.62% for Jerseys, respectively. The average coancestry coefficients were 8.33 and 15.84% for Holsteins and 9.23 and 23.46% for Jerseys with pedigree and genomic measures, respectively. Generation interval for the whole 29-yr time period averaged approximately 5 yr for all selection pathways combined. The ΔF per generation based on pedigree, segment, and marker-by-marker genomic measures for the entire 29-yr period was estimated to be 0.75, 1.10, 1.16, and 1.02% for Holstein animals and 0.67, 0.62, 0.63, and 0.59% for Jersey animals, respectively. The Δf was estimated to be 0.98 and 0.98% for Holsteins and 0.73 and 0.78% for Jerseys with pedigree and genomic measures, respectively. These ΔF and Δf translated to an Ne that ranged from 43 to 66 animals for Holsteins and 64 to 85 animals for Jerseys. In addition, the Ne based on linkage disequilibrium was 58 and 120 for Holsteins and Jerseys, respectively. The 10-yr period that involved the application of genomic selection resulted in an increased ΔF per generation with ranges from 1.19 to 2.06% for pedigree and genomic measures in Holsteins. Given the rate at which inbreeding is increasing after the implementation of genomic selection, there is a need to implement measures and means for controlling the rate of inbreeding per year, which will help to manage and maintain farm animal genetic resources.}, number={6}, journal={Journal of Dairy Science}, publisher={American Dairy Science Association}, author={Makanjuola, Bayode O. and Miglior, Filippo and Abdalla, Emhimad A. and Maltecca, Christian and Schenkel, Flavio S. and Baes, Christine F.}, year={2020}, month={Jun}, pages={5183–5199} }
 @article{makanjuola_maltecca_miglior_schenkel_baes_2020, title={Effect of recent and ancient inbreeding on production and fertility traits in Canadian Holsteins}, volume={21}, ISSN={["1471-2164"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85090180171&partnerID=MN8TOARS}, DOI={10.1186/s12864-020-07031-w}, abstractNote={Abstract Background Phenotypic performances of livestock animals decline with increasing levels of inbreeding, however, the noticeable decline known as inbreeding depression, may not be due only to the total level of inbreeding, but rather could be distinctly associated with more recent or more ancient inbreeding. Therefore, splitting inbreeding into different age classes could help in assessing detrimental effects of different ages of inbreeding. Hence, this study sought to investigate the effect of recent and ancient inbreeding on production and fertility traits in Canadian Holstein cattle with both pedigree and genomic records. Furthermore, inbreeding coefficients were estimated using traditional pedigree measure ( F PED ) and genomic measures using segment based ( F ROH ) and marker-by-marker ( F GRM ) based approaches. Results Inbreeding depression was found for all production and most fertility traits, for example, every 1% increase in F PED , F ROH and F GRM was observed to cause a − 44.71, − 40.48 and − 48.72 kg reduction in 305-day milk yield (MY), respectively. Similarly, an extension in first service to conception (FSTC) of 0.29, 0.24 and 0.31 day in heifers was found for every 1% increase in F PED , F ROH and F GRM , respectively. Fertility traits that did not show significant depression were observed to move in an unfavorable direction over time. Splitting both pedigree and genomic inbreeding into age classes resulted in recent age classes showing more detrimental inbreeding effects, while more distant age classes caused more favorable effects. For example, a − 1.56 kg loss in 305-day protein yield (PY) was observed for every 1% increase in the most recent pedigree age class, whereas a 1.33 kg gain was found per 1% increase in the most distant pedigree age class. Conclusions Inbreeding depression was observed for production and fertility traits. In general, recent inbreeding had unfavorable effects, while ancestral inbreeding had favorable effects. Given that more negative effects were estimated from recent inbreeding when compared to ancient inbreeding suggests that recent inbreeding should be the primary focus of selection programs. Also, further work to identify specific recent homozygous regions negatively associated with phenotypic traits could be investigated.}, number={1}, journal={BMC GENOMICS}, author={Makanjuola, Bayode O. and Maltecca, Christian and Miglior, Filippo and Schenkel, Flavio S. and Baes, Christine F.}, year={2020}, month={Sep} }
 @article{lozada‐soto_maltecca_wackel_flowers_gray_he_huang_jiang_tiezzi_2020, title={Evidence for recombination variability in purebred swine populations}, volume={138}, ISSN={0931-2668 1439-0388}, url={http://dx.doi.org/10.1111/jbg.12510}, DOI={10.1111/jbg.12510}, abstractNote={Abstract This study aimed to investigate interpopulation variation due to sex, breed and age, and the intrapopulation variation in the form of genetic variance for recombination in swine. Genome‐wide recombination rate and recombination occurrences (RO) were traits studied in Landrace (LR) and Large White (LW) male and female populations. Differences were found for sex, breed, sex‐breed interaction, and age effects for genome‐wide recombination rate and RO at one or more chromosomes. Dams were found to have a higher genome‐wide recombination rate and RO at all chromosomes than sires. LW animals had higher genome‐wide recombination rate and RO at seven chromosomes but lower at two chromosomes than LR individuals. The sex‐breed interaction effect did not show any pattern not already observable by sex. Recombination increased with increasing parity in females, while in males no effect of age was observed. We estimated heritabilities and repeatabilities for both investigated traits and obtained the genetic correlation between male and female genome‐wide recombination rate within each of the two breeds studied. Estimates of heritability and repeatability were low ( h 2 = 0.01–0.26; r = 0.18–0.42) for both traits in all populations. Genetic correlations were high and positive, with estimates of 0.98 and 0.94 for the LR and LW breeds, respectively. We performed a GWAS for genome‐wide recombination rate independently in the four sex/breed populations. The results of the GWAS were inconsistent across the four populations with different significant genomic regions identified. The results of this study provide evidence of variability for recombination in purebred swine populations.}, number={2}, journal={Journal of Animal Breeding and Genetics}, publisher={Wiley}, author={Lozada‐Soto, Emmanuel A. and Maltecca, Christian and Wackel, Hanna and Flowers, William and Gray, Kent and He, Yuqing and Huang, Yijian and Jiang, Jicai and Tiezzi, Francesco}, year={2020}, month={Sep}, pages={259–273} }
 @article{freebern_santos_fang_jiang_parker gaddis_liu_vanraden_maltecca_cole_ma_2020, title={GWAS and fine-mapping of livability and six disease traits in Holstein cattle}, volume={21}, ISSN={["1471-2164"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85077786273&partnerID=MN8TOARS}, DOI={10.1186/s12864-020-6461-z}, abstractNote={Abstract Background Health traits are of significant economic importance to the dairy industry due to their effects on milk production and associated treatment costs. Genome-wide association studies (GWAS) provide a means to identify associated genomic variants and thus reveal insights into the genetic architecture of complex traits and diseases. The objective of this study is to investigate the genetic basis of seven health traits in dairy cattle and to identify potential candidate genes associated with cattle health using GWAS, fine mapping, and analyses of multi-tissue transcriptome data. Results We studied cow livability and six direct disease traits, mastitis, ketosis, hypocalcemia, displaced abomasum, metritis, and retained placenta, using de-regressed breeding values and more than three million imputed DNA sequence variants. After data edits and filtering on reliability, the number of bulls included in the analyses ranged from 11,880 (hypocalcemia) to 24,699 (livability). GWAS was performed using a mixed-model association test, and a Bayesian fine-mapping procedure was conducted to calculate a posterior probability of causality to each variant and gene in the candidate regions. The GWAS detected a total of eight genome-wide significant associations for three traits, cow livability, ketosis, and hypocalcemia, including the bovine Major Histocompatibility Complex (MHC) region associated with livability. Our fine-mapping of associated regions reported 20 candidate genes with the highest posterior probabilities of causality for cattle health. Combined with transcriptome data across multiple tissues in cattle, we further exploited these candidate genes to identify specific expression patterns in disease-related tissues and relevant biological explanations such as the expression of Group-specific Component ( GC ) in the liver and association with mastitis as well as the Coiled-Coil Domain Containing 88C ( CCDC88C ) expression in CD8 cells and association with cow livability. Conclusions Collectively, our analyses report six significant associations and 20 candidate genes of cattle health. With the integration of multi-tissue transcriptome data, our results provide useful information for future functional studies and better understanding of the biological relationship between genetics and disease susceptibility in cattle.}, number={1}, journal={BMC GENOMICS}, author={Freebern, Ellen and Santos, Daniel J. A. and Fang, Lingzhao and Jiang, Jicai and Parker Gaddis, Kristen L. and Liu, George E. and VanRaden, Paul M. and Maltecca, Christian and Cole, John B. and Ma, Li}, year={2020}, month={Jan} }
 @article{bergamaschi_maltecca_fix_schwab_tiezzi_2020, title={Genome-wide association study for carcass quality traits and growth in purebred and crossbred pigs}, volume={98}, ISSN={["1525-3163"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85078371039&partnerID=MN8TOARS}, DOI={10.1093/jas/skz360}, abstractNote={Abstract Carcass quality traits such as back fat (BF), loin depth (LD), and ADG are of extreme economic importance for the swine industry. This study aimed to (i) estimate the genetic parameters for such traits and (ii) conduct a single-step genome-wide association study (ssGWAS) to identify genomic regions that affect carcass quality and growth traits in purebred (PB) and three-way crossbred (CB) pigs. A total of 28,497 PBs and 135,768 CBs pigs were phenotyped for BF, LD, and ADG. Of these, 4,857 and 3,532 were genotyped using the Illumina PorcineSNP60K Beadchip. After quality control, 36,328 SNPs were available and were used to perform an ssGWAS. A bootstrap analysis (n = 1,000) and a signal enrichment analysis were performed to declare SNP significance. Genome regions were based on the variance explained by significant 10-SNP sliding windows. Estimates of PB heritability (SE) were 0.42 (0.019) for BF, 0.39 (0.020) for LD, and 0.35 (0.021) for ADG. Estimates of CB heritability were 0.49 (0.042) for BF, 0.27 (0.029) for LD, and 0.12 (0.021) for ADG. Genetic correlations (SE) across the two populations were 0.81 (0.02), 0.79 (0.04), and 0.56 (0.05), for BF, LD, and ADG, respectively. The variance explained by significant regions for each trait in PBs ranged from 1.51% to 1.35% for BF, from 4.02% to 3.18% for LD, and from 2.26% to 1.45% for ADG. In CBs, the variance explained by significant regions ranged from 1.88% to 1.37% for BF, from 1.29% to 1.23% for LD, and from 1.54% to 1.32% for ADG. In this study, we have described regions of the genome that determine carcass quality and growth traits of PB and CB pigs. These results provide evidence that there are overlapping and nonoverlapping regions in the genome influencing carcass quality and growth traits in PBs and three-way CB pigs.}, number={1}, journal={JOURNAL OF ANIMAL SCIENCE}, author={Bergamaschi, Matteo and Maltecca, Christian and Fix, Justin and Schwab, Clint and Tiezzi, Francesco}, year={2020}, month={Jan} }
 @article{tiezzi_brito_howard_huang_gray_schwab_fix_maltecca_2020, title={Genomics of Heat Tolerance in Reproductive Performance Investigated in Four Independent Maternal Lines of Pigs}, volume={11}, ISSN={["1664-8021"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85087889959&partnerID=MN8TOARS}, DOI={10.3389/fgene.2020.00629}, abstractNote={Improving swine climatic resilience through genomic selection has the potential to minimize welfare issues and increase the industry profitability. The main objective of this study was to investigate the genetic and genomic determinism of tolerance to heat stress in four independent purebred populations of swine. Three female reproductive traits were investigated: total number of piglets born (TNB), number of piglets born alive (NBA) and average birth weight (ABW). More than 80,000 phenotypic and 12,000 genotyped individuals were included in this study. Genomic random-regression models were fitted regressing the phenotypes of interest on a set of 95 environmental covariates extracted from public weather station records. The models yielded estimates of (genomic) reactions norms for individual pigs, as indicator of heat tolerance. Heat tolerance is a heritable trait, although the heritabilities are larger under comfortable than heat-stress conditions (larger than 0.05 vs. 0.02 for TNB; 0.10 vs. 0.05 for NBA; larger than 0.20 vs. 0.10 for ABW). TNB showed the lowest genetic correlation (-38%) between divergent climatic conditions, being the trait with the strongest impact of genotype by environment interaction, while NBA and ABW showed values slightly negative or equal to zero reporting a milder impact of the genotype by environment interaction. After estimating genetic parameters, a genome-wide association study was performed based on the single-step GBLUP method. Heat tolerance was observed to be a highly polygenic trait. Multiple and non-overlapping genomic regions were identified for each trait based on the genomic breeding values for reproductive performance under comfortable or heat stress conditions. Relevant regions were found on chromosomes (SSC) 1, 3, 5, 6, 9, 11, and 12, although there were important regions across all autosomal chromosomes. The genomic region located on SSC9 appears to be of particular interest since it was identified for two traits (TNB and NBA) and in two independent populations. Heat tolerance based on reproductive performance indicators is a heritable trait and genetic progress for heat tolerance can be achieved through genetic or genomic selection. Various genomic regions and candidate genes with important biological functions were identified, which will be of great value for future functional genomic studies.}, journal={FRONTIERS IN GENETICS}, author={Tiezzi, Francesco and Brito, Luiz F. and Howard, Jeremy and Huang, Yi Jian and Gray, Kent and Schwab, Clint and Fix, Justin and Maltecca, Christian}, year={2020}, month={Jun} }
 @book{bergamaschi_tiezzi_howard_huang_gray_schillebeeckx_mcnulty_maltecca_2020, title={Gut microbiome and feed efficiency of pigs}, DOI={10.21203/rs.3.rs-106069/v1}, author={Bergamaschi, M. and Tiezzi, F. and Howard, J. and Huang, Y.J. and Gray, K.A. and Schillebeeckx, C. and McNulty, N.P. and Maltecca, C.}, year={2020}, month={Nov} }
 @article{bergamaschi_tiezzi_howard_huang_gray_schillebeeckx_mcnulty_maltecca_2020, title={Gut microbiome composition differences among breeds impact feed efficiency in swine}, volume={8}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85088464470&partnerID=MN8TOARS}, DOI={10.1186/s40168-020-00888-9}, abstractNote={Abstract Background Feed efficiency is a crucial parameter in swine production, given both its economic and environmental impact. The gut microbiota plays an essential role in nutrient digestibility and is, therefore, likely to affect feed efficiency. This study aimed to characterize feed efficiency, fatness traits, and gut microbiome composition in three major breeds of domesticated swine and investigate a possible link between feed efficiency and gut microbiota composition. Results Average daily feed intake (ADFI), average daily gain (ADG), feed conversion ratio (FCR), residual feed intake (RFI), backfat, loin depth, and intramuscular fat of 615 pigs belonging to the Duroc (DR), Landrace (LR), and Large White (LW) breeds were measured. Gut microbiota composition was characterized by 16S rRNA gene sequencing. Orthogonal contrasts between paternal line (DR) and maternal lines (LR+LW) and between the two maternal lines (LR versus LW) were performed. Average daily feed intake and ADG were statistically different with DR having lower ADFI and ADG compared to LR and LW. Landrace and LW had a similar ADG and RFI, with higher ADFI and FCR for LW. Alpha diversity was higher in the fecal microbial communities of LR pigs than in those of DR and LW pigs for all time points considered. Duroc communities had significantly higher proportional representation of the Catenibacterium and Clostridium genera compared to LR and LW, while LR pigs had significantly higher proportions of Bacteroides than LW for all time points considered. Amplicon sequence variants from multiple genera (including Anaerovibrio , Bacteroides , Blautia , Clostridium , Dorea , Eubacterium , Faecalibacterium , Lactobacillus , Oscillibacter , and Ruminococcus ) were found to be significantly associated with feed efficiency, regardless of the time point considered. Conclusions In this study, we characterized differences in the composition of the fecal microbiota of three commercially relevant breeds of swine, both over time and between breeds. Correlations between different microbiome compositions and feed efficiency were established. This suggests that the microbial community may contribute to shaping host productive parameters. Moreover, our study provides important insights into how the intestinal microbial community might influence host energy harvesting capacity. A deeper understanding of this process may allow us to modulate the gut microbiome in order to raise more efficient animals.}, number={1}, journal={Microbiome}, publisher={Springer Science and Business Media LLC}, author={Bergamaschi, Matteo and Tiezzi, Francesco and Howard, Jeremy and Huang, Yi Jian and Gray, Kent A. and Schillebeeckx, Constantino and McNulty, Nathan P. and Maltecca, Christian}, year={2020} }
 @article{bergamaschi_maltecca_schillebeeckx_mcnulty_schwab_shull_fix_tiezzi_2020, title={Heritability and genome-wide association of swine gut microbiome features with growth and fatness parameters}, volume={10}, ISSN={["2045-2322"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85086790625&partnerID=MN8TOARS}, DOI={10.1038/s41598-020-66791-3}, abstractNote={Abstract Despite recent efforts to characterize longitudinal variation in the swine gut microbiome, the extent to which a host’s genome impacts the composition of its gut microbiome is not yet well understood in pigs. The objectives of this study were: i) to identify pig gut microbiome features associated with growth and fatness, ii) to estimate the heritability of those features, and, iii) to conduct a genome-wide association study exploring the relationship between those features and single nucleotide polymorphisms ( SNP ) in the pig genome. A total of 1,028 pigs were characterized. Animals were genotyped with the Illumina PorcineSNP60 Beadchip. Microbiome samples from fecal swabs were obtained at weaning ( Wean ), at mid-test during the growth trial ( MidTest ), and at the end of the growth trial ( OffTest ). Average daily gain was calculated from birth to week 14 of the growth trial, from weaning to week 14, from week 14 to week 22, and from week 14 to harvest. Backfat and loin depth were also measured at weeks 14 and 22. Heritability estimates (±SE) of Operational Taxonomic Units ranged from 0.025 (±0.0002) to 0.139 (±0.003), from 0.029 (±0.003) to 0.289 (±0.004), and from 0.025 (±0.003) to 0.545 (±0.034) at Wean, MidTest, and OffTest, respectively. Several SNP were significantly associated with taxa at the three time points. These SNP were located in genomic regions containing a total of 68 genes. This study provides new evidence linking gut microbiome composition with growth and carcass traits in swine, while also identifying putative host genetic markers associated with significant differences in the abundance of several prevalent microbiome features.}, number={1}, journal={SCIENTIFIC REPORTS}, author={Bergamaschi, Matteo and Maltecca, Christian and Schillebeeckx, Constantino and McNulty, Nathan P. and Schwab, Clint and Shull, Caleb and Fix, Justin and Tiezzi, Francesco}, year={2020}, month={Jun} }
 @article{makanjuola_maltecca_miglior_schenkel_baes_2020, title={Inbreeding depression due to different age classes of inbreeding on production and fertility traits in Canadian Holsteins}, volume={103}, number={Supplement 1}, journal={Journal of Dairy Science}, author={Makanjuola, B.O. and Maltecca, C. and Miglior, F. and Schenkel, F.S. and Baes, C.F.}, year={2020}, pages={115–115} }
 @article{cecchinato_toledo-alvarado_pegolo_rossoni_santus_maltecca_bittante_tiezzi_2020, title={Integration of Wet-Lab Measures, Milk Infrared Spectra, and Genomics to Improve Difficult-to-Measure Traits in Dairy Cattle Populations}, volume={11}, ISSN={["1664-8021"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85092451701&partnerID=MN8TOARS}, DOI={10.3389/fgene.2020.563393}, abstractNote={The objective of this study was to evaluate the contribution of Fourier-transformed infrared spectroscopy (FTIR) data for dairy cattle breeding through two different approaches: (i) estimating the genetic parameters for 30 measured milk traits and their FTIR predictions and investigating the additive genetic correlation between them and (ii) evaluating the effectiveness of FTIR-derived phenotyping to replicate a candidate bull's progeny testing or breeding value prediction at birth. Records were available from 1,123 cows phenotyped using gold standard laboratory methodologies (LAB data). This included phenotypes related to fine milk composition and milk technological characteristics, milk acidity, and milk protein fractions. The dataset used to generate FTIR predictions comprised 729,202 test-day records from 51,059 Brown Swiss cows (FIELD data). A first approach consisted of estimating genetic parameters for phenotypes available from LAB and FIELD datasets. To do so, a set of bivariate animal models were run, and genetic correlations between LAB and FIELD phenotypes were estimated using FIELD information obtained at the population level. Heritability estimates were generally higher for FIELD predictions than for the corresponding LAB measures. The additive genetic correlations (r a ) between LAB and FIELD phenotypes had different magnitudes across traits but were generally strong. Overall, these results demonstrated the potential of using FIELD information as indicator traits for the indirect genetic improvement of LAB measures. In the second approach, we included genotype information for 1,011 cows from the LAB dataset, 1,493 cows from the FIELD dataset, 181 sires with daughters in both LAB and FIELD datasets, and 540 sires with daughters in the FIELD dataset only. Predictions were obtained using the single-step GBLUP method. A four fold cross-validation was used to assess the predictive ability of the different models, assessed as the ability to predict masked LAB records from daughters of progeny testing bulls. The correlation between observed and predicted LAB measures in validation was averaged over the four training-validation sets. Different sets of phenotypic information were used sequentially in cross-validation schemes: (i) LAB cows from the training set; (ii) FIELD cows from the training set; and (iii) FIELD cows from the validation set. Models that included FIELD records showed an improvement for the majority of traits. This study suggests that breeding programs for difficult-to-measure traits could be implemented using FTIR information. While these programs should use progeny testing, acceptable values of accuracy can be achieved also for bulls without phenotyped progeny. Robust calibration equations are, deemed as essential.}, journal={FRONTIERS IN GENETICS}, author={Cecchinato, Alessio and Toledo-Alvarado, Hugo and Pegolo, Sara and Rossoni, Attilio and Santus, Enrico and Maltecca, Christian and Bittante, Giovanni and Tiezzi, Francesco}, year={2020}, month={Sep} }
 @article{morgante_huang_sorensen_maltecca_mackay_2020, title={Leveraging Multiple Layers of Data To Predict Drosophila Complex Traits}, volume={10}, ISSN={["2160-1836"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85097210372&partnerID=MN8TOARS}, DOI={10.1534/g3.120.401847}, abstractNote={Abstract The ability to accurately predict complex trait phenotypes from genetic and genomic data are critical for the implementation of personalized medicine and precision agriculture; however, prediction accuracy for most complex traits is currently low. Here, we used data on whole genome sequences, deep RNA sequencing, and high quality phenotypes for three quantitative traits in the ∼200 inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) to compare the prediction accuracies of gene expression and genotypes for three complex traits. We found that expression levels (r = 0.28 and 0.38, for females and males, respectively) provided higher prediction accuracy than genotypes (r = 0.07 and 0.15, for females and males, respectively) for starvation resistance, similar prediction accuracy for chill coma recovery (null for both models and sexes), and lower prediction accuracy for startle response (r = 0.15 and 0.14 for female and male genotypes, respectively; and r = 0.12 and 0.11, for females and male transcripts, respectively). Models including both genotype and expression levels did not outperform the best single component model. However, accuracy increased considerably for all the three traits when we included gene ontology (GO) category as an additional layer of information for both genomic variants and transcripts. We found strongly predictive GO terms for each of the three traits, some of which had a clear plausible biological interpretation. For example, for starvation resistance in females, GO:0033500 (r = 0.39 for transcripts) and GO:0032870 (r = 0.40 for transcripts), have been implicated in carbohydrate homeostasis and cellular response to hormone stimulus (including the insulin receptor signaling pathway), respectively. In summary, this study shows that integrating different sources of information improved prediction accuracy and helped elucidate the genetic architecture of three Drosophila complex phenotypes.}, number={12}, journal={G3-GENES GENOMES GENETICS}, author={Morgante, Fabio and Huang, Wen and Sorensen, Peter and Maltecca, Christian and Mackay, Trudy F. C.}, year={2020}, month={Dec}, pages={4599–4613} }
 @article{bergamaschi_tiezzi_howard_huang_gray_schillebeeckx_mcnulty_maltecca_2020, title={Microbiome composition differences among breeds impact feed efficiency in swine}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85133737605&partnerID=MN8TOARS}, DOI={10.21203/rs.2.22531}, journal={ResearchSquare}, author={Bergamaschi, M. and Tiezzi, F. and Howard, J. and Huang, Y.J. and Gray, K.A. and Schillebeeckx, C. and McNulty, N.P. and Maltecca, C.}, year={2020} }
 @article{khanal_maltecca_schwab_fix_bergamaschi_tiezzi_2020, title={Modeling host-microbiome interactions for the prediction of meat quality and carcass composition traits in swine}, volume={52}, ISBN={1297-9686}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85088852317&partnerID=MN8TOARS}, DOI={10.1186/s12711-020-00561-7}, abstractNote={Abstract Background The objectives of this study were to evaluate genomic and microbial predictions of phenotypes for meat quality and carcass traits in swine, and to evaluate the contribution of host-microbiome interactions to the prediction. Data were collected from Duroc-sired three-way crossbred individuals (n = 1123) that were genotyped with a 60 k SNP chip. Phenotypic information and fecal 16S rRNA microbial sequences at three stages of growth (Wean, Mid-test, and Off-test) were available for all these individuals. We used fourfold cross-validation with animals grouped based on sire relatedness. Five models with three sets of predictors (full, informatively reduced, and randomly reduced) were evaluated. ‘Full’ included information from all genetic markers and all operational taxonomic units (OTU), while ‘informatively reduced’ and ‘randomly reduced’ represented a reduced number of markers and OTU based on significance preselection and random sampling, respectively. The baseline model included the fixed effects of dam line, sex and contemporary group and the random effect of pen. The other four models were constructed by including only genomic information, only microbiome information, both genomic and microbiome information, and microbiome and genomic information and their interaction. Results Inclusion of microbiome information increased predictive ability of phenotype for most traits, in particular when microbiome information collected at a later growth stage was used. Inclusion of microbiome information resulted in higher accuracies and lower mean squared errors for fat-related traits (fat depth, belly weight, intramuscular fat and subjective marbling), objective color measures (Minolta a*, Minolta b* and Minolta L*) and carcass daily gain. Informative selection of markers increased predictive ability but decreasing the number of informatively reduced OTU did not improve model performance. The proportion of variation explained by the host-genome-by-microbiome interaction was highest for fat depth (~ 20% at Mid-test and Off-test) and shearing force (~ 20% consistently at Wean, Mid-test and Off-test), although the inclusion of the interaction term did not increase the accuracy of predictions significantly. Conclusions This study provides novel insight on the use of microbiome information for the phenotypic prediction of meat quality and carcass traits in swine. Inclusion of microbiome information in the model improved predictive ability of phenotypes for fat deposition and color traits whereas including a genome-by-microbiome term did not improve prediction accuracy significantly.}, number={1}, journal={GENETICS SELECTION EVOLUTION}, author={Khanal, Piush and Maltecca, Christian and Schwab, Clint and Fix, Justin and Bergamaschi, Matteo and Tiezzi, Francesco}, year={2020} }
 @article{maltecca_tiezzi_cole_baes_2020, title={Symposium review: Exploiting homozygosity in the era of genomics—Selection, inbreeding, and mating programs}, volume={103}, url={https://doi.org/10.3168/jds.2019-17846}, DOI={10.3168/jds.2019-17846}, abstractNote={The advent of genomic selection paved the way for an unprecedented acceleration in genetic progress. The increased ability to select superior individuals has been coupled with a drastic reduction in the generation interval for most dairy populations, representing both an opportunity and a challenge. Homozygosity is now rapidly accumulating in dairy populations. Currently, inbreeding depression is managed mostly by culling at the farm level and by controlling the overall accumulation of homozygosity at the population level. A better understanding of how homozygosity and recessive load are related will guarantee continued genetic improvement while curtailing the accumulation of harmful recessives and maintaining enough genetic variability to ensure the possibility of selection in the face of changing environmental conditions. In this review, we present a snapshot of the current dairy selection structure as it relates to response to selection and accumulation of homozygosity, briefly outline the main approaches currently used to manage inbreeding and overall variability, and present some approaches that can be used in the short term to control accumulation of harmful recessives while maintaining sustained selection pressure.}, number={6}, journal={Journal of Dairy Science}, publisher={American Dairy Science Association}, author={Maltecca, C. and Tiezzi, F. and Cole, J.B. and Baes, C.}, year={2020}, month={Jun}, pages={5302–5313} }
 @article{cole_eaglen_maltecca_mulder_pryce_2020, title={The future of phenomics in dairy cattle breeding}, volume={10}, ISSN={["2160-6064"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85085321884&partnerID=MN8TOARS}, DOI={10.1093/af/vfaa007}, abstractNote={Increasingly complex dairy cattle production systems require that all aspects of animal performance are measured across individuals' lifetimes. Selection emphasis is shifting away from traits related to animal productivity toward those related to effcient resource utilization and improved health and welfare/ resilience. The goal of phenomics is to provide information for making decisions related to on-farm management, as well as genetic improvement.}, number={2}, journal={ANIMAL FRONTIERS}, author={Cole, John B. and Eaglen, Sophie A. E. and Maltecca, Christian and Mulder, Han A. and Pryce, Jennie E.}, year={2020}, month={Apr}, pages={37–44} }
 @article{maltecca_bergamaschi_tiezzi_2020, title={The interaction between microbiome and pig efficiency: A review}, volume={137}, url={https://doi.org/10.1111/jbg.12443}, DOI={10.1111/jbg.12443}, abstractNote={Abstract The existence of genetic control over the abundance of particular taxa and the link of these to energy balance and growth has been documented in model organisms and humans as well as several livestock species. Preliminary evidence of the same mechanisms is currently under investigation in pigs. Future research should expand these results and elicit the extent of genetic control of the gut microbiome population in swine and its relationship with growth efficiency. The quest for a more efficient pig at the interface between the host and its metagenome rests on the central hypothesis that the gut microbiome is an essential component of the variability of growth in all living organisms. Swine do not escape this general rule, and the identification of the significance of the interaction between host and its gut microbiota in the growth process could be a game‐changer in the achievement of sustainable and efficient lean meat production. Standard sampling protocols, sequencing techniques, bioinformatic pipelines and methods of analysis will be paramount for the portability of results across experiments and populations. Likewise, characterizing and accounting for temporal and spatial variability will be a necessary step if microbiome is to be utilized routinely as an aid to selection.}, number={1}, journal={Journal of Animal Breeding and Genetics}, publisher={Wiley}, author={Maltecca, Christian and Bergamaschi, Matteo and Tiezzi, Francesco}, year={2020}, month={Jan}, pages={4–13} }
 @article{maltecca_baes_tiezzi_2020, title={The use of genomic information to improve selection response while controlling inbreeding in dairy cattle breeding programs}, volume={72}, ISBN={["978-1-78676-296-2"]}, ISSN={["2059-6944"]}, DOI={10.19103/AS.2019.0058.05}, journal={ADVANCES IN BREEDING OF DAIRY CATTLE}, author={Maltecca, C. and Baes, C. and Tiezzi, F.}, year={2020}, pages={71–96} }
 @article{he_maltecca_tiezzi_soto_flowers_2020, title={Transcriptome analysis identifies genes and co-expression networks underlying heat tolerance in pigs}, volume={21}, ISSN={["1471-2156"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85083872520&partnerID=MN8TOARS}, DOI={10.1186/s12863-020-00852-4}, abstractNote={Abstract Background Heat stress adversely affects pig growth and reproduction performance by reducing feed intake, weight gain, farrowing rate, and litter size. Heat tolerance is an important characteristic in pigs, allowing them to mitigate the negative effects of heat stress on their physiological activities. Yet, genetic variation and signaling pathways associated with the biological processes of heat-tolerant pigs are currently not fully understood. This study examined differentially expressed genes and constructed gene co-expression networks on mRNAs of pigs under different heat-stress conditions using whole transcriptomic RNA-seq analyses. Semen parameters, including total sperm number per ejaculate, motility, normal morphology rate, droplets, and rejected ejaculate rate, were measured weekly on 12 boars for two time periods: thermoneutral (January to May), and heat stress (July to October). Boars were classified into heat-tolerant ( n = 6) and heat-susceptible (n = 6) groups based on the variation of their ejaculate parameters across the two periods. RNA was isolated from the blood samples collected from the thermoneutral and heat stress periods for gene expression analysis. Results Under heat stress, a total of 66 differentially expressed genes (25 down-regulated, 41 up-regulated) were identified in heat-tolerant pigs compared to themselves during the thermoneutral period. A total of 1041 differentially expressed genes (282 down-regulated, 759 up-regulated) were identified in the comparison between heat-tolerant pigs and heat-susceptible pigs under heat stress. Weighted gene co-expression network analysis detected 4 and 7 modules with genes highly associated (r > 0.50, p < 0.05) with semen quality parameters in heat-tolerant and heat-susceptible pigs under the effects of heat stress, respectively. Conclusion This study utilized the sensitivity of semen to heat stress to discriminate the heat-tolerance ability of pigs. The gene expression profiles under the thermoneutral and heat stress conditions were documented in heat-tolerant and heat-susceptible boars. Findings contribute to the understanding of genes and biological mechanisms related to heat stress response in pigs and provide potential biomarkers for future investigations on the reproductive performance of pigs.}, number={1}, journal={BMC GENETICS}, author={He, Yuqing and Maltecca, Christian and Tiezzi, Francesco and Soto, Emmanuel Lozada and Flowers, William L.}, year={2020}, month={Apr} }
 @article{tiezzi_schwab_fix_maltecca_2019, title={212 Genomic prediction of carcass average daily gain, fat and loin depth in three-way crossbred pigs including information collected on purebreds}, volume={97}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skz258.079}, DOI={10.1093/jas/skz258.079}, abstractNote={Abstract The purpose of this study was to predict three-way crossbred performance for carcass traits using different crossbred/purebred reference populations. Carcass measures (average daily gain, back-fat and loin depths) were collected in 4,893 three-way-cross individuals (CB individuals, 1,252 being genotyped). Live measures of body weight and tissue deposition were collected on 3,050 purebred Duroc individuals (PB individuals, 941 being genotyped), paternal-half-sibs (PHS) of the CB individuals. Models’ predictive performance was tested via 4-fold cross-validation. The basic model included CB phenotypes from the training set without inclusion of genomic information (i.e. pedigree BLUP). We also sequentially included: 1) CB genotypes; 2) PB phenotypes and genotypes for the training families (PBt); 3) PB phenotypes and genotypes for the validation families (PBv). Variance components (heritabilities and genetic correlations between CB and PB traits) were not estimated but fixed at different values within a plausible interval, the combination of such parameters that gave the best predictive ability was considered for that model. Results reported pedigree prediction of CB traits to show about 0.25 accuracy (correlation between breeding value and adjusted phenotype) for the three traits. The inclusion of CB genotypes was beneficial, with an increase ranging from 25 to 50% (depending on the trait) compared to pedigree prediction. When PBt genotypes and phenotypes were included, prediction accuracy dropped to almost null accuracy. When PBv genotypes and phenotypes were included, predictive performance was better than models that included CB information only. Results suggest that PB information can improve selection accuracy for CB traits, with the condition PB are PHS of the CB in validation. Otherwise, inclusion of PB information from the training set can be detrimental. CB genotypes, on the other hand, always improve prediction accuracy. We can conclude that reference populations aimed at improving CB performance should include phenotypes and genotypes from these individuals.}, number={Supplement_3}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Tiezzi, Francesco and Schwab, Clint and Fix, Justin and Maltecca, Christian}, year={2019}, month={Dec}, pages={40–40} }
 @article{bergamaschi_maltecca_schwab_fix_tiezzi_2019, title={213 Genomic selection of carcass quality traits in crossbred pigs using a reference population}, volume={97}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skz258.082}, DOI={10.1093/jas/skz258.082}, abstractNote={Abstract The objective of this work was to evaluate the predictive ability of different models applied to carcass traits in crossbred pigs. The pigs were divided in 2 finishing flows: A=36,110 and B=95,041 animals, and were progeny of 386 sires (almost entirely genotyped with the 60k SNP chip). In flow A, individuals were housed into single-sire single-gender pens, and split-marketing on a pen basis was applied. In flow B, individuals were kept in standard commercial conditions and split-marketing on an individuals basis was applied. A dataset containing individual records of three carcass traits: back-fat (BF), loin depth (LD), and carcass daily gain (CACG) was used. Data from flow A were divided into training and validation sets on the basis of contemporary groups (8 in training and 1 in testing). Variance components and solutions were obtained using the BLUPF90 suite of programs. Models included fixed effects (dam line, sow parity, sex, cross fostering, and contemporary group) and random effects (additive genetic, batch, litter, and residual). Models tested were univariate vs multivariate and pedigree vs single-step. The addition of flow B records to the training set was evaluated, by including or excluding these records. Heritabilities were 0.68±0.023 for BF, 0.47±0.018 for LD, and 0.55±0.023 for CACG. CACG gain was correlated with BF (0.43±0.029) and LD (0.39±0.03). Low genetic correlation was found between BF and LD (0.17±0.034). Prediction accuracies were 0.39±0.05, 0.17±0.06, and 0.13±0.03 for BF, LD, and CACG respectively. The mean accuracy of BF, LD, and CG increased (~6%) when records from flow B were included in the training set, whereas the increase of accuracy between models (univariate vs multivariate) was not significant. The inclusion of sire genotypes did not improve prediction accuracy significantly. Based on these results, the prediction of carcass quality traits in crossbred pigs is possible.}, number={Supplement_3}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Bergamaschi, Matteo and Maltecca, Christian and Schwab, Clint and Fix, Justin and Tiezzi, Francesco}, year={2019}, month={Dec}, pages={41–41} }
 @article{khanal_maltecca_schwab_fix_tiezzi_2019, title={214 Correlation among host gut microbiome and their relationship with meat quality and carcass composition traits of swine}, volume={97}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skz258.087}, DOI={10.1093/jas/skz258.087}, abstractNote={Abstract Study on correlation among host gut microbiome and their relationship with meat quality and carcass composition traits remains limited. The objectives of this study were 1) to estimate the microbial correlation between meat quality and carcass traits; and 2) to estimate the genetic correlation between microbial alpha diversity, and meat quality and carcass traits in commercial swine population. Data were collected from Duroc sired three-way cross individuals (n = 1,123) genotyped with 60K SNP chips. Fecal 16S microbial sequences for all individuals were obtained at three different stages: weaning (WEAN: 18.64 ± 1.09 days); week 15 (W_15: 118.2 ± 1.18 days); and off test (OT: 196.4 ± 7.80 days). Alpha diversity was measured at each stage [WEAN (alpha_w), W_15 (alpha_15) and OT (alpha_off)] using the Shannon index, which was computed as: ∑ ni=1piln(pi) where pi was the proportional abundance of ith operational taxonomic unit. Microbial correlations were estimated using multi-trait model, which included fixed effects of dam line, contemporary group and sex, as well as random effects of pen, additive genetic and microbiome. Bivariate analyses were conducted between different traits and alpha_w, alpha_15 and alpha_off with the same fixed effects and random pen and additive genetic effect. Analyses were conducted in ASREML v.4. Microbial correlations ranged from -0.93 ± 0.11 between firmness and slice shear force to 0.97 ± 0.02 between carcass average daily gain (CADG) and loin weight. For meat quality traits, correlations were weak, except for alpha_15 with Minolta a* (-0.45±0.19). Alpha_15 showed weak correlations except with CADG (-0.43±0.19). All correlations between alpha_ot and growth, carcass and meat quality traits were weak. These results may establish a newer approach of genetic evaluation process by utilizing gut microbiome information.}, number={Supplement_3}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Khanal, Piush and Maltecca, Christian and Schwab, Clint and Fix, Justin and Tiezzi, Francesco}, year={2019}, month={Dec}, pages={44–44} }
 @article{khanal_maltecca_schwab_fix_tiezzi_2019, title={216 Contribution of host gut microbiome in prediction of meat quality and carcass composition traits in swine}, volume={97}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skz258.088}, DOI={10.1093/jas/skz258.088}, abstractNote={Abstract The objective of this study was to assess the improvement in predictive ability (PA) for meat quality and carcass composition traits by including the effect of host gut microbiome. Data were collected from Duroc sired three-way crossbred individuals (n = 1,123) genotyped from 60k SNP chips. Fecal 16S microbial sequences for all individuals were obtained at three different stages: weaning (WEAN: 18.64±1.09 days); week 15 (W_15: 118.2±1.18 days); and off test (OT: 196.4±7.80 days). A 4-fold cross validation was used, with animals grouped based on sire relatedness. Analysis was conducted in “BGLR” package in R. The first model included the fixed effect of dam line, contemporary group, and sex, as well as random pen and additive genetic effect (with genomic relationship matrix). The second model included same fixed and random effects with the addition of the microbiome effect (with microbiome relationship matrix). With the inclusion of microbiome, the PA increased for majority of traits. For carcass traits, the increase in PA was greatest for belly weight which changed from 0.22±0.06 to 0.32±0.06 at OT and lowest for loin depth and ham weight which changed from 0.14±0.06 to 0.15 ±0.06 and 0.12±0.02 to 0.13 ±0.03 at WEAN respectively. For meat quality traits, the increase in PA was greatest for subjective firmness score which changed from 0.16±0.04 to 0.19±0.04 at OT and lowest for marbling score and intramuscular fat which changed from 0.27±0.09 to 0.28±0.08 and 0.39±0.05 to 0.40±0.05 at WEAN respectively. Higher predictive ability was observed at OT rather than at WEAN and W_15 microbiome information. The results may lead to a newer approach to the genetic evaluation program of swine.}, number={Supplement_3}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Khanal, Piush and Maltecca, Christian and Schwab, Clint and Fix, Justin and Tiezzi, Francesco}, year={2019}, month={Dec}, pages={44–45} }
 @article{he_jacobi_maltecca_odle_2019, title={292 Differential gene expression analysis for piglets supplied dietary prebiotics and arachidonic acid for gastrointestinal disturbances}, volume={97}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skz258.253}, DOI={10.1093/jas/skz258.253}, abstractNote={Abstract Gastrointestinal (GI) disturbances cause significant economic losses in the swine industry. Dietary-based approaches have been well applied to control the inflammation and optimize microbial colonization of the GI tract. This study aimed to screen differentially expressed (DE) RNAs in piglets supplied with arachidonic acid (ARA) or prebiotics in an acute dextran sodium sulfate (DSS) colitis model. Suckling pigs (n = 48) from 6 litters were randomly assigned into 4 diet groups: 0.5% ARA, 0.5% ARA + PRE (4g/L galactooligosaccharide +4 g/L polydextrose), 2.5% ARA, and 2.5% ARA + PRE. On day 17–21, half of the pigs (n = 24) were treated with 0.625 g DSS/kg BW to induce colitis. RNA samples (n = 48) were isolated from the mucosal layer of GI tract on day 22 for the gene expression analysis via RNAseq. The GLIMMIX procedure of SAS (v.9.4) was used to fit the statistical models to gene counts, were the main effects and interactions of PRE, ARA, and DSS were fit in the model as fixed effects and litter as random. Genes with FDR < 0.05 were mapped to the KEGG pathway. A total of 133 DE genes (88 up-regulated, 45 down-regulated) were identified in pigs with colitis compared to healthy ones. PRE and ARA affected gene expression differently but with no interaction effect. PRE supplement decreased the total DE genes from 83 (59 up-regulated, 24 down-regulated) to 33 (16 up-regulated and 17 down-regulated) in pigs with colitis compared with healthy ones. A total of 37 DE genes (21 up-regulated, 16 down-regulated), and 49 DE genes (32 up-regulated, 17 down-regulated) were identified in pigs with colitis compared to healthy ones supplied with 0.5% and 2.5% ARA, respectively. DE genes identified in this study are involved in various signaling pathways with supplied prebiotics significantly changing the gene expression in pigs afflicted with colitis.}, number={Supplement_3}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={He, Yuqing and Jacobi, Sheila and Maltecca, Christian and Odle, Jack}, year={2019}, month={Dec}, pages={122–123} }
 @article{he_maltecca_tiezzi_flowers_2019, title={381 Investigation of heat stress on differential gene expression in tolerant and susceptible pigs}, volume={97}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/skz258.294}, DOI={10.1093/jas/skz258.294}, abstractNote={Abstract Heat stress adversely affects pig growth and reproduction performance by reducing feed intake, body weight gain, farrowing rate, and litter size. Heat tolerance is an important trait in pigs, allowing them to mitigate the negative effects of heat stress on their physiological activities. Yet, genetic variation and signaling pathways associated with the biological processes of pigs tolerant to heat stress are currently not fully understood. This study aimed at identifying differentially expressed (DE) mRNAs of pigs under different heat-stress environments using whole transcriptomic RNA-seq analyses. RNA was isolated from blood samples of boars (n = 12) collected at two time points (late winter and middle summer) and labeled as pre-stress when subjected to no heat-stress, and stress when subjected to heat stress for 2 months. Semen parameters, including sperm count, motility, normal morphology, droplets, and rejected ejaculate rate, were measured for boar classification into either tolerant or susceptible to the heat stress. Genes displayed different expression levels between susceptible and tolerant pigs under pre-stress, and stress period respectively. A total of 692 DE genes (654 down-regulated, 38 up-regulated) were found in susceptible pigs compared to tolerant pigs during the pre-stress period. A total of 724 DE genes (622 down-regulated, 62 up-regulated) were found in susceptible pigs compared to tolerant pigs during the heat stress period. Heat stress showed greater effects in significant transcript expression (FDR < 0.05) among susceptible pigs than tolerant pigs with 88 DE genes (51 down-regulated, 37 up-regulated) and 5 DE genes (1 down-regulated, 4 up-regulated), respectively. This study contributed to the expression profiles of transcripts and the exploration of novel genes in pigs responding to heat stress.}, number={Supplement_3}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={He, Yuqing and Maltecca, Christian and Tiezzi, Francesco and Flowers, Billy}, year={2019}, month={Dec}, pages={144–144} }
 @inproceedings{cockrum_maltecca_2019, title={Cattle/Swine}, booktitle={Plant and Animal Genome XXVII Conference}, publisher={PAG}, author={Cockrum, R. and Maltecca, C.}, year={2019} }
 @article{makanjuola_miglior_sargolzaei_maltecca_schenkel_baes_2019, title={Effect of genomic selection on rate of inbreeding and effective population size in North American Holstein and Jersey dairy cattle populations}, volume={102}, number={Supplement 1}, journal={Journal of Dairy Science}, publisher={ELSEVIER SCIENCE INC STE}, author={Makanjuola, B. and Miglior, F. and Sargolzaei, M. and Maltecca, C. and Schenkel, F. and Baes, C.}, year={2019}, pages={291–291} }
 @article{maltecca_baes_tiezza_2019, title={Exploiting homozygosity in the era of genomics-Runs of homozygosity, inbreeding, and genomic mating programs}, volume={102}, number={Supplement 1}, journal={Journal of Dairy Science}, publisher={ELSEVIER SCIENCE INC STE}, author={Maltecca, C. and Baes, C. and Tiezza, F.}, year={2019}, pages={98–99} }
 @article{freebern_santos_fang_jiang_parker gaddis_liu_vanraden_maltecca_cole_ma_2019, title={GWAS and fine-mapping of livability and six disease traits in holstein cattle}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85094380729&partnerID=MN8TOARS}, DOI={10.1101/775098}, abstractNote={Abstract Background Health traits are of significant economic importance to the dairy industry due to their effects on milk production and associated treatment costs. Genome-wide association studies (GWAS) provide a means to identify associated genomic variants and thus reveal insights into the genetic architecture of complex traits and diseases. The objective of this study is to investigate the genetic basis of seven health traits in dairy cattle and to identify potential candidate genes associated with cattle health using GWAS, fine mapping, and analyses of multitissue transcriptome data. Results We studied cow livability and six direct disease traits, mastitis, ketosis, hypocalcemia, displaced abomasum, metritis, and retained placenta, using de-regressed breeding values and more than three million imputed DNA sequence variants. After data edits and filtering on reliability, phenotypes for 11,880 to 24,699 Holstein bulls were included in the analyses of the seven traits. GWAS was performed using a mixed-model association test, and a Bayesian fine-mapping procedure was conducted to calculate a posterior probability of causality to each variant and gene in the candidate regions. The GWAS results detected a total of eight genome-wide significant associations for three traits, cow livability, ketosis, and hypocalcemia, including the bovine MHC region associated with livability. Our fine-mapping of associated regions reported 20 candidate genes with the highest posterior probabilities of causality for cattle health. Combined with transcriptome data across multiple tissues in cattle, we further exploited these candidate genes to identify specific expression patterns in disease-related tissues and relevant biological explanations such as the expression of GC in the liver and association with mastitis as well as the CCDC88C expression in CD8 cells and association with cow livability. Conclusions Collectively, our analyses report six significant associations and 20 candidate genes of cattle health. With the integration of multi-tissue transcriptome data, our results provide useful information for future functional studies and better understanding of the biological relationship between genetics and disease susceptibility in cattle.}, journal={bioRxiv}, author={Freebern, E. and Santos, D.J.A. and Fang, L. and Jiang, J. and Parker Gaddis, K.L. and Liu, G.E. and Vanraden, P.M. and Maltecca, C. and Cole, J.B. and Ma, L.}, year={2019} }
 @article{khanal_maltecca_schwab_gray_tiezzi_2019, title={Genetic parameters of meat quality, carcass composition, and growth traits in commercial swine}, volume={97}, ISSN={["1525-3163"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85072057293&partnerID=MN8TOARS}, DOI={10.1093/jas/skz247}, abstractNote={Abstract Swine industry breeding goals are mostly directed towards meat quality and carcass traits due to their high economic value. Yet, studies on meat quality and carcass traits including both phenotypic and genotypic information remain limited, particularly in commercial crossbred swine. The objectives of this study were to estimate the heritabilities for different carcass composition traits and meat quality traits and to estimate the genetic and phenotypic correlations between meat quality, carcass composition, and growth traits in 2 large commercial swine populations: The Maschhoffs LLC (TML) and Smithfield Premium Genetics (SPG), using genotypes and phenotypes data. The TML data set consists of 1,254 crossbred pigs genotyped with 60K SNP chip and phenotyped for meat quality, carcass composition, and growth traits. The SPG population included over 35,000 crossbred pigs phenotyped for meat quality, carcass composition, and growth traits. For TML data sets, the model included fixed effects of dam line, contemporary group (CG), gender, as well as random additive genetic effect and pen nested within CG. For the SPG data set, fixed effects included parity, gender, and CG, as well as random additive genetic effect and harvest group. Analyses were conducted using BLUPF90 suite of programs. Univariate and bivariate analyses were implemented to estimate heritabilities and correlations among traits. Primal yield traits were uniquely created in this study. Heritabilities [high posterior density interval] of meat quality traits ranged from 0.08 [0.03, 0.16] for pH and 0.08 [0.03, 0.1] for Minolta b* to 0.27 [0.22, 0.32] for marbling score, except intramuscular fat with the highest estimate of 0.52 [0.40, 0.62]. Heritabilities of primal yield traits were higher than that of primal weight traits and ranged from 0.17 [0.13, 0.25] for butt yield to 0.45 [0.36, 0.55] for ham yield. The genetic correlations of meat quality and carcass composition traits with growth traits ranged from moderate to high in both directions. High genetic correlations were observed for male and female for all traits except pH. The genetic parameter estimates of this study indicate that a multitrait approach should be considered for selection programs aimed at meat quality and carcass composition in commercial swine populations.}, number={9}, journal={JOURNAL OF ANIMAL SCIENCE}, publisher={Oxford University Press US}, author={Khanal, Piush and Maltecca, Christian and Schwab, Clint and Gray, Kent and Tiezzi, Francesco}, year={2019}, month={Sep}, pages={3669–3683} }
 @article{he_maltecca_tiezzi_canovas_bhattarai_mckay_2019, title={Investigation of genetic variation in global DNA methylation in bull semen and its relationship with semen quality and fertility parameters}, volume={102}, number={Supplement 1}, journal={Journal of Diary Science}, author={He, Y. and Maltecca, C. and Tiezzi, F. and Canovas, A. and Bhattarai, S. and Mckay, S.}, year={2019}, pages={293–293} }
 @article{morgante_huang_s?rensen_maltecca_mackay_2019, title={Leveraging multiple layers of data to predict Drosophila complex traits}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85095519460&partnerID=MN8TOARS}, DOI={10.1101/824896}, abstractNote={Abstract An important challenge in genetics is to be able to predict complex traits accurately. Despite recent advances, prediction accuracy for most complex traits remains low. Here, we used the Drosophila Genetic Reference Panel (DGRP), a collection of 200 lines with whole-genome sequences and deep RNA sequencing data, to evaluate the usefulness of using high-quality gene expression levels compared to relying on genotypes for predicting three complex traits. We found that expression levels provided higher accuracy than genotypes for starvation resistance, similar accuracy for chill coma recovery, and lower accuracy for startle response. Models including both genotype and expressions levels did not outperform the best single component model. However, accuracy increased considerably for all the three traits when we included another layer of information, i.e., gene ontology (GO). We found that a limited number of GO terms, some of which had a clear biological interpretation, were strongly predictive of the traits. In summary, this study shows that integrating different sources of information can improve prediction accuracy, especially when large samples are not available.}, journal={bioRxiv}, author={Morgante, F. and Huang, W. and S?rensen, P. and Maltecca, C. and Mackay, T.F.C.}, year={2019} }
 @inproceedings{maltecca_baes_tiezzi_2019, title={Managing Homozygosity and Diversity in Livestock}, booktitle={Plant and Animal Genome XXVII Conference}, publisher={PAG}, author={Maltecca, Christian and Baes, C.F. and Tiezzi, F.}, year={2019} }
 @article{khanal_maltecca_schwab_fix_tiezzi_2019, title={Microbiability of meat quality and carcass composition traits in swine}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85095623455&partnerID=MN8TOARS}, DOI={10.1101/833731}, abstractNote={Abstract The impact of gut microbiome composition was investigated at different stages of production (Wean, Mid-test, and Off-test) on meat quality and carcass composition traits of 1,123 three-way-crossbred pigs. Data were analyzed using linear mixed models which included the fixed effects of dam line, contemporary group and gender as well as the random effects of pen, animal and microbiome information at different stages. The contribution of the microbiome to all traits was prominent although it varied over time, increasing from weaning to Off-test for most traits. Microbiability estimates of carcass composition traits were greater compared to meat quality traits. Adding microbiome information did not affect the estimates of genomic heritability of meat quality traits but affected the estimates of carcass composition traits. High microbial correlations were found among different traits, particularly with traits related to fat deposition with decrease in genomic correlation up to 20% for loin weight and belly weight. Decrease in genomic heritabilities and genomic correlations with the inclusion of microbiome information suggested that genomic correlation was partially contributed by genetic similarity of microbiome composition.}, journal={bioRxiv}, author={Khanal, P. and Maltecca, C. and Schwab, C. and Fix, J. and Tiezzi, F.}, year={2019} }
 @article{maltecca_lu_schillebeeckx_mcnulty_schwab_shull_tiezzi_2019, title={Predicting Growth and Carcass Traits in Swine Using Microbiome Data and Machine Learning Algorithms}, volume={9}, ISSN={2045-2322}, url={http://dx.doi.org/10.1038/S41598-019-43031-X}, DOI={10.1038/s41598-019-43031-x}, abstractNote={Abstract In this paper, we evaluated the power of microbiome measures taken at three time points over the growth test period (weaning, 15 and 22 weeks) to foretell growth and carcass traits in 1039 individuals of a line of crossbred pigs. We measured prediction accuracy as the correlation between actual and predicted phenotypes in a five-fold cross-validation setting. Phenotypic traits measured included live weight measures and carcass composition obtained during the trial as well as at slaughter. We employed a null model excluding microbiome information as a baseline to assess the increase in prediction accuracy stemming from the inclusion of operational taxonomic units (OTU) as predictors. We further contrasted performance of models from the Bayesian alphabet (Bayesian Lasso) as well machine learning approaches (Random Forest and Gradient Boosting) and semi-parametric kernel models (Reproducing Kernel Hilbert space). In most cases, prediction accuracy increased significantly with the inclusion of microbiome data. Accuracy was more substantial with the inclusion of microbiome information taken at weeks 15 and 22, with values ranging from approximately 0.30 for loin traits to more than 0.50 for back fat. Conversely, microbiome composition at weaning resulted in most cases in marginal gains of prediction accuracy, suggesting that later measures might be more useful to include in predictive models. Model choice affected predictions marginally with no clear winner for any model/trait/time point. We, therefore, suggest average prediction across models as a robust strategy in fitting microbiome information. In conclusion, microbiome composition can effectively be used as a predictor of growth and composition traits, particularly for fatness traits. The inclusion of OTU predictors could potentially be used to promote fast growth of individuals while limiting fat accumulation. Early microbiome measures might not be good predictors of growth and OTU information might be best collected at later life stages. Future research should focus on the inclusion of both microbiome as well as host genome information in predictions, as well as the interaction between the two. Furthermore, the influence of the microbiome on feed efficiency as well as carcass and meat quality should be investigated.}, number={1}, journal={Scientific Reports}, publisher={Springer Nature}, author={Maltecca, Christian and Lu, Duc and Schillebeeckx, Constantino and McNulty, Nathan P. and Schwab, Clint and Shull, Caleb and Tiezzi, Francesco}, year={2019}, month={Apr} }
 @inproceedings{maltecca_2019, title={Swine}, booktitle={Plant and Animal Genome XXVII Conference}, publisher={PAG}, author={Maltecca, Christian}, year={2019} }
 @article{baes_makanjuola_miglior_marras_howard_fleming_maltecca_2019, title={Symposium review: The genomic architecture of inbreeding: How homozygosity affects health and performance}, volume={102}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85059948091&partnerID=MN8TOARS}, DOI={10.3168/jds.2018-15520}, abstractNote={Inbreeding depression is a growing concern in livestock because it can detrimentally affect animal fitness, health, and production levels. Genomic information can be used to more effectively capture variance in Mendelian sampling, thereby enabling more accurate estimation of inbreeding, but further progress is still required. The calculation of inbreeding for herd management purposes is largely still done using pedigree information only, although inbreeding coefficients calculated in this manner have been shown to be less accurate than genomic inbreeding measures. Continuous stretches of homozygous genotypes, so called runs of homozygosity, have been shown to provide a better estimate of autozygosity at the genomic level than conventional measures based on inbreeding coefficients calculated through conventional pedigree information or even genomic relationship matrices. For improved and targeted management of genomic inbreeding at the population level, the development of methods that incorporate genomic information in mate selection programs may provide a more precise tool for reducing the detrimental effects of inbreeding in dairy herds. Additionally, a better understanding of the genomic architecture of inbreeding and incorporating that knowledge into breeding programs could significantly refine current practices. Opportunities to maintain high levels of genetic progress in traits of interest while managing homozygosity and sustaining acceptable levels of heterozygosity in highly selected dairy populations exist and should be examined more closely for continued sustainability of both the dairy cattle population as well as the dairy industry. The inclusion of precise genomic measures of inbreeding, such as runs of homozygosity, inbreeding, and mating programs, may provide a path forward. In this symposium review article, we describe traditional measures of inbreeding and the recent developments made toward more precise measures of homozygosity using genomic information. The effects of homozygosity resulting from inbreeding on phenotypes, the identification and mapping of detrimental homozygosity haplotypes, management of inbreeding with genomic data, and areas in need of further research are discussed.}, number={3}, journal={JOURNAL OF DAIRY SCIENCE}, author={Baes, Christine F. and Makanjuola, Bayode O. and Miglior, Filippo and Marras, Gabriele and Howard, Jeremy T. and Fleming, Allison and Maltecca, Christian}, year={2019}, month={Mar}, pages={2807–2817} }
 @inproceedings{maltecca_schwab_khanal_tiezzi_2019, title={The Microbiability of Meat Quality Traits in Swine}, booktitle={Plant and Animal Genome XXVII Conference}, publisher={PAG}, author={Maltecca, Christian and Schwab, C. and Khanal, P. and Tiezzi, F.}, year={2019} }
 @article{wackel_tiezzi_gray_flowers_huang_maltecca_2018, title={136 Evidence of Genetic Variation for Recombination Events in Purebred Swine Populations.}, volume={96}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/sky073.134}, DOI={10.1093/jas/sky073.134}, abstractNote={Recombination can affect the genetic gain of a trait in different ways. A high recombination rate can cause instability of genomic predictions as a result of the linkage disequilibrium breaking between markers and QTL. Conversely, recombination rate can maintain and increase the ability to recruit genetic variability by virtue of the same process. Within this research, we investigated the potential effects of sex and breed as well as the genetic variation of recombination events in swine. Data originated from four breed/sex commercial nucleus populations of Smithfield Premium Genetics: Large White sires (LWS, n=270), Large White dams (LWD, n=1755), Landrace sires (LRS, n=281) and Landrace dams (LRD, n=1356). Individuals in the analysis were genotyped at 10k, 60k or 80k Illumina SNP chips then all imputed to 80k using the Fimpute software. The software FindhapV4 was used to obtain the total number of recombination events for each individual’s progeny (n=20,712 total progeny records). The R package MCMCglmm was employed to fit a model with the total number of recombination events in the genome as the predicted variable. Animal and contemporary group (herd, year, and season of observed recombination event) were random predictors, while sex and breed were fixed effects. Heritability estimates of recombination were obtained within each breed/sex combination using THRGIBBS1F90.The model included the number of recombination events as a predictor variable and a random sire or dam effect for each population. The sire/dam effects was assumed N(0, G/Aσs/d2 ) where A and G were a pedigree or genomic relationship matrix, respectively. Two fixed effects were included, a contemporary group and a covariate for age at recombination event. Least squared mean estimates (LSME) of total number of recombination events for sex were 16.25(±0.152) in dams and 12.09(±0.181) in sires. LSME for breed were 14.32(±0.229) in LW and 14.05(±0.231) in LR. Sex and breed were both significant (p< 0.05).Heritabilities of recombination across the whole genome were 0.039(±0.036) for LRS, 0.074(±0.030) for LRD, 0.090(±0.062) for LWS, and 0.107(±0.034) for LWD. Heritabilities, when genomic data was included, were 0.050(±0.036) for LRS, 0.232(±0.028) for LRD, 0.084(±0.045) for LWS, and 0.257(±0.029) in LWD. These results show that recombination is heritable and that both sex and breed are significant contributors, with females and LW having a significantly larger number of recombination events. Further research should focus on environmental factors and the interaction between genetics and environment in determining recombination events.}, number={suppl_2}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Wackel, H and Tiezzi, F and Gray, K A and Flowers, W L and Huang, Y and Maltecca, C}, year={2018}, month={Apr}, pages={72–73} }
 @article{lu_tiezzi_maltecca_2018, title={298 Gut Microbiome Provides A New Source of Variation to Improve Growth Efficiency in Crossbred Pigs.}, volume={96}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/sky404.247}, DOI={10.1093/jas/sky404.247}, abstractNote={Gut microbiome has long been proven to affect pork production via nutritional, physiological, and immunological processes. We studied host genetics – gut microbiome relationship in pigs, seeking to incorporate such relationship in genetic imrpovement of pigs. There were 1205, 1295, and 1283 rectal samples collected at weaning (18.6 ± 1.09 days), 15 weeks post weaning (118.2 ± 1.18 days), and end of feeding trial (196.4 ± 7.86 days), respectively. There were 1039 animals having samples collected at all 3 time points. Analyses were performed at operational taxonomic unit (OTU) level, including 1755 OTUs. The animals were also gentoyped with the Illumina PorcineSNP60 Beadchip. Our association analyses identified 131 OTUs with large contribution to the total variance of backfat (BF), live weight (WT), and loin depth (LD), at week 14, 18, and 22, for each phenotypic record. Three OTUs (17, 758, and 1163) explained the largest proportion of the trait variance. Heritabilities of the 3 OTUs varied between 0.13 ± 0.05 and 0.40 ± 0.06 for OTU17, 0.02 ± 0.03 and 0.20 ± 0.06 for OTU758, 0.02 ± 0.03 and 0.21 ± 0.06 for OTU1163. Single nucleotide polymorphisms (SNPs) that had consistently large effects on OTU17 and OTU758, at week 15 and end of test, were identified on chromosomes 3, 6, and 7. Using microbiome data in estimating breeding values (BV) for BF and average daily weight gain (ADG) at 22 weeks post weaning, we found that providing the microbiome information, under the form of relatedness among individuals based on similarity of microbial communities, significantly improved the model fit for both BF and ADG, as well as reduced standard error of prediction for the BVs. This analysis was one of our preliminary attempts to working out a direction for using gut microbiome data in improving the accuracy of BVs in the pork industry.}, number={suppl_3}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Lu, D and Tiezzi, F and Maltecca, C}, year={2018}, month={Dec}, pages={112–113} }
 @article{khanal_maltecca_schwab_gray_tiezzi_2018, title={305 Genetic parameters of meat quality and carcass composition traits in crossbred swine.}, volume={96}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/sky404.254}, DOI={10.1093/jas/sky404.254}, abstractNote={The objective of this study was to estimate the heritabilities and genetic correlations of meat quality and carcass composition traits in 2 commercial crossbred swine populations: The Maschhoffs (TML) and Smithfield Premium Genetics (SPG). The TML dataset consisted of 1,255 crossbred individuals genotyped and phenotyped for meat quality (MQ), carcass yield (CY) and carcass weight (CW) traits. The SPG population included over 30,000 crossbred individuals phenotyped for a subset of MQ, CY and CW traits, and 1,156 sires genotyped. The two populations were analyzed separately with the use of multiple-trait genomic models. For the TML dataset, models included fixed effects of dam line, contemporary group (CG), gender, as well as a random additive genetic effect and pen nested within CG. For the SPG dataset, fixed effects included parity, gender and CG, as well as a random additive genetic effect and harvest group. Analyses were conducted using the BLUPf90 suite of programs. Bivariate analyses were used to estimate correlations among traits. Heritabilities [confidence interval] for CY traits (0.17[0.09, 0.25] to 0.45[0.36, 0.55]) were higher than CW (0.14[0.06, 0.23] to 0.30[0.20,0.41]). For MQ traits, heritabilities ranged from low to moderate having highest estimate for intramuscular fat: 0.52[0.40, 0.62]. Most of the genetic correlations were significant and ranged from -0.07[-0.14, -0.02]) to -0.70[-0.85, -0.84], 0.50[0.32, 0.79] to 0.81[0.78, 0.82] and -0.10[-0.02, -0.04] to -0.96 [-1.00, -0.83] respectively among CY, CW and MQ traits. The genetic correlations of MQ and carcass composition traits ranged from moderate to high in both directions. The genetic parameter estimates indicate that a multi-trait approach should be considered for selection programs aimed at carcass quality and composition in commercial crossbred swine population.}, number={suppl_3}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Khanal, P and Maltecca, C and Schwab, C and Gray, K and Tiezzi, F}, year={2018}, month={Dec}, pages={116–116} }
 @article{maltecca_howard_baes_pryce_2018, title={309 Beyond predictions: managing inbreeding and variability in the genomic era.}, volume={96}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.1093/jas/sky404.258}, DOI={10.1093/jas/sky404.258}, abstractNote={Routine inclusion of genomic information in livestock species has completed the first phase of genomic selection (GS) adoption as a breeding standard; however, the full potential of this tool is far from being realized. Since its introduction, genomic selection has revolutionized the breeding world with the opportunity of using DNA to generate fast and accurate individual predictions. A similar degree of change has yet to be seen in the utilization of genomic information to manage livestock populations. The reasons of success of GS are evident when looking at the tremendous impact it had on accelerating the rate of genetic gain. This has been achieved through substantially reducing the generation interval as candidates can be identified at an early age through the use of genetic markers to develop genomic breeding values with acceptable levels of accuracy. Furthermore, GS has enabled a significant boost in intensity through an increased number of candidates genotyped and available for selection. Yet, GS has not substantially changed the basic mechanisms of animal breeding, since genomic information is only used to effectively rank individuals at an earlier age based on their additive merit. Theory and early simulations suggested that implementation of GS should result in a lower rate of inbreeding per generation. However, experience has shown that sires selected on GEBV have a higher inbreeding than those selected using conventional approaches. GS offers considerable flexibility to boost genetic trends in traits of interest. It also provides an opportunity for more sustainable breeding in terms of fitness and genetic variability. In our work, we illustrate several examples of how GS can be used to better define inbreeding and how this can, in turn, be used to balance long term variability and short term gains for optimal genetic management of livestock populations.}, number={suppl_3}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Maltecca, C and Howard, J and Baes, C and Pryce, J}, year={2018}, month={Dec}, pages={117–118} }
 @article{putz_tiezzi_maltecca_gray_knauer_2018, title={A comparison of accuracy validation methods for genomic and pedigree-based predictions of swine litter size traits using Large White and simulated data}, volume={135}, ISSN={["1439-0388"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85040771964&partnerID=MN8TOARS}, DOI={10.1111/jbg.12302}, abstractNote={Summary The objective of this study was to compare and determine the optimal validation method when comparing accuracy from single‐step GBLUP (ss GBLUP ) to traditional pedigree‐based BLUP . Field data included six litter size traits. Simulated data included ten replicates designed to mimic the field data in order to determine the method that was closest to the true accuracy. Data were split into training and validation sets. The methods used were as follows: (i) theoretical accuracy derived from the prediction error variance ( PEV ) of the direct inverse ( iLHS ), (ii) approximated accuracies from the accf90( GS ) program in the BLUPF 90 family of programs (Approx), (iii) correlation between predictions and the single‐step GEBV s from the full data set (GEBV Full ), (iv) correlation between predictions and the corrected phenotypes of females from the full data set ( Y c ), (v) correlation from method iv divided by the square root of the heritability ( Y ch ) and (vi) correlation between sire predictions and the average of their daughters' corrected phenotypes ( Y cs ). Accuracies from iLHS increased from 0.27 to 0.37 (37%) in the Large White. Approximation accuracies were very consistent and close in absolute value (0.41 to 0.43). Both iLHS and Approx were much less variable than the corrected phenotype methods (ranging from 0.04 to 0.27). On average, simulated data showed an increase in accuracy from 0.34 to 0.44 (29%) using ss GBLUP . Both iLHS and Y ch approximated the increase well, 0.30 to 0.46 and 0.36 to 0.45, respectively. GEBV Full performed poorly in both data sets and is not recommended. Results suggest that for within‐breed selection, theoretical accuracy using PEV was consistent and accurate. When direct inversion is infeasible to get the PEV , correlating predictions to the corrected phenotypes divided by the square root of heritability is adequate given a large enough validation data set.}, number={1}, journal={JOURNAL OF ANIMAL BREEDING AND GENETICS}, author={Putz, A. M. and Tiezzi, F. and Maltecca, C. and Gray, K. A. and Knauer, M. T.}, year={2018}, month={Feb}, pages={5–13} }
 @article{morgante_huang_maltecca_mackay_2018, title={Effect of genetic architecture on the prediction accuracy of quantitative traits in samples of unrelated individuals}, volume={120}, ISSN={["1365-2540"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85041836520&partnerID=MN8TOARS}, DOI={10.1038/s41437-017-0043-0}, abstractNote={Predicting complex phenotypes from genomic data is a fundamental aim of animal and plant breeding, where we wish to predict genetic merits of selection candidates; and of human genetics, where we wish to predict disease risk. While genomic prediction models work well with populations of related individuals and high linkage disequilibrium (LD) (e.g., livestock), comparable models perform poorly for populations of unrelated individuals and low LD (e.g., humans). We hypothesized that low prediction accuracies in the latter situation may occur when the genetics architecture of the trait departs from the infinitesimal and additive architecture assumed by most prediction models. We used simulated data for 10,000 lines based on sequence data from a population of unrelated, inbred Drosophila melanogaster lines to evaluate this hypothesis. We show that, even in very simplified scenarios meant as a stress test of the commonly used Genomic Best Linear Unbiased Predictor (G-BLUP) method, using all common variants yields low prediction accuracy regardless of the trait genetic architecture. However, prediction accuracy increases when predictions are informed by the genetic architecture inferred from mapping the top variants affecting main effects and interactions in the training data, provided there is sufficient power for mapping. When the true genetic architecture is largely or partially due to epistatic interactions, the additive model may not perform well, while models that account explicitly for interactions generally increase prediction accuracy. Our results indicate that accounting for genetic architecture can improve prediction accuracy for quantitative traits.}, number={6}, journal={HEREDITY}, author={Morgante, Fabio and Huang, Wen and Maltecca, Christian and Mackay, Trudy F. C.}, year={2018}, month={Jun}, pages={500–514} }
 @article{howard_ashwell_baynes_brooks_yeatts_maltecca_2018, title={Genetic Parameter Estimates for Metabolizing Two Common Pharmaceuticals in Swine}, volume={9}, ISSN={1664-8021}, url={http://dx.doi.org/10.3389/fgene.2018.00040}, DOI={10.3389/fgene.2018.00040}, abstractNote={In livestock, the regulation of drugs used to treat livestock has received increased attention and it is currently unknown how much of the phenotypic variation in drug metabolism is due to the genetics of an animal. Therefore, the objective of the study was to determine the amount of phenotypic variation in fenbendazole and flunixin meglumine drug metabolism due to genetics. The population consisted of crossbred female and castrated male nursery pigs (n = 198) that were sired by boars represented by four breeds. The animals were spread across nine batches. Drugs were administered intravenously and blood collected a minimum of 10 times over a 48 h period. Genetic parameters for the parent drug and metabolite concentration within each drug were estimated based on pharmacokinetics (PK) parameters or concentrations across time utilizing a random regression model. The PK parameters were estimated using a non-compartmental analysis. The PK model included fixed effects of sex and breed of sire along with random sire and batch effects. The random regression model utilized Legendre polynomials and included a fixed population concentration curve, sex, and breed of sire effects along with a random sire deviation from the population curve and batch effect. The sire effect included the intercept for all models except for the fenbendazole metabolite (i.e., intercept and slope). The mean heritability across PK parameters for the fenbendazole and flunixin meglumine parent drug (metabolite) was 0.15 (0.18) and 0.31 (0.40), respectively. For the parent drug (metabolite), the mean heritability across time was 0.27 (0.60) and 0.14 (0.44) for fenbendazole and flunixin meglumine, respectively. The errors surrounding the heritability estimates for the random regression model were smaller compared to estimates obtained from PK parameters. Across both the PK and plasma drug concentration across model, a moderate heritability was estimated. The model that utilized the plasma drug concentration across time resulted in estimates with a smaller standard error compared to models that utilized PK parameters. The current study found a low to moderate proportion of the phenotypic variation in metabolizing fenbendazole and flunixin meglumine that was explained by genetics in the current study.}, number={FEB}, journal={Frontiers in Genetics}, publisher={Frontiers Media SA}, author={Howard, Jeremy T. and Ashwell, Melissa S. and Baynes, Ronald E. and Brooks, James D. and Yeatts, James L. and Maltecca, Christian}, year={2018}, month={Feb} }
 @inproceedings{cole_gaddis_null_maltecca_clay_2018, title={Genome-wide association study and gene network analysis of fertility, retained placenta, and metritis in US Holstein cattle}, volume={1}, booktitle={Proceedings of the World Congress on Genetics Applied to Livestock Production}, author={Cole, J.B. and Gaddis, K.L.P. and Null, D.J. and Maltecca, C. and Clay, J.S.}, year={2018}, pages={171} }
 @inproceedings{makanjuola_miglior_melzer_sargolzaei_maltecca_fleming_baes_2018, title={Genomic inbreeding estimation from whole genome sequence compared to medium density genomic data estimates}, volume={11}, booktitle={Proceedings of the World Congress on Genetics Applied to Livestock Production}, author={Makanjuola, B. and Miglior, F. and Melzer, N. and Sargolzaei, M. and Maltecca, C. and Fleming, A. and Baes, C.F.}, year={2018}, pages={603} }
 @inproceedings{maltecca_lu_tiezzi_mcnulty_schwab_2018, title={Host Variability and the Longitudinal Diversity of Microbiota Composition in Swine}, booktitle={Plant and Animal Genome XXVI Conference}, publisher={PAG}, author={Maltecca, Christian and Lu, B. and Tiezzi, F. and McNulty, N. and Schwab, C.}, year={2018} }
 @article{lu_tiezzi_schillebeeckx_mcnulty_schwab_shull_maltecca_2018, title={Host contributes to longitudinal diversity of fecal microbiota in swine selected for lean growth}, volume={6}, ISSN={["2049-2618"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85042877844&partnerID=MN8TOARS}, DOI={10.1186/s40168-017-0384-1}, abstractNote={In pigs, gut bacteria have been shown to play important roles in nutritional, physiological, and immunological processes in the host. However, the contribution of their metagenomes or part of them, which are normally reflected by fragments of 16S rRNA-encoding genes, has yet to be fully investigated. Fecal samples, collected from a population of crossbred pigs at three time points, including weaning, week 15 post weaning (hereafter "week 15"), and end-of-feeding test (hereafter "off-test"), were used to evaluate changes in the composition of the fecal microbiome of each animal over time. This study used 1205, 1295, and 1283 samples collected at weaning, week 15, and off-test, respectively. There were 1039 animals that had samples collected at all three time points and also had phenotypic records on back fat thickness (BF) and average daily body weight gain (ADG). Firmicutes and Bacteroidetes were the most abundant phyla at all three time points. The most abundant genera at all three time points included Clostridium, Escherichia, Bacteroides, Prevotella, Ruminococcus, Fusobacterium, Campylobacter, Eubacterium, and Lactobacillus. Two enterotypes were identified at each time point. However, only enterotypes at week 15 and off-test were significantly associated with BF. We report herein two novel findings: (i) alpha diversity and operational taxonomic unit (OTU) richness were moderately heritable at week 15, h2 of 0.15 ± 0.06 to 0.16 ± 0.07 and 0.23 ± 0.09 to 0.26 ± 0.08, respectively, as well as at off-test, h2 of 0.20 ± 0.09 to 0.33 ± 0.10 and 0.17 ± 0.08 to 0.24 ± 0.08, respectively, whereas very low heritability estimates for both measures were detected at weaning; and (ii) alpha diversity at week 15 had strong and negative genetic correlations with BF, − 0.53 ± 0.23 to − 0.45 ± 0.25, as well as with ADG, − 0.53 ± 0.32 to − 0.53 ± 0.29. These results are important for efforts to genetically improve the domesticated pig because they suggest fecal microbiota diversity can be used as an indicator trait to improve traits that are expensive to measure.}, number={1}, journal={MICROBIOME}, publisher={BioMed Central}, author={Lu, Duc and Tiezzi, Francesco and Schillebeeckx, Constantino and McNulty, Nathan P. and Schwab, Clint and Shull, Caleb and Maltecca, Christian}, year={2018}, month={Jan} }
 @article{marras_howard_martin_fleming_alves_makanjuola_schenkel_miglior_maltecca_baes_2018, title={Identification of unfavourable homozygous haplotypes associated with with milk and fertility traits in Holsteins}, volume={11}, journal={Proceedings of the  World Congress Genetics Applied Livestock Production}, author={Marras, G. and Howard, J. and Martin, P. and Fleming, A. and Alves, K. and Makanjuola, B. and Schenkel, F. and Miglior, F. and Maltecca, C. and Baes, C.}, year={2018}, pages={767} }
 @article{tiezzi_arceo_cole_maltecca_2018, title={Including gene networks to predict calving difficulty in Holstein, Brown Swiss and Jersey cattle}, volume={19}, ISSN={["1471-2156"]}, url={https://doi.org/10.1186/s12863-018-0606-y}, DOI={10.1186/s12863-018-0606-y}, abstractNote={Calving difficulty or dystocia has a great economic impact in the US dairy industry. Reported risk factors associated with calving difficulty are feto-pelvic disproportion, gestation length and conformation. Different dairy cattle breeds have different incidence of calving difficulty, with Holstein having the highest dystocia rates and Jersey the lowest. Genomic selection becomes important especially for complex traits with low heritability, where the accuracy of conventional selection is lower. However, for complex traits where a large number of genes influence the phenotype, genome-wide association studies showed limitations. Biological networks could overcome some of these limitations and better capture the genetic architecture of complex traits. In this paper, we characterize Holstein, Brown Swiss and Jersey breed-specific dystocia networks and employ them in genomic predictions. Marker association analysis identified single nucleotide polymorphisms explaining the largest average proportion of genetic variance on BTA18 in Holstein, BTA25 in Brown Swiss, and BTA15 in Jersey. Gene networks derived from the genome-wide association included 1272 genes in Holstein, 1454 genes in Brown Swiss, and 1455 genes in Jersey. Furthermore, 256 genes in Holstein network, 275 genes in the Brown Swiss network, and 253 genes in the Jersey network were within previously reported dystocia quantitative trait loci. The across-breed network included 80 genes, with 9 genes being within previously reported dystocia quantitative trait loci. The gene-gene interactions in this network differed in the different breeds. Gene ontology enrichment analysis of genes in the networks showed Regulation of ARF GTPase was very significant (FDR ≤ 0.0098) on Holstein. Neuron morphogenesis and differentiation was the term most enriched (FDR ≤ 0.0539) on the across-breed network. Genomic prediction models enriched with network-derived relationship matrices did not outperform regular GBLUP models. Regions identified in the genome were in the proximity of previously described quantitative trait loci that would most likely affect calving difficulty by altering the feto-pelvic proportion. Inclusion of identified networks did not increase prediction accuracy. The approach used in this paper could be extended to any instance with asymmetric distribution of phenotypes, for example, resistance to disease data.}, number={1}, journal={BMC GENETICS}, publisher={Springer Nature}, author={Tiezzi, Francesco and Arceo, Maria E. and Cole, John B. and Maltecca, Christian}, year={2018}, month={Apr} }
 @misc{fleming_abdalla_maltecca_baes_2018, title={Invited review: Reproductive and genomic technologies to optimize breeding strategies for genetic progress in dairy cattle}, volume={61}, ISSN={["2363-9822"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85041051692&partnerID=MN8TOARS}, DOI={10.5194/aab-61-43-2018}, abstractNote={Abstract. Dairy cattle breeders have exploited technological advances that have emerged in the past in regards to reproduction and genomics. The implementation of such technologies in routine breeding programs has permitted genetic gains in traditional milk production traits as well as, more recently, in low-heritability traits like health and fertility. As demand for dairy products increases, it is important for dairy breeders to optimize the use of available technologies and to consider the many emerging technologies that are currently being investigated in various fields. Here we review a number of technologies that have helped shape dairy breeding programs in the past and present, along with those potentially forthcoming. These tools have materialized in the areas of reproduction, genotyping and sequencing, genetic modification, and epigenetics. Although many of these technologies bring encouraging opportunities for genetic improvement of dairy cattle populations, their applications and benefits need to be weighed with their impacts on economics, genetic diversity, and society.}, number={1}, journal={ARCHIVES ANIMAL BREEDING}, author={Fleming, Allison and Abdalla, Emhimad A. and Maltecca, Christian and Baes, Christine F.}, year={2018}, month={Jan}, pages={43–57} }
 @inproceedings{maltecca_lu_tiezzi_schillebeeckx_mcnulty_schwab_shull_2018, title={Metagenomic predictions of growth and carcass traits in pigs with the use of bayesian alphabet and machine learning methods}, booktitle={Proceedings of the World Congress of Genetics Applied to Livestock Production}, author={Maltecca, C. and Lu, D. and Tiezzi, F. and Schillebeeckx, C. and McNulty, N. and Schwab, C. and Shull, C.}, year={2018} }
 @inproceedings{lu_tiezzi_schillebeeckx_mcnulty_schwab_maltecca_2018, title={Microbiome Contribute Significantly to Variation in Fat and Growth Traits in Crossbred Pigs?}, volume={2}, booktitle={Proceedings of the World Congress on Genetics Applied to Livestock Production}, author={Lu, D. and Tiezzi, F. and Schillebeeckx, C. and McNulty, N.P. and Schwab, C. and Maltecca, C.}, year={2018}, pages={614} }
 @inproceedings{sewel_li_schwab_maltecca_tiezzi_2018, title={On the value of genotyping terminal crossbred pigs for nucleus genomic selection for carcass traits}, booktitle={Proceedings of the World Congress Genetics Applied to Livestock Production}, author={Sewel, A. and Li, H. and Schwab, C. and Maltecca, C. and Tiezzi, F.}, year={2018} }
 @article{maltecca_lu_schillebeeckx_mcnulty_schwab_schull_tiezzi_2018, title={Predicting growth and carcass traits in swine using metagenomic data and machine learning algorithms}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85095635612&partnerID=MN8TOARS}, DOI={10.1101/363309}, abstractNote={ABSTRACT In this paper, we evaluated the power of metagenome measures taken at three time points over the growth test period (weaning, 15 and 22 weeks) to foretell growth and carcass traits in 1039 individuals of a line of crossbred pigs. We measured prediction accuracy as the correlation between actual and predicted phenotypes in a five-fold cross-validation setting. Phenotypic traits measured included live weight measures and carcass composition obtained during the trial as well as at slaughter. We employed a null model excluding microbiome information as a baseline to assess the increase in prediction accuracy stemming from the inclusion of operational taxonomic units (OTU) as predictors. We further contrasted performance of models from the Bayesian alphabet (Bayesian Lasso) as well machine learning approaches (Random Forest and Gradient Boosting) and semi-parametric kernel models (Reproducing Kernel Hilbert space). In most cases, prediction accuracy increased significantly with the inclusion of microbiome data. Accuracy was more substantial with the inclusion of metagenomic information taken at week 15 and 22, with values ranging from approximately 0.30 for loin traits to more than 0.50 for back-fat. Conversely, microbiome composition at weaning resulted in most cases in marginal gains of prediction accuracy, suggesting that later measures might be more useful to include in predictive models. Model choice affected predictions marginally with no clear winner for any model/trait/time point. We, therefore, suggest average prediction across models as a robust strategy in fitting metagenomic information. In conclusion, microbiome composition can effectively be used as a predictor of growth and composition traits, particularly for fatness traits. The inclusion of OTU predictors could potentially be used to promote fast growth of individuals while limiting fat accumulation. Early microbiome measures might not be good predictors of growth and OTU information might be best collected at later life stages. Future research should focus on the inclusion of both microbiome as well as host genome information in predictions, as well as the interaction between the two. Furthermore, the influence of microbiome on feed efficiency as well as carcass and meat quality should be investigated.}, journal={bioRxiv}, author={Maltecca, C. and Lu, D. and Schillebeeckx, C. and McNulty, N.P. and Schwab, C. and Schull, C. and Tiezzi, F.}, year={2018} }
 @inproceedings{tuggle_maltecca_2018, title={Swine}, booktitle={Plant and Animal Genome XXVI Conference}, publisher={PAG}, author={Tuggle, C.K. and Maltecca, C.}, year={2018} }
 @inbook{isik_holland_maltecca_2017, title={A Review of Linear Mixed Models}, ISBN={9783319551753 9783319551777}, url={http://dx.doi.org/10.1007/978-3-319-55177-7_2}, DOI={10.1007/978-3-319-55177-7_2}, booktitle={Genetic Data Analysis for Plant and Animal Breeding}, publisher={Springer International Publishing}, author={Isik, Fikret and Holland, James and Maltecca, Christian}, year={2017}, pages={49–86} }
 @article{howard_tiezzi_huang_gray_maltecca_2017, title={A heuristic method to identify runs of homozygosity associated with reduced performance in livestock}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85095649334&partnerID=MN8TOARS}, DOI={10.1101/131706}, abstractNote={ABSTRACT While for the most part genome-wide metrics are currently employed in managing livestock inbreeding, genomic data offer, in principle, the ability to identify functional inbreeding. Here we present a heuristic method to identify haplotypes contained within a run of homozygosity (ROH) associated with reduced performance. Results are presented for simulated and swine data. The algorithm comprises 3 steps. Step 1 scans the genome based on marker windows of decreasing size and identifies ROH genotypes associated with an unfavorable phenotype. Within this stage, multiple aggregation steps reduce the haplotype to the smallest possible length. In step 2, the resulting regions are formally tested for significance with the use of a linear mixed model. Lastly, step 3 removes nested windows. The effect of the unfavorable haplotypes identified and their associated haplotype probabilities for a progeny of a given mating pair or an individual can be used to generate an inbreeding load matrix ( ILM ). Diagonals of ILM characterize the functional inbreeding load of individual (IIL). We estimated the accuracy of predicting the phenotype based on ILL. We further compared the significance of the regression coefficient for IIL on phenotypes to genome-wide inbreeding metrics. We tested the algorithm using simulated scenarios (n =12) combining different levels of linkage disequilibrium (LD) and number of loci impacting a quantitative trait. Additionally, we investigated 9 traits from two maternal purebred swine lines. In simulated data, as the LD in the population increased the algorithm identified a greater proportion of the true unfavorable ROH effects. For example, the proportion of highly unfavorable true ROH effects identified raised from 32 to 41 % for the low to the high LD scenario. In both simulated and real data the haplotypes identified were contained within a much larger ROH (9.12-12.1 Mb). The IIL prediction accuracy was greater than zero across all scenarios for simulated data (high LD scenario mean (95% confidence interval): 0.49 (0.47-0.52)) and for nearly all swine traits (mean ± SD: 0.17±0.10). On average across simulated and swine datasets the IIL regression coefficient was more closely related to progeny performance than any genome-wide inbreeding metric. A heuristic method was developed that identified ROH genotypes with reduced performance and characterized the combined effects of ROH genotypes within and across individuals.}, journal={bioRxiv}, author={Howard, J.T. and Tiezzi, F. and Huang, Y. and Gray, K.A. and Maltecca, C.}, year={2017} }
 @article{howard_tiezzi_huang_gray_maltecca_2017, title={A heuristic method to identify runs of homozygosity associated with reduced performance in livestock}, volume={95}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85031745977&partnerID=MN8TOARS}, DOI={10.2527/jas2017.1664}, abstractNote={Although, for the most part, genome-wide metrics are currently used in managing livestock inbreeding, genomic data offer, in principle, the ability to identify functional inbreeding. Here, we present a heuristic method to identify haplotypes contained within a run of homozygosity (ROH) associated with reduced performance. Results are presented for simulated and swine data. The algorithm comprises 3 steps. Step 1 scans the genome based on marker windows of decreasing size and identifies ROH genotypes associated with an unfavorable phenotype. Within this stage, multiple aggregation steps reduce the haplotype to the smallest possible length. In step 2, the resulting regions are formally tested for significance with the use of a linear mixed model. Lastly, step 3 removes nested windows. The effect of the unfavorable haplotypes identified and their associated haplotype probabilities for a progeny of a given mating pair or an individual can be used to generate an inbreeding load matrix (ILM). Diagonals of ILM characterize the functional individual inbreeding load (IIL). We estimated the accuracy of predicting the phenotype based on IIL. We further compared the significance of the regression coefficient for IIL on phenotypes with genome-wide inbreeding metrics. We tested the algorithm using simulated scenarios (12 scenarios), combining different levels of linkage disequilibrium (LD) and number of loci impacting a quantitative trait. Additionally, we investigated 9 traits from 2 maternal purebred swine lines. In simulated data, as the LD in the population increased, the algorithm identified a greater proportion of the true unfavorable ROH effects. For example, the proportion of highly unfavorable true ROH effects identified rose from 32 to 41% for the low- to the high-LD scenario. In both simulated and real data, the haplotypes identified were contained within a much larger ROH (9.12-12.1 Mb). The IIL prediction accuracy was greater than 0 across all scenarios for simulated data (mean of 0.49 [95% confidence interval 0.47-0.52] for the high-LD scenario) and for nearly all swine traits (mean of 0.17 [SD 0.10]). On average, across simulated and swine data sets, the IIL regression coefficient was more closely related to progeny performance than any genome-wide inbreeding metric. A heuristic method was developed that identified ROH genotypes with reduced performance and characterized the combined effects of ROH genotypes within and across individuals.}, number={10}, journal={Journal of Animal Science}, author={Howard, J.T. and Tiezzi, F. and Huang, Y. and Gray, K.A. and Maltecca, C.}, year={2017}, pages={4318–4332} }
 @article{cole_bormann_gill_khatib_koltes_maltecca_miglior_2017, title={BREEDING AND GENETICS SYMPOSIUM: Resilience of livestock to changing environments}, volume={95}, ISSN={["1525-3163"]}, DOI={10.2527/jas.2017.1402}, abstractNote={The Breeding and Genetics Symposium titled “Resilience of Livestock to Changing Environments” was held at the Joint Annual Meeting, July 19–24, 2016, in Salt Lake City, UT. The objective of the symposium was to provide a broad overview of recent research on the effects of changing environmental conditions on livestock. Topics covered by the speakers included a review of the variation in response to heat stress and its effects on metabolic parameters and energy demands in pigs and cattle, production and reproduction in livestock and aquaculture species, the development of genetic improvement programs to produce more robust animals, and the use of gene introgression to develop heat-resistant animals. Substantial discussion focused on the tradeoffs involved in producing robust, high-producing livestock. The symposium included 6 invited presentations, each of which is discussed below. Modern livestock have been selected to efficiently convert feed into food and fiber for human use, but the most productive breeds generally require intensive management to maintain high levels of production. Most major livestock breeds in the U.S. are derived from animals that evolved in temperate climates, such as Holstein dairy cattle. Unfortunately, the climate in the southern states is hot enough to cause several months per year of heat stress. Heat stress occurs when the environmental temperature exceeds an animal's thermoneutral point, and its effects include decreased dry matter intake, reduced water consumption, depressed production, and impaired fertility (e.g., West, 2003). These effects will become more common in areas that have not previously experienced heat stress as global temperatures continue to rise (IPCC, 2014). Technological interventions, including fans, sprinklers, and shade structures, can be used to ameliorate many of the effects of heat stress, but they provide only temporary relief. Genetic selection for greater thermotolerance is possible and will result in cumulative, permanent gains (Aguilar et al., 2009; Dikmen et al., 2012, 2015).}, number={4}, journal={JOURNAL OF ANIMAL SCIENCE}, author={Cole, J. B. and Bormann, M. and Gill, C. A. and Khatib, H. and Koltes, J. E. and Maltecca, C. and Miglior, F.}, year={2017}, month={Apr}, pages={1777–1779} }
 @inbook{isik_holland_maltecca_2017, title={Breeding Values}, ISBN={9783319551753 9783319551777}, url={http://dx.doi.org/10.1007/978-3-319-55177-7_4}, DOI={10.1007/978-3-319-55177-7_4}, booktitle={Genetic Data Analysis for Plant and Animal Breeding}, publisher={Springer International Publishing}, author={Isik, Fikret and Holland, James and Maltecca, Christian}, year={2017}, pages={107–140} }
 @article{cole_bormann_gill_khatib_koltes_maltecca_miglior_2017, title={Breeding and genetics symposium: Resilience of livestock to changing environments}, volume={95}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85019637384&partnerID=MN8TOARS}, DOI={10.2527/jas2017.1402}, number={4}, journal={Journal of Animal Science}, author={Cole, J.B. and Bormann, J.M. and Gill, C.A. and Khatib, H. and Koltes, J.E. and Maltecca, C. and Miglior, F.}, year={2017}, pages={1777–1779} }
 @inproceedings{houlahan_beard_miglior_richardson_maltecca_gredler_baes_2017, place={Leiden, The Netherlands}, title={Design of breeding strategies for feed efficiency and methane emissions in Holstein using ZPLAN+}, DOI={10.3920/9789086868599_211}, booktitle={Book of Abstracts of the 68th Annual Meeting of the European Federation of Animal Science}, publisher={Wageningen Academic}, author={Houlahan, K. and Beard, S. and Miglior, F. and Richardson, C. and Maltecca, C. and Gredler, B. and Baes, C.}, year={2017}, pages={183–183} }
 @article{thorpe_xi_maltecca_walters_smith_odle_jacobi_2017, title={Dietary prebiotics and arachidonic acid alter intestinal phospholipid composition and time-dependently change fecal microbiome in formula-fed piglets}, volume={31}, journal={FASEB Journal}, author={Thorpe, M. K., Xi and Xi, L. and Maltecca, C. and Walters, K.R. and Smith, A. and Odle, J. and Jacobi, S. K.}, year={2017} }
 @inbook{isik_holland_maltecca_2017, title={Exploratory Marker Data Analysis}, ISBN={9783319551753 9783319551777}, url={http://dx.doi.org/10.1007/978-3-319-55177-7_9}, DOI={10.1007/978-3-319-55177-7_9}, booktitle={Genetic Data Analysis for Plant and Animal Breeding}, publisher={Springer International Publishing}, author={Isik, Fikret and Holland, James and Maltecca, Christian}, year={2017}, pages={263–285} }
 @article{howard_ashwell_baynes_brooks_yeatts_maltecca_2017, title={Gene co-expression network analysis identifies porcine genes associated with variation in metabolizing fenbendazole and flunixin meglumine in the liver}, volume={7}, ISSN={2045-2322}, url={http://dx.doi.org/10.1038/s41598-017-01526-5}, DOI={10.1038/s41598-017-01526-5}, abstractNote={Identifying individual genetic variation in drug metabolism pathways is of importance not only in livestock, but also in humans in order to provide the ultimate goal of giving the right drug at the right dose at the right time. Our objective was to identify individual genes and gene networks involved in metabolizing fenbendazole (FBZ) and flunixin meglumine (FLU) in swine liver. The population consisted of female and castrated male pigs that were sired by boars represented by 4 breeds. Progeny were randomly placed into groups: no drug (UNT), FLU or FBZ administered. Liver transcriptome profiles from 60 animals with extreme (i.e. fast or slow drug metabolism) pharmacokinetic (PK) profiles were generated from RNA sequencing. Multiple cytochrome P450 (CYP1A1, CYP2A19 and CYP2C36) genes displayed different transcript levels across treated versus UNT. Weighted gene co-expression network analysis identified 5 and 3 modules of genes correlated with PK parameters and a portion of these were enriched for biological processes relevant to drug metabolism for FBZ and FLU, respectively. Genes within identified modules were shown to have a higher transcript level relationship (i.e. connectivity) in treated versus UNT animals. Investigation into the identified genes would allow for greater insight into FBZ and FLU metabolism.}, number={1}, journal={Scientific Reports}, publisher={Springer Nature}, author={Howard, Jeremy T. and Ashwell, Melissa S. and Baynes, Ronald E. and Brooks, James D. and Yeatts, James L. and Maltecca, Christian}, year={2017}, month={May} }
 @book{isik_holland_maltecca_2017, title={Genetic Data Analysis for Plant and Animal Breeding}, ISBN={9783319551753 9783319551777}, url={http://dx.doi.org/10.1007/978-3-319-55177-7}, DOI={10.1007/978-3-319-55177-7}, publisher={Springer International Publishing}, author={Isik, Fikret and Holland, James and Maltecca, Christian}, year={2017} }
 @inproceedings{maltecca_lu_tiezzi_2017, title={Genetics and genomics of swine lean growth at the interface between host and commensal gut bacteria}, booktitle={Proceedings of the 22nd Association for the Advancement of Animal Breeding and Genetics Conference}, author={Maltecca, C. and Lu, B. and Tiezzi, F.}, year={2017}, pages={221–228} }
 @article{howard_tiezzi_pryce_maltecca_2017, title={Geno-Diver: A combined coalescence and forward-in-time simulator for populations undergoing selection for complex traits}, volume={134}, ISSN={["1439-0388"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85018945915&partnerID=MN8TOARS}, DOI={10.1111/jbg.12277}, abstractNote={Summary Geno‐Diver is a combined coalescence and forward‐in‐time simulator designed to simulate complex traits with a quantitative and/or fitness component and implement multiple selection and mating strategies utilizing pedigree or genomic information. The simulation is carried out in two steps. The first step generates whole‐genome sequence data for founder individuals. A variety of trait architectures can be generated for quantitative and fitness traits along with their covariance. The second step generates new individuals forward‐in‐time based on a variety of selection and mating scenarios. Genetic values are predicted for individuals utilizing pedigree or genomic information. Relationship matrices and their associated inverses are generated using computationally efficient routines. We benchmarked Geno‐Diver with a previous simulation program and described how to simulate a traditional quantitative trait along with a quantitative and fitness trait. A user manual with examples, source code in C++11 and executable versions of Geno‐Diver for Linux are freely available at https://github.com/jeremyhoward/Geno-Diver .}, number={6}, journal={JOURNAL OF ANIMAL BREEDING AND GENETICS}, author={Howard, J. T. and Tiezzi, F. and Pryce, J. E. and Maltecca, C.}, year={2017}, month={Dec}, pages={553–563} }
 @misc{isik_holland_maltecca_2017, title={Genomic Relationships and GBLUP}, ISBN={9783319551753 9783319551777}, url={http://dx.doi.org/10.1007/978-3-319-55177-7_11}, DOI={10.1007/978-3-319-55177-7_11}, journal={Genetic Data Analysis for Plant and Animal Breeding}, publisher={Springer International Publishing}, author={Isik, Fikret and Holland, James and Maltecca, Christian}, year={2017}, pages={311–354} }
 @article{tiezzi_campos_gaddis_maltecca_2017, title={Genotype by environment (climate) interaction improves genomic prediction for production traits in US Holstein cattle}, volume={100}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85009801648&partnerID=MN8TOARS}, DOI={10.3168/jds.2016-11543}, abstractNote={Genotype by environment interaction (G × E) in dairy cattle productive traits has been shown to exist, but current genetic evaluation methods do not take this component into account. As several environmental descriptors (e.g., climate, farming system) are known to vary within the United States, not accounting for the G × E could lead to reranking of bulls and loss in genetic gain. Using test-day records on milk yield, somatic cell score, fat, and protein percentage from all over the United States, we computed within herd-year-season daughter yield deviations for 1,087 Holstein bulls and regressed them on genetic and environmental information to estimate variance components and to assess prediction accuracy. Genomic information was obtained from a 50k SNP marker panel. Environmental effect inputs included herd (160 levels), geographical region (7 levels), geographical location (2 variables), climate information (7 variables), and management conditions of the herds (16 total variables divided in 4 subgroups). For each set of environmental descriptors, environmental, genomic, and G × E components were sequentially fitted. Variance components estimates confirmed the presence of G × E on milk yield, with its effect being larger than main genetic effect and the environmental effect for some models. Conversely, G × E was moderate for somatic cell score and small for milk composition. Genotype by environment interaction, when included, partially eroded the genomic effect (as compared with the models where G × E was not included), suggesting that the genomic variance could at least in part be attributed to G × E not appropriately accounted for. Model predictive ability was assessed using 3 cross-validation schemes (new bulls, incomplete progeny test, and new environmental conditions), and performance was compared with a reference model including only the main genomic effect. In each scenario, at least 1 of the models including G × E was able to perform better than the reference model, although it was not possible to find the overall best-performing model that included the same set of environmental descriptors. In general, the methodology used is promising in accounting for G × E in genomic predictions, but challenges exist in identifying a unique set of covariates capable of describing the entire variety of environments.}, number={3}, journal={JOURNAL OF DAIRY SCIENCE}, author={Tiezzi, F. and Campos, G. and Gaddis, K. L. Parker and Maltecca, C.}, year={2017}, month={Mar}, pages={2042–2056} }
 @inbook{isik_holland_maltecca_2017, title={Imputing Missing Genotypes}, ISBN={9783319551753 9783319551777}, url={http://dx.doi.org/10.1007/978-3-319-55177-7_10}, DOI={10.1007/978-3-319-55177-7_10}, booktitle={Genetic Data Analysis for Plant and Animal Breeding}, publisher={Springer International Publishing}, author={Isik, Fikret and Holland, James and Maltecca, Christian}, year={2017}, pages={287–309} }
 @inbook{isik_holland_maltecca_2017, title={Introduction to ASReml Software}, ISBN={9783319551753 9783319551777}, url={http://dx.doi.org/10.1007/978-3-319-55177-7_1}, DOI={10.1007/978-3-319-55177-7_1}, booktitle={Genetic Data Analysis for Plant and Animal Breeding}, publisher={Springer International Publishing}, author={Isik, Fikret and Holland, James and Maltecca, Christian}, year={2017}, pages={1–48} }
 @misc{howard_pryce_baes_maltecca_2017, title={Invited review: Inbreeding in the genomics era: Inbreeding, inbreeding depression, and management of genomic variability}, volume={100}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85020250647&partnerID=MN8TOARS}, DOI={10.3168/jds.2017-12787}, abstractNote={Traditionally, pedigree-based relationship coefficients have been used to manage the inbreeding and degree of inbreeding depression that exists within a population. The widespread incorporation of genomic information in dairy cattle genetic evaluations allows for the opportunity to develop and implement methods to manage populations at the genomic level. As a result, the realized proportion of the genome that 2 individuals share can be more accurately estimated instead of using pedigree information to estimate the expected proportion of shared alleles. Furthermore, genomic information allows genome-wide relationship or inbreeding estimates to be augmented to characterize relationships for specific regions of the genome. Region-specific stretches can be used to more effectively manage areas of low genetic diversity or areas that, when homozygous, result in reduced performance across economically important traits. The use of region-specific metrics should allow breeders to more precisely manage the trade-off between the genetic value of the progeny and undesirable side effects associated with inbreeding. Methods tailored toward more effectively identifying regions affected by inbreeding and their associated use to manage the genome at the herd level, however, still need to be developed. We have reviewed topics related to inbreeding, measures of relatedness, genetic diversity and methods to manage populations at the genomic level, and we discuss future challenges related to managing populations through implementing genomic methods at the herd and population levels.}, number={8}, journal={JOURNAL OF DAIRY SCIENCE}, author={Howard, Jeremy T. and Pryce, Jennie E. and Baes, Christine and Maltecca, Christian}, year={2017}, month={Aug}, pages={6009–6024} }
 @inbook{isik_holland_maltecca_2017, title={Multi Environmental Trials}, ISBN={9783319551753 9783319551777}, url={http://dx.doi.org/10.1007/978-3-319-55177-7_8}, DOI={10.1007/978-3-319-55177-7_8}, booktitle={Genetic Data Analysis for Plant and Animal Breeding}, publisher={Springer International Publishing}, author={Isik, Fikret and Holland, James and Maltecca, Christian}, year={2017}, pages={227–262} }
 @inbook{isik_holland_maltecca_2017, title={Multivariate Models}, ISBN={9783319551753 9783319551777}, url={http://dx.doi.org/10.1007/978-3-319-55177-7_6}, DOI={10.1007/978-3-319-55177-7_6}, booktitle={Genetic Data Analysis for Plant and Animal Breeding}, publisher={Springer International Publishing}, author={Isik, Fikret and Holland, James and Maltecca, Christian}, year={2017}, pages={165–201} }
 @article{moretti_biffani_tiezzi_maltecca_chessa_bozzi_2017, title={Rumination time as a potential predictor of common diseases in high-productive Holstein dairy cows}, volume={84}, ISSN={0022-0299 1469-7629}, url={http://dx.doi.org/10.1017/S0022029917000619}, DOI={10.1017/S0022029917000619}, abstractNote={We examined the hypothesis that rumination time (RT) could serve as a useful predictor of various common diseases of high producing dairy cows and hence improve herd management and animal wellbeing. We measured the changes in rumination time (RT) in the days before the recording of diseases (specifically: mastitis, reproductive system diseases, locomotor system issues, and gastroenteric diseases). We built predictive models to assess the association between RT and these diseases, using the former as the outcome variable, and to study the effects of the latter on the former. The average Pseudo-R 2 of the fitted models was moderate to low, and this could be due to the fact that RT is influenced by other additional factors which have a greater effect than the predictors used here. Although remaining in a moderate-to-low range, the average Pseudo-R 2 of the models regarding locomotion issues and gastroenteric diseases was higher than the others, suggesting the greater effect of these diseases on RT. The results are encouraging, but further work is needed if these models are to become useful predictors.}, number={4}, journal={Journal of Dairy Research}, publisher={Cambridge University Press (CUP)}, author={Moretti, Riccardo and Biffani, Stefano and Tiezzi, Francesco and Maltecca, Christian and Chessa, Stefania and Bozzi, Riccardo}, year={2017}, month={Nov}, pages={385–390} }
 @inbook{isik_holland_maltecca_2017, title={Spatial Analysis}, ISBN={9783319551753 9783319551777}, url={http://dx.doi.org/10.1007/978-3-319-55177-7_7}, DOI={10.1007/978-3-319-55177-7_7}, booktitle={Genetic Data Analysis for Plant and Animal Breeding}, publisher={Springer International Publishing}, author={Isik, Fikret and Holland, James and Maltecca, Christian}, year={2017}, pages={203–226} }
 @article{lu_jiao_tiezzi_knauer_huang_gray_maltecca_2017, title={The relationship between different measures of feed efficiency and feeding behavior traits in Duroc pigs}, volume={95}, DOI={10.2527/jas2017.1509}, number={8}, journal={Journal of Animal Science}, author={Lu, D. and Jiao, S. and Tiezzi, F. and Knauer, M. and Huang, Y. and Gray, K. A. and maltecca}, year={2017}, pages={3370–3380} }
 @article{lu_jiao_tiezzi_knauer_huang_gray_maltecca_2017, title={The relationship between different measures of feed efficiency and feeding behavior traits in Duroc pigs}, volume={95}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85026859866&partnerID=MN8TOARS}, DOI={10.2527/jas.2017.1509}, abstractNote={Utilization of feed in livestock species consists of a wide range of biological processes, and therefore, its efficiency can be expressed in various ways, including direct measurement, such as daily feed intake, as well as indicator measures, such as feeding behavior. Measuring feed efficiency is important to the swine industry, and its accuracy can be enhanced by using automated feeding systems, which record feed intake and associated feeding behavior of individual animals. Each automated feeder space is often shared among several pigs and therefore raises concerns about social interactions among pen mates with regard to feeding behavior. The study herein used a data set of 14,901 Duroc boars with individual records on feed intake, feeding behavior, and other off-test traits. These traits were modeled with and without the random spatial effect of Pen_Room, a concatenation of room and pen, or random social interaction among pen mates. The nonheritable spatial effect of common Pen-Room was observed for traits directly measuring feed intake and accounted for up to 13% of the total phenotypic variance in the average daily feeding rate. The social interaction effect explained larger proportions of phenotypic variation in all the traits studied, with the highest being 59% for ADFI in the group of feeding behaviors, 73% for residual feed intake (RFI; RFI4 and RFI6) in the feed efficiency traits, and 69% for intramuscular fat percentage in the off-test traits. After accounting for the social interaction effect, residual BW gain and RFI and BW gain (RIG) were found to have the heritability of 0.38 and 0.18, respectively, and had strong genetic correlations with growth and off-test traits. Feeding behavior traits were found to be moderately heritable, ranging from 0.14 (ADFI) to 0.52 (average daily occupation time), and some of them were strongly correlated with feed efficiency measures; for example, there was a genetic correlation of 0.88 between ADFI and RFI6. Our work suggested that accounting for the social common pen effect was important for estimating genetic parameters of traits recorded by the automated feeding system. Residual BW gain and RIG appeared to be two robust measures of feed efficiency. Feeding behavior measures are worth further investigation as indicators of feed efficiency.}, number={8}, journal={Journal of Animal Science}, author={Lu, D. and Jiao, S. and Tiezzi, F. and Knauer, M. and Huang, Y. and Gray, K.A. and Maltecca, C.}, year={2017}, pages={3370–3380} }
 @inbook{isik_holland_maltecca_2017, title={Variance Modeling in ASReml}, ISBN={9783319551753 9783319551777}, url={http://dx.doi.org/10.1007/978-3-319-55177-7_3}, DOI={10.1007/978-3-319-55177-7_3}, booktitle={Genetic Data Analysis for Plant and Animal Breeding}, publisher={Springer International Publishing}, author={Isik, Fikret and Holland, James and Maltecca, Christian}, year={2017}, pages={87–106} }
 @article{howard_tiezzi_huang_gray_maltecca_2016, title={028 The use of alternative genomic metrics in swine nucleus herds to manage the diversity of purebred and crossbred animals}, volume={94}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.2527/msasas2016-028}, DOI={10.2527/msasas2016-028}, abstractNote={In livestock breeding populations, regions of the genome with a high frequency of runs of homozygosity (ROH) have reduced diversity. Metrics that reduce ROH frequency may provide an attractive way to manage the diversity and ensure long-term gains while avoiding inbreeding accumulation. The 2 objectives of the current work were to characterize the frequency of ROH in Landrace (LR), Large White (LW), and their cross (LR × LW) through a combination of real and simulated genotypes and to determine the impact of optimizing different inbreeding metrics for nucleus and crossbred populations. A ROH statistic (ROH5Mb: “1” if SNP was in ROH of 5 Mb and “0” otherwise) was calculated across the genome for genotyped LR (n = 1206) and LW (n = 1349) dams and simulated crossbred genotypes derived from mating 81 LR sires to 100 LW dams. High ROH5Mb frequencies were declared for a contiguous set of SNP within the top 5%. In addition to random mating, pedigree, genomic, or shared ROH-based relationship matrices were used to minimize relationships between mating pairs within breed and the latter 2 were in crossings. Mating plans with 25 sires available with a restriction on the maximum number of mating were devised for within-breed and across-breed mating populations of 625 and 1250 dams, respectively. Each plan was replicated 25 times. Regions of shared high ROH5Mb frequency were found on SSC1, SSC3, and SSC14 and regions with a high ROH5Mb frequency within a breed were found to persist in the crossbreeds. Runs of homozygosity and genomic-based relationship matrices decreased the proportion of the overall genome in a ROH by 2.45- and 2.19-fold when compared with pedigree-based relationships. Furthermore, the use of pedigree-based relationships was not able to decrease regions with high ROH5Mb frequency more heavily than ROH- or G-based relationships. The use of alternative genomic relatedness metrics such as ROH allow for relationships to be minimized for targeted regions of low diversity.}, number={suppl_2}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Howard, J. T. and Tiezzi, F. and Huang, Y. and Gray, K. A. and Maltecca, C.}, year={2016}, month={Apr}, pages={13–13} }
 @article{bryan_maltecca_gray_huang_tiezzi_2016, title={029 Mitigating the effect of seasonality on sow reproductive performance using genetic selection}, volume={94}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.2527/msasas2016-029}, DOI={10.2527/msasas2016-029}, abstractNote={The objective of the study was to estimate variance components and inbreeding effect for sow reproductive performance considered as different traits according to the season of conception. Reproductive and pedigree data were obtained for 18,648 Landrace litters from nucleus farms in Texas (n = 1) and North Carolina (n = 2). Traits included number born alive (NBA), total number born (TNB), number born dead (BD), and fetal loss (FL) calculated as BD/TNB. Season of conception was defined as winter (December–February), spring (March–May), summer (June–August), and fall (September–November). Variance components and genetic correlations were estimated with gibbs1f90 using a multiple-trait model with trait by season represented in the model. The model included fixed effects of contemporary group (herd by year) and parity and the random additive genetic effect of sow. For the inbreeding estimates, level of inbreeding was also included in the model as a covariate. Heritability estimates were greatest for NBA, TNB, and BD for conception in summer months with estimates of 0.198, 0.208, and 0.165, respectively, and for FL for spring conception with an estimate of 0.172. Heritability estimates were lowest for spring conception for NBA (0.107) and TNB (0.086), for fall conception for BD (0.122), and for summer conception for FL (0.138). Genetic correlations were greatest for NBA (0.946) and TNB (0.934) in spring and winter, and the relationship between spring and fall for BD (0.987) and PWM (0.935). Genetic correlations were lowest for NBA (0.794) and TNB (0.733) for spring and summer, for spring and winter for BD (0.817), and for fall and winter for FL (0.823). The estimates and SE of inbreeding depression for each trait by season are shown in Table 029. The results suggest that NBA, TNB, BD, and FL should be treated as different traits according to season of conception, and summer performance appears to be determined by a different genetic background compared with the other seasons. Selection for increased performance during the summer months may be a more effective method to mitigate seasonal infertility than selection for performance across the year. It is also suggested that increased inbreeding may be especially detrimental for sows conceiving during the summer and fall season. Estimates and SE of inbreeding depression for each trait by season Estimates and SE of inbreeding depression for each trait by season}, number={suppl_2}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Bryan, M. R. and Maltecca, C. and Gray, K. A. and Huang, Y. and Tiezzi, F.}, year={2016}, month={Apr}, pages={14–14} }
 @article{howard_tiezzi_pryce_maltecca_2016, title={0301 A combined coalescence forward in time simulator software for pedigreed populations undergoing selection for complex traits}, volume={94}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.2527/jam2016-0301}, DOI={10.2527/jam2016-0301}, abstractNote={The use of marker information in animal breeding has recently been an active area of research and has been incorporated in selection decisions and as a tool to control inbreeding across a variety of species. There is yet still much to be learned on the optimal way to use marker information to select animals and manage the genome of a population that is undergoing selection for complex traits that have a traditional quantitative basis (i.e., yield) and/or fitness basis (i.e., number of progeny). We have developed a combined coalescence and forward-in-time simulator for complex traits and populations. The simulator is performed in two stages. In the first stage whole-genome SNP data is read in ms format and is utilized to generate founder individuals and associated SNP marker panels ranging in size from thousands to millions of SNP. During this stage a wide variety of trait architectures can be generated with additive and dominance effects for both a traditional quantitative trait and fitness along with genomic covariance among traits. The second stage generates new individuals across generations based on a variety of selection scenarios. The selection stage can be performed using a wide variety of relationship matrices including pedigree, independent markers, haplotypes, or run of homozygosity based haplotypes. Relationship matrices and their associated inverse are generated using computationally efficient algorithms based on updating matrices from previous generations. Complex population structures can be generated that allow for a differential contribution of gametes to the next generation as well as mating constraints. To demonstrate the program, we present a small application that mimics a dairy cattle and swine population to describe some of the metrics that are generated. Scenarios were generated based on a 12,000 SNP marker panel spread across 3 chromosomes and a population size of 650 animals (sires = 50; dams = 600) per generation. A scenario with selection on a quantitative trait occurring for 5 generations and breeding values estimated from pedigree or independent SNP had a running time for the dairy cattle scenario of 4.85 and 5.82 min, respectively. Geno-Driver allows for a wide range of selection strategies to be evaluated in the presence of a fitness trait and is available at https://github.com/jeremyhoward/GenoDriver.}, number={suppl_5}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Howard, J. T. and Tiezzi, F. and Pryce, J. E. and Maltecca, C.}, year={2016}, month={Oct}, pages={143–144} }
 @article{moretti_bozzi_maltecca_tiezzi_chessa_bar_biffani_2016, title={0387 Daily rumination time in Italian Holstein cows: Heritability and correlation with milk production}, volume={94}, ISSN={0021-8812 1525-3163}, url={http://dx.doi.org/10.2527/jam2016-0387}, DOI={10.2527/jam2016-0387}, number={suppl_5}, journal={Journal of Animal Science}, publisher={Oxford University Press (OUP)}, author={Moretti, R. and Bozzi, R. and Maltecca, C. and Tiezzi, F. and Chessa, S. and Bar, D. and Biffani, S.}, year={2016}, month={Oct}, pages={187–188} }
 @article{howard_tiezzi_huang_gray_maltecca_2016, title={A method for the identification of unfavorable haplotypes contained within runs of homozygosity that impact fitness traits and its application to different swine nucleus lines.}, volume={94}, ISSN={["1525-3163"]}, DOI={10.2527/jas2016.94supplement426a}, number={S4}, journal={JOURNAL OF ANIMAL SCIENCE}, author={Howard, J. T. and Tiezzi, F. and Huang, Y. and Gray, K. A. and Maltecca, C.}, year={2016}, month={Sep}, pages={26–27} }
 @article{gaddis_cole_clay_maltecca_2016, title={Benchmarking dairy herd health status using routinely recorded herd summary data}, volume={99}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84955679208&partnerID=MN8TOARS}, DOI={10.3168/jds.2015-9840}, abstractNote={Genetic improvement of dairy cattle health through the use of producer-recorded data has been determined to be feasible. Low estimated heritabilities indicate that genetic progress will be slow. Variation observed in lowly heritable traits can largely be attributed to nongenetic factors, such as the environment. More rapid improvement of dairy cattle health may be attainable if herd health programs incorporate environmental and managerial aspects. More than 1,100 herd characteristics are regularly recorded on farm test-days. We combined these data with producer-recorded health event data, and parametric and nonparametric models were used to benchmark herd and cow health status. Health events were grouped into 3 categories for analyses: mastitis, reproductive, and metabolic. Both herd incidence and individual incidence were used as dependent variables. Models implemented included stepwise logistic regression, support vector machines, and random forests. At both the herd and individual levels, random forest models attained the highest accuracy for predicting health status in all health event categories when evaluated with 10-fold cross-validation. Accuracy (SD) ranged from 0.61 (0.04) to 0.63 (0.04) when using random forest models at the herd level. Accuracy of prediction (SD) at the individual cow level ranged from 0.87 (0.06) to 0.93 (0.001) with random forest models. Highly significant variables and key words from logistic regression and random forest models were also investigated. All models identified several of the same key factors for each health event category, including movement out of the herd, size of the herd, and weather-related variables. We concluded that benchmarking health status using routinely collected herd data is feasible. Nonparametric models were better suited to handle this complex data with numerous variables. These data mining techniques were able to perform prediction of health status and could add evidence to personal experience in herd management.}, number={2}, journal={JOURNAL OF DAIRY SCIENCE}, author={Gaddis, K. L. Parker and Cole, J. B. and Clay, J. S. and Maltecca, C.}, year={2016}, month={Feb}, pages={1298–1314} }
 @article{dhakal_tiezzi_clay_maltecca_2016, title={Causal relationships between clinical mastitis events, milk yields and lactation persistency in US Holsteins}, volume={189}, ISSN={["1878-0490"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84965172115&partnerID=MN8TOARS}, DOI={10.1016/j.livsci.2016.04.015}, abstractNote={Complex relationships exist between udder susceptibility to mastitis and milk production traits. Identifying causal association between these traits could help to disentangle these complex relationships. The main objective of the study was to use producer-recorded health data to examine the causal relationship between mastitis events, milk yield and lactation persistency. A total of 48,058 first lactation cows, daughters of 2213 Holstein bulls and raised across 207 herds were analyzed using structural equation models. Traits included in the dataset were mastitis events and average test day milk yields recorded in three different periods: period 1 (5–60 DIM), period 2 (61–120 DIM) and period 3 (121–180 DIM). In addition, lactation persistency was also included. A subset including 28,867 daughters of 1809 Holstein sires having both first and second lactation across 201 herds was further investigated. In these datasets, mastitis events were defined on a lactation basis as binary trait; either a cow was assigned a score of 1 (had a mastitis event in that lactation) or a score of 0 (healthy) for that particular lactation, regardless of the time of occurrence. Total milk yield from first and second lactation were also included in the analyses. We estimated negative structural coefficient (−0.032) between clinical mastitis and test day milk production in early lactation period suggesting that mastitis results in a direct decline in milk production in early lactation. We nonetheless elicited little impact of mastitis on test day milk production of mid and late lactation periods, and on milk yield lactation persistency. Likewise the positive estimate of the structural coefficient (0.123) from mastitis event in first lactation to second lactation suggests an increased risk of mastitis in second lactation if a case of mastitis occurs in the primiparous cow. Heritability estimates obtained from the structural equation models were low for mastitis (ranged 0.04 to 0.07), and negative genetic correlations were found between mastitis events and milk yield. The study illustrates how mastitis events and production are causally linked. Through the use of structural equation models we elicited the causal effect among mastitis and production traits that evolve over the course of cow life.}, journal={LIVESTOCK SCIENCE}, publisher={Elsevier}, author={Dhakal, K. and Tiezzi, F. and Clay, J. S. and Maltecca, C.}, year={2016}, month={Jul}, pages={8–16} }
 @article{howard_tiezzi_huang_gray_maltecca_2016, title={Characterization and management of long runs of homozygosity in parental nucleus lines and their associated crossbred progeny}, volume={48}, ISSN={["1297-9686"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84996878800&partnerID=MN8TOARS}, DOI={10.1186/s12711-016-0269-y}, abstractNote={In nucleus populations, regions of the genome that have a high frequency of runs of homozygosity (ROH) occur and are associated with a reduction in genetic diversity, as well as adverse effects on fitness. It is currently unclear whether, and to what extent, ROH stretches persist in the crossbred genome and how genomic management in the nucleus population might impact low diversity regions and its implications on the crossbred genome.We calculated a ROH statistic based on lengths of 5 (ROH5) or 10 (ROH10) Mb across the genome for genotyped Landrace (LA), Large White (LW) and Duroc (DU) dams. We simulated crossbred dam (LA × LW) and market [DU × (LA × LW)] animal genotypes based on observed parental genotypes and the ROH frequency was tabulated. We conducted a simulation using observed genotypes to determine the impact of minimizing parental relationships on multiple diversity metrics within nucleus herds, i.e. pedigree-(A), SNP-by-SNP relationship matrix or ROH relationship matrix. Genome-wide metrics included, pedigree inbreeding, heterozygosity and proportion of the genome in ROH of at least 5 Mb. Lastly, the genome was split into bins of increasing ROH5 frequency and, within each bin, heterozygosity, ROH5 and length (Mb) of ROH were evaluated.We detected regions showing high frequencies of either ROH5 and/or ROH10 across both LW and LA on SSC1, SSC4, and SSC14, and across all breeds on SSC9. Long haplotypes were shared across parental breeds and thus, regions of ROH persisted in crossbred animals. Averaged across replicates and breeds, progeny had higher levels of heterozygosity (0.0056 ± 0.002%) and lower proportion of the genome in a ROH of at least 5 Mb (-0.015 ± 0.003%) than their parental genomes when genomic relationships were constrained, while pedigree relationships resulted in negligible differences at the genomic level. Across all breeds, only genomic data was able to target low diversity regions.We show that long stretches of ROH present in the parents persist in crossbred animals. Furthermore, compared to using pedigree relationships, using genomic information to constrain parental relationships resulted in maintaining more genetic diversity and more effectively targeted low diversity regions.}, number={1}, journal={GENETICS SELECTION EVOLUTION}, publisher={BioMed Central}, author={Howard, Jeremy T. and Tiezzi, Francesco and Huang, Yijian and Gray, Kent A. and Maltecca, Christian}, year={2016}, month={Nov} }
 @article{jiao_tiezzi_huang_gray_maltecca_2016, title={The use of multiple imputation for the accurate measurements of individual feed intake by electronic feeders}, volume={94}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84975804514&partnerID=MN8TOARS}, DOI={10.2527/jas.2015-9667}, abstractNote={Obtaining accurate individual feed intake records is the key first step in achieving genetic progress toward more efficient nutrient utilization in pigs. Feed intake records collected by electronic feeding systems contain errors (erroneous and abnormal values exceeding certain cutoff criteria), which are due to feeder malfunction or animal-feeder interaction. In this study, we examined the use of a novel data-editing strategy involving multiple imputation to minimize the impact of errors and missing values on the quality of feed intake data collected by an electronic feeding system. Accuracy of feed intake data adjustment obtained from the conventional linear mixed model (LMM) approach was compared with 2 alternative implementations of multiple imputation by chained equation, denoted as MI (multiple imputation) and MICE (multiple imputation by chained equation). The 3 methods were compared under 3 scenarios, where 5, 10, and 20% feed intake error rates were simulated. Each of the scenarios was replicated 5 times. Accuracy of the alternative error adjustment was measured as the correlation between the true daily feed intake (DFI; daily feed intake in the testing period) or true ADFI (the mean DFI across testing period) and the adjusted DFI or adjusted ADFI. In the editing process, error cutoff criteria are used to define if a feed intake visit contains errors. To investigate the possibility that the error cutoff criteria may affect any of the 3 methods, the simulation was repeated with 2 alternative error cutoff values. Multiple imputation methods outperformed the LMM approach in all scenarios with mean accuracies of 96.7, 93.5, and 90.2% obtained with MI and 96.8, 94.4, and 90.1% obtained with MICE compared with 91.0, 82.6, and 68.7% using LMM for DFI. Similar results were obtained for ADFI. Furthermore, multiple imputation methods consistently performed better than LMM regardless of the cutoff criteria applied to define errors. In conclusion, multiple imputation is proposed as a more accurate and flexible method for error adjustments in feed intake data collected by electronic feeders.}, number={2}, journal={Journal of Animal Science}, author={Jiao, S. and Tiezzi, F. and Huang, Y. and Gray, K.A. and Maltecca, C.}, year={2016}, pages={824–832} }
 @article{tiezzi_parker-gaddis_cole_clay_maltecca_2015, title={A Genome-Wide Association Study for Clinical Mastitis in First Parity US Holstein Cows Using Single-Step Approach and Genomic Matrix Re-Weighting Procedure}, volume={10}, ISSN={["1932-6203"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84922687710&partnerID=MN8TOARS}, DOI={10.1371/journal.pone.0114919}, abstractNote={Clinical mastitis (CM) is one of the health disorders with large impacts on dairy farming profitability and animal welfare. The objective of this study was to perform a genome-wide association study (GWAS) for CM in first-lactation Holstein. Producer-recorded mastitis event information for 103,585 first-lactation cows were used, together with genotype information on 1,361 bulls from the Illumina BovineSNP50 BeadChip. Single-step genomic-BLUP methodology was used to incorporate genomic data into a threshold-liability model. Association analysis confirmed that CM follows a highly polygenic mode of inheritance. However, 10-adjacent-SNP windows showed that regions on chromosomes 2, 14 and 20 have impacts on genetic variation for CM. Some of the genes located on chromosome 14 (LY6K, LY6D, LYNX1, LYPD2, SLURP1, PSCA) are part of the lymphocyte-antigen-6 complex (LY6) known for its neutrophil regulation function linked to the major histocompatibility complex. Other genes on chromosome 2 were also involved in regulating immune response (IFIH1, LY75, and DPP4), or are themselves regulated in the presence of specific pathogens (ITGB6, NR4A2). Other genes annotated on chromosome 20 are involved in mammary gland metabolism (GHR, OXCT1), antibody production and phagocytosis of bacterial cells (C6, C7, C9, C1QTNF3), tumor suppression (DAB2), involution of mammary epithelium (OSMR) and cytokine regulation (PRLR). DAVID enrichment analysis revealed 5 KEGG pathways. The JAK-STAT signaling pathway (cell proliferation and apoptosis) and the 'Cytokine-cytokine receptor interaction' (cytokine and interleukines response to infectious agents) are co-regulated and linked to the 'ABC transporters' pathway also found here. Gene network analysis performed using GeneMania revealed a co-expression network where 665 interactions existed among 145 of the genes reported above. Clinical mastitis is a complex trait and the different genes regulating immune response are known to be pathogen-specific. Despite the lack of information in this study, candidate QTL for CM were identified in the US Holstein population.}, number={2}, journal={PLOS ONE}, author={Tiezzi, Francesco and Parker-Gaddis, Kristen L. and Cole, John B. and Clay, John S. and Maltecca, Christian}, year={2015}, month={Feb} }
 @article{tiezzi_maltecca_2015, title={Accounting for trait architecture in genomic predictions of US Holstein cattle using a weighted realized relationship matrix}, volume={47}, ISSN={["1297-9686"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84961377449&partnerID=MN8TOARS}, DOI={10.1186/s12711-015-0100-1}, abstractNote={Genomic BLUP (GBLUP) can predict breeding values for non-phenotyped individuals based on the identity-by-state genomic relationship matrix (G). The G matrix can be constructed from thousands of markers spread across the genome. The strongest assumption of G and consequently of GBLUP is that all markers contribute equally to the genetic variance of a trait. This assumption is violated for traits that are controlled by a small number of quantitative trait loci (QTL) or individual QTL with large effects. In this paper, we investigate the performance of using a weighted genomic relationship matrix (wG) that takes into consideration the genetic architecture of the trait in order to improve predictive ability for a wide range of traits. Multiple methods were used to calculate weights for several economically relevant traits in US Holstein dairy cattle. Predictive performance was tested by k-means cross-validation. Relaxing the GBLUP assumption of equal marker contribution by increasing the weight that is given to a specific marker in the construction of the trait-specific G resulted in increased predictive performance. The increase was strongest for traits that are controlled by a small number of QTL (e.g. fat and protein percentage). Furthermore, bias in prediction estimates was reduced compared to that resulting from the use of regular G. Even for traits with low heritability and lower general predictive performance (e.g. calving ease traits), weighted G still yielded a gain in accuracy. Genomic relationship matrices weighted by marker realized variance yielded more accurate and less biased predictions for traits regulated by few QTL. Genome-wide association analyses were used to derive marker weights for creating weighted genomic relationship matrices. However, this can be cumbersome and prone to low stability over generations because of erosion of linkage disequilibrium between markers and QTL. Future studies may include other sources of information, such as functional annotation and gene networks, to better exploit the genetic architecture of traits and produce more stable predictions.}, number={1}, journal={GENETICS SELECTION EVOLUTION}, publisher={BioMed Central}, author={Tiezzi, Francesco and Maltecca, Christian}, year={2015}, month={Apr} }
 @inproceedings{gaddis_cole_clay_maltecca_2015, place={Victoria, Australia}, title={Benchmarking cow health status with dairy herd summary data}, booktitle={Proceedings of the 21st Conference of the Association for the Advancement of Animal Breeding and Genetics (AAABG)}, publisher={Association for the Advancement of Animal Breeding and Genetics}, author={Gaddis, K.L.P. and Cole, J.B. and Clay, J.S. and Maltecca, C.}, year={2015}, pages={366–369} }
 @article{tiezzi_valente_cassandro_maltecca_2015, title={Causal relationships between milk quality and coagulation properties in Italian Holstein-Friesian dairy cattle}, volume={47}, ISSN={["1297-9686"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84938963215&partnerID=MN8TOARS}, DOI={10.1186/s12711-015-0123-7}, abstractNote={Recently, selection for milk technological traits was initiated in the Italian dairy cattle industry based on direct measures of milk coagulation properties (MCP) such as rennet coagulation time (RCT) and curd firmness 30 min after rennet addition (a30) and on some traditional milk quality traits that are used as predictors, such as somatic cell score (SCS) and casein percentage (CAS). The aim of this study was to shed light on the causal relationships between traditional milk quality traits and MCP. Different structural equation models that included causal effects of SCS and CAS on RCT and a30 and of RCT on a30 were implemented in a Bayesian framework.Our results indicate a non-zero magnitude of the causal relationships between the traits studied. Causal effects of SCS and CAS on RCT and a30 were observed, which suggests that the relationship between milk coagulation ability and traditional milk quality traits depends more on phenotypic causal pathways than directly on common genetic influence. While RCT does not seem to be largely controlled by SCS and CAS, some of the variation in a30 depends on the phenotypes of these traits. However, a30 depends heavily on coagulation time. Our results also indicate that, when direct effects of SCS, CAS and RCT are considered simultaneously, most of the overall genetic variability of a30 is mediated by other traits.This study suggests that selection for RCT and a30 should not be performed on correlated traits such as SCS or CAS but on direct measures because the ability of milk to coagulate is improved through the causal effect that the former play on the latter, rather than from a common source of genetic variation. Breaking the causal link (e.g. standardizing SCS or CAS before the milk is processed into cheese) would reduce the impact of the improvement due to selective breeding. Since a30 depends heavily on RCT, the relative emphasis that is put on this trait should be reconsidered and weighted for the fact that the pure measure of a30 almost double-counts RCT.}, number={1}, journal={GENETICS SELECTION EVOLUTION}, publisher={BioMed Central}, author={Tiezzi, Francesco and Valente, Bruno D. and Cassandro, Martino and Maltecca, Christian}, year={2015}, month={May} }
 @article{howard_maltecca_haile-mariam_hayes_pryce_2015, title={Characterizing homozygosity across United States, New Zealand and Australian Jersey cow and bull populations}, volume={16}, ISSN={["1471-2164"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85019258044&partnerID=MN8TOARS}, DOI={10.1186/s12864-015-1352-4}, abstractNote={Dairy cattle breeding objectives are in general similar across countries, but environment and management conditions may vary, giving rise to slightly different selection pressures applied to a given trait. This potentially leads to different selection pressures to loci across the genome that, if large enough, may give rise to differential regions with high levels of homozygosity. The objective of this study was to characterize differences and similarities in the location and frequency of homozygosity related measures of Jersey dairy cows and bulls from the United States (US), Australia (AU) and New Zealand (NZ). The populations consisted of a subset of genotyped Jersey cows born in US (n = 1047) and AU (n = 886) and Jersey bulls progeny tested from the US (n = 736), AU (n = 306) and NZ (n = 768). Differences and similarities across populations were characterized using a principal component analysis (PCA) and a run of homozygosity (ROH) statistic (ROH45), which counts the frequency of a single nucleotide polymorphism (SNP) being in a ROH of at least 45 SNP. Regions that exhibited high frequencies of ROH45 and those that had significantly different ROH45 frequencies between populations were investigated for their association with milk yield traits. Within sex, the PCA revealed slight differentiation between the populations, with the greatest occurring between the US and NZ bulls. Regions with high levels of ROH45 for all populations were detected on BTA3 and BTA7 while several other regions differed in ROH45 frequency across populations, the largest number occurring for the US and NZ bull contrast. In addition, multiple regions with different ROH45 frequencies across populations were found to be associated with milk yield traits. Multiple regions exhibited differential ROH45 across AU, NZ and US cow and bull populations, an interpretation is that locations of the genome are undergoing differential directional selection. Two regions on BTA3 and BTA7 had high ROH45 frequencies across all populations and will be investigated further to determine the gene(s) undergoing directional selection.}, number={1}, journal={BMC GENOMICS}, author={Howard, Jeremy T. and Maltecca, Christian and Haile-Mariam, Mekonnen and Hayes, Ben J. and Pryce, Jennie E.}, year={2015}, month={Mar} }
 @article{tiezzi_maltecca_cecchinato_bittante_2015, title={Comparison between different statistical models for the prediction of direct genetic component on embryo establishment and survival in Italian Brown Swiss dairy cattle}, volume={180}, ISSN={["1878-0490"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84941940825&partnerID=MN8TOARS}, DOI={10.1016/j.livsci.2015.06.029}, abstractNote={The aims of this study were to infer variance components and heritability for the direct component on embryo establishment and survival related traits and to compare different statistical models in terms of goodness-of-fit and predictive ability. Embryo establishment and survival (EES) was defined as the outcome of an AI event, its direct effect was represented as the effect of the service sire from which semen was taken. Indicators of EES were calving per service (CS) and non-return at 56 d after service (NR56). Insemination records from the Italian Brown Swiss population reared in the Alps were used. Data included 124,206 inseminations performed by 86 technicians on 28,873 cows in 1400 herds. Services were recorded from 1999 to 2008. Linear-sire, linear-animal, threshold-sire, and threshold-animal models were used to estimate (co)variance components for CS and NR56. Four levels of complexity within each model were tested, so that 16 different models were compared for each of the two fertility traits. Comparison was assessed on the basis of the goodness-of-fit and predictive ability. Paternal half-sibs groups were created as average outcome of the inseminations from a given service sire. Goodness-of-fit was evaluated by regressing the service sire estimated breeding value from each model to paternal half-sibs average CS or NR56. Predictive ability was assessed through sums of chi-squared and percentage of wrong predictions. Predictors were the respective service sire’s estimated breeding values constructed on a reduced (independent) training dataset, including years from 1999 to 2005, and predictands were the paternal half-sibs means for every bull in the remaining years (2006–2008). Prediction of EES was considered differently according to whether service sires had observations in the training dataset (prediction of proven bulls) or they had not (prediction of young bulls). Estimates of heritability ranged from 0.011 to 0.119 for CS, and from 0.005 to 0.054 for NR56. In general, threshold models explained a larger proportion of additive genetic variance than linear models, and animal models yielded higher heritabilities than sire models. Calving per service was much more predictable than NR56, but no significant differences were found among models. Although heritabilities were low, the prediction of future EES of a paternal half-sib group is feasible.}, journal={LIVESTOCK SCIENCE}, publisher={Elsevier}, author={Tiezzi, F. and Maltecca, C. and Cecchinato, A. and Bittante, G.}, year={2015}, month={Oct}, pages={6–13} }
 @article{howard_o’nan_maltecca_baynes_ashwell_2015, title={Differential Gene Expression across Breed and Sex in Commercial Pigs Administered Fenbendazole and Flunixin Meglumine}, volume={10}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0137830}, DOI={10.1371/journal.pone.0137830}, abstractNote={Characterizing the variability in transcript levels across breeds and sex in swine for genes that play a role in drug metabolism may shed light on breed and sex differences in drug metabolism. The objective of the study is to determine if there is heterogeneity between swine breeds and sex in transcript levels for genes previously shown to play a role in drug metabolism for animals administered flunixin meglumine or fenbendazole. Crossbred nursery female and castrated male pigs (n = 169) spread across 5 groups were utilized. Sires (n = 15) of the pigs were purebred Duroc, Landrace, Yorkshire or Hampshire boars mated to a common sow population. Animals were randomly placed into the following treatments: no drug (control), flunixin meglumine, or fenbendazole. One hour after the second dosing, animals were sacrificed and liver samples collected. Quantitative Real-Time PCR was used to measure liver gene expression of the following genes: SULT1A1, ABCB1, CYP1A2, CYP2E1, CYP3A22 and CYP3A29. The control animals were used to investigate baseline transcript level differences across breed and sex. Post drug administration transcript differences across breed and sex were investigated by comparing animals administered the drug to the controls. Contrasts to determine fold change were constructed from a model that included fixed and random effects within each drug. Significant (P-value <0.007) basal transcript differences were found across breeds for SULT1A1, CYP3A29 and CYP3A22. Across drugs, significant (P-value <0.0038) transcript differences existed between animals given a drug and controls across breeds and sex for ABCB1, PS and CYP1A2. Significant (P <0.0038) transcript differences across breeds were found for CYP2E1 and SULT1A1 for flunixin meglumine and fenbendazole, respectively. The current analysis found transcript level differences across swine breeds and sex for multiple genes, which provides greater insight into the relationship between flunixin meglumine and fenbendazole and known drug metabolizing genes.}, number={9}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Howard, Jeremy T. and O’Nan, Audrey T. and Maltecca, Christian and Baynes, Ronald E. and Ashwell, Melissa S.}, editor={Kobeissy, Firas HEditor}, year={2015}, month={Sep}, pages={e0137830} }
 @article{morgante_sørensen_sorensen_maltecca_mackay_2015, title={Genetic Architecture of Micro-Environmental Plasticity in Drosophila melanogaster}, volume={5}, ISSN={2045-2322}, url={http://dx.doi.org/10.1038/srep09785}, DOI={10.1038/srep09785}, abstractNote={Abstract Individuals of the same genotype do not have the same phenotype for quantitative traits when reared under common macro-environmental conditions, a phenomenon called micro-environmental plasticity. Genetic variation in micro-environmental plasticity is assumed in models of the evolution of phenotypic variance and is important in applied breeding and personalized medicine. Here, we quantified genetic variation for micro-environmental plasticity for three quantitative traits in the inbred, sequenced lines of the Drosophila melanogaster Genetic Reference Panel. We found substantial genetic variation for micro-environmental plasticity for all traits, with broad sense heritabilities of the same magnitude or greater than those of trait means. Micro-environmental plasticity is not correlated with residual segregating variation, is trait-specific and has genetic correlations with trait means ranging from zero to near unity. We identified several candidate genes associated with micro-environmental plasticity of startle response, including Drosophila Hsp90 , setting the stage for future genetic dissection of this phenomenon.}, number={1}, journal={Scientific Reports}, publisher={Springer Science and Business Media LLC}, author={Morgante, Fabio and Sørensen, Peter and Sorensen, Daniel A. and Maltecca, Christian and Mackay, Trudy F. C.}, year={2015}, month={May} }
 @article{howard_jiao_tiezzi_huang_gray_maltecca_2015, title={Genome-wide association study on legendre random regression coefficients for the growth and feed intake trajectory on Duroc Boars}, volume={16}, ISSN={["1471-2156"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84930210856&partnerID=MN8TOARS}, DOI={10.1186/s12863-015-0218-8}, abstractNote={Feed intake and growth are economically important traits in swine production. Previous genome wide association studies (GWAS) have utilized average daily gain or daily feed intake to identify regions that impact growth and feed intake across time. The use of longitudinal models in GWAS studies, such as random regression, allows for SNPs having a heterogeneous effect across the trajectory to be characterized. The objective of this study is therefore to conduct a single step GWAS (ssGWAS) on the animal polynomial coefficients for feed intake and growth.Corrected daily feed intake (DFI Adj) and average daily weight measurements (DBW Avg) on 8981 (n=525,240 observations) and 5643 (n=283,607 observations) animals were utilized in a random regression model using Legendre polynomials (order=2) and a relationship matrix that included genotyped and un-genotyped animals. A ssGWAS was conducted on the animal polynomials coefficients (intercept, linear and quadratic) for animals with genotypes (DFIAdj: n=855; DBWAvg: n=590). Regions were characterized based on the variance of 10-SNP sliding windows GEBV (WGEBV). A bootstrap analysis (n=1000) was conducted to declare significance. Heritability estimates for the traits trajectory ranged from 0.34-0.52 to 0.07-0.23 for DBWAvg and DFIAdj, respectively. Genetic correlations across age classes were large and positive for both DBWAvg and DFIAdj, albeit age classes at the beginning had a small to moderate genetic correlation with age classes towards the end of the trajectory for both traits. The WGEBV variance explained by significant regions (P<0.001) for each polynomial coefficient ranged from 0.2-0.9 to 0.3-1.01% for DBWAvg and DFIAdj, respectively. The WGEBV variance explained by significant regions for the trajectory was 1.54 and 1.95% for DBWAvg and DFIAdj. Both traits identified candidate genes with functions related to metabolite and energy homeostasis, glucose and insulin signaling and behavior.We have identified regions of the genome that have an impact on the intercept, linear and quadratic terms for DBWAvg and DFIAdj. These results provide preliminary evidence that individual growth and feed intake trajectories are impacted by different regions of the genome at different times.}, number={1}, journal={BMC GENETICS}, publisher={BioMed Central Ltd}, author={Howard, Jeremy T. and Jiao, Shihui and Tiezzi, Francesco and Huang, Yijian and Gray, Kent A. and Maltecca, Christian}, year={2015}, month={May} }
 @article{gaddis_tiezzi_cole_clay_maltecca_2015, title={Genomic prediction of disease occurrence using producer-recorded health data: a comparison of methods}, volume={47}, ISSN={["1297-9686"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84928778631&partnerID=MN8TOARS}, DOI={10.1186/s12711-015-0093-9}, abstractNote={Genetic selection has been successful in achieving increased production in dairy cattle; however, corresponding declines in fitness traits have been documented. Selection for fitness traits is more difficult, since they have low heritabilities and are influenced by various non-genetic factors. The objective of this paper was to investigate the predictive ability of two-stage and single-step genomic selection methods applied to health data collected from on-farm computer systems in the U.S. Implementation of single-trait and two-trait sire models was investigated using BayesA and single-step methods for mastitis and somatic cell score. Variance components were estimated. The complete dataset was divided into training and validation sets to perform model comparison. Estimated sire breeding values were used to estimate the number of daughters expected to develop mastitis. Predictive ability of each model was assessed by the sum of χ 2 values that compared predicted and observed numbers of daughters with mastitis and the proportion of wrong predictions. According to the model applied, estimated heritabilities of liability to mastitis ranged from 0.05 (S D=0.02) to 0.11 (S D=0.03) and estimated heritabilities of somatic cell score ranged from 0.08 (S D=0.01) to 0.18 (S D=0.03). Posterior mean of genetic correlation between mastitis and somatic cell score was equal to 0.63 (S D=0.17). The single-step method had the best predictive ability. Conversely, the smallest number of wrong predictions was obtained with the univariate BayesA model. The best model fit was found for single-step and pedigree-based models. Bivariate single-step analysis had a better predictive ability than bivariate BayesA; however, the latter led to the smallest number of wrong predictions. Genomic data improved our ability to predict animal breeding values. Performance of genomic selection methods depends on a multitude of factors. Heritability of traits and reliability of genotyped individuals has a large impact on the performance of genomic evaluation methods. Given the current characteristics of producer-recorded health data, single-step methods have several advantages compared to two-step methods.}, number={1}, journal={GENETICS SELECTION EVOLUTION}, publisher={BioMed Central}, author={Gaddis, Kristen L. Parker and Tiezzi, Francesco and Cole, John B. and Clay, John S. and Maltecca, Christian}, year={2015}, month={May} }
 @article{bagnato_strillacci_pellegrino_schiavini_frigo_rossoni_fontanesi_maltecca_prinsen_dolezal_et al._2015, title={Identification and validation of copy number variants in Italian Brown Swiss dairy cattle using Illumina Bovine SNP50 Beadchip (R)}, volume={14}, ISSN={["1828-051X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84962009420&partnerID=MN8TOARS}, DOI={10.4081/ijas.2015.3900}, abstractNote={The determination of copy number variation (CNV) is very important for the evaluation of genomic traits in several species because they are a major source for the genetic variation, influencing gene expression, phenotypic variation, adaptation and the development of diseases.The aim of this study was to obtain a CNV genome map using the Illumina Bovine SNP50 BeadChip data of 651 bulls of the Italian Brown Swiss breed.PennCNV and SVS7 (Golden Helix) software were used for the detection of the CNVs and Copy Number Variation Regions (CNVRs).A total of 5,099 and 1,289 CNVs were identified with PennCNV and SVS7 software, respectively.These were grouped at the population level into 1101 (220 losses, 774 gains, 107 complex) and 277 (185 losses, 56 gains and 36 complex) CNVR.Ten of the selected CNVR were experimentally validated with a qPCR experiment.The GO and pathway analyses were conducted and they identified genes (false discovery rate corrected) in the CNVR related to biological process-es, cellular component, molecular function and metabolic pathways.Among those, we found the FCGR2B, PPARα, KATNAL1, DNAJC15, PTK2, TG, STAT family, NPM1, GATA2, LMF1, ECHS1 genes, already known in literature because of their association with various traits in cattle.Although there is variability in the CNVRs detection across methods and platforms, this study allowed the identification of CNVRs in Italian Brown Swiss, overlapping those already detected in other breeds and finding additional ones, thus producing new knowledge for association studies with traits of interest in cattle.}, number={3}, journal={ITALIAN JOURNAL OF ANIMAL SCIENCE}, author={Bagnato, A. and Strillacci, M. G. and Pellegrino, L. and Schiavini, F. and Frigo, E. and Rossoni, A. and Fontanesi, L. and maltecca and Prinsen, R. T. M. M. and Dolezal, M. A. and et al.}, year={2015}, pages={552-+} }
 @article{dhakal_tiezzi_clay_maltecca_2015, title={Inferring causal relationships between reproductive and metabolic health disorders and production traits in first-lactation US Holsteins using recursive models}, volume={98}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84925299358&partnerID=MN8TOARS}, DOI={10.3168/jds.2014-8448}, abstractNote={Health disorders in dairy cows have a substantial effect on the profitability of a dairy enterprise because of loss in milk sales, culling of unhealthy cows, and replacement costs. Complex relationships exist between health disorders and production traits. Understanding the causal structures among these traits may help us disentangle these complex relationships. The principal objective of this study was to use producer-recorded data to explore phenotypic and genetic relationships among reproductive and metabolic health disorders and production traits in first-lactation US Holsteins. A total of 77,004 first-lactation daughters' records of 2,183 sires were analyzed using recursive models. Health data contained information on reproductive health disorders [retained placenta (RP); metritis (METR)] and metabolic health disorders [ketosis (KETO); displaced abomasum (DA)]. Production traits included mean milk yield (MY) from early lactation (mean MY from 6 to 60 d in milk and from 61 to 120 d in milk), peak milk yield (PMY), day in milk of peak milk yield (PeakD), and lactation persistency (LP). Three different sets of traits were analyzed in which recursive effects from each health disorder on culling, recursive effects of one health disorder on another health disorder and on MY, and recursive effects of each health disorder on production traits, including PeakD, PMY, and LP, were assumed. Different recursive Gaussian-threshold and threshold models were implemented in a Bayesian framework. Estimates of the structural coefficients obtained between health disorders and culling were positive; on the liability scale, the structural coefficients ranged from 0.929 to 1.590, confirming that the presence of a health disorder increased culling. Positive recursive effects of RP to METR (0.117) and of KETO to DA (0.122) were estimated, whereas recursive effects from health disorders to production traits were negligible in all cases. Heritability estimates of health disorders ranged from 0.023 to 0.114, in accordance with previous studies. Similarly, genetic correlations obtained between health disorders were moderate. The results obtained suggest that reproductive and metabolic health disorder and culling due to metabolic and reproductive diseases have strong causal relationships. Based on these results, we concluded that a health disorder (either reproductive or metabolic) occurring in early lactation has a moderate causal effect on the reproductive or metabolic health disorder occurring in later lactation. In addition, direct, indirect, and overall effects of reproductive and metabolic health disorders on milk yields for cows that avoid culling are weak.}, number={4}, journal={JOURNAL OF DAIRY SCIENCE}, publisher={Elsevier}, author={Dhakal, K. and Tiezzi, F. and Clay, J. S. and Maltecca, C.}, year={2015}, month={Apr}, pages={2713–2726} }
 @article{howard_haile-mariam_pryce_maltecca_2015, title={Investigation of regions impacting inbreeding depression and their association with the additive genetic effect for United States and Australia Jersey dairy cattle}, volume={16}, ISSN={["1471-2164"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84945200087&partnerID=MN8TOARS}, DOI={10.1186/s12864-015-2001-7}, abstractNote={Variation in environment, management practices, nutrition or selection objectives has led to a variety of different choices being made in the use of genetic material between countries. Differences in genome-level homozygosity between countries may give rise to regions that result in inbreeding depression to differ. The objective of this study was to characterize regions that have an impact on a runs of homozygosity (ROH) metric and estimate their association with the additive genetic effect of milk (MY), fat (FY) and protein yield (PY) and calving interval (CI) using Australia (AU) and United States (US) Jersey cows.Genotyped cows with phenotypes on MY, FY and PY (n = 6751 US; n = 3974 AU) and CI (n = 5816 US; n = 3905 AU) were used in a two-stage analysis. A ROH statistic (ROH4Mb), which counts the frequency of a SNP being in a ROH of at least 4 Mb was calculated across the genome. In the first stage, residuals were obtained from a model that accounted for the portion explained by the estimated breeding value. In the second stage, these residuals were regressed on ROH4Mb using a single marker regression model and a gradient boosted machine (GBM) algorithm. The relationship between the additive and ROH4Mb of a region was characterized based on the (co)variance of 500 kb estimated genomic breeding values derived from a Bayesian LASSO analysis. Phenotypes to determine ROH4Mb and additive effects were residuals from the two-stage approach and yield deviations, respectively.Associations between yield traits and ROH4Mb were found for regions on BTA13, BTA23 and BTA25 for the US population and BTA3, BTA7, BTA17 for the AU population. Only one association (BTA7) was found for CI and ROH4Mb for the US population. Multiple potential epistatic interactions were characterized based on the GBM analysis. Lastly, the covariance sign between ROH4Mb and additive SNP effect of a region was heterogeneous across the genome.We identified multiple genomic regions associated with ROH4Mb in US and AU Jersey females. The covariance of regions impacting ROH4Mb and the additive genetic effect were positive and negative, which provides evidence that the homozygosity effect is location dependent.}, number={1}, journal={BMC GENOMICS}, author={Howard, Jeremy T. and Haile-Mariam, Mekonnen and Pryce, Jennie E. and Maltecca, Christian}, year={2015}, month={Oct} }
 @inproceedings{howard_pryce_haile-mariam_maltecca_2015, title={Regions impacting inbreeding depression and their association with additive genetic
effects for jersey cattle from the United States of America and Australia}, volume={21}, booktitle={Proceedings of the 21st Association for the Advancement of Animal Breeding and Genetics Conference}, author={Howard, J.T. and Pryce, J.E. and Haile-Mariam, M. and Maltecca, C.}, year={2015}, pages={346–349} }
 @article{dhakal_tiezzi_clay_maltecca_2015, title={Short communication: Genomic selection for hoof lesions in first-parity US Holsteins}, volume={98}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84928093134&partnerID=MN8TOARS}, DOI={10.3168/jds.2014-8830}, abstractNote={<h2>Abstract</h2> Hoof lesions contributing to lameness are crucial economic factors that hinder the profitability of dairy enterprises. Producer-recorded hoof lesions data of US Holsteins were categorized into infectious (abscess, digital and interdigital dermatitis, heel erosion, and foot rot) and noninfectious (korn, corkscrew, sole and toe ulcer, sole hemorrhage, white line separation, fissures, thin soles, and upper leg lesions) categories of hoof lesions. Pedigree- and genomic-based univariate analyses were conducted to estimate the variance components and heritability of infectious and noninfectious hoof lesions. A threshold sire model was used with fixed effects of year-seasons and random effects of herd and sire. For genomic-based analysis, a single-step procedure was conducted, incorporating <b>H</b> matrix to estimate genomic variance components and heritability for hoof lesions. The pedigree-based analysis produced heritability estimates of 0.11 (±0.05) for infectious hoof lesions and 0.08 (±0.05) for noninfectious hoof lesions. The single-step genomic analysis produced heritability estimates of 0.14 (±0.06) for infectious hoof lesions and 0.12 (±0.08) for noninfectious hoof lesions. Approximated genetic correlations between hoof lesion traits and hoof type traits along with productive life and net merit were all low and ranged between −0.25 and 0.14. Sire reliabilities increased, on average, by 0.24 and 0.18 for infectious and noninfectious hoof lesions, respectively, with incorporation of genomic data.}, number={5}, journal={JOURNAL OF DAIRY SCIENCE}, author={Dhakal, K. and Tiezzi, F. and Clay, J. S. and Maltecca, C.}, year={2015}, month={May}, pages={3502–3507} }
 @article{putz_tiezzi_maltecca_gray_knauer_2015, title={Variance component estimates for alternative litter size traits in swine}, volume={93}, ISSN={["1525-3163"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84975508749&partnerID=MN8TOARS}, DOI={10.2527/jas.2015-9416}, abstractNote={Litter size at d 5 (LS5) has been shown to be an effective trait to increase total number born (TNB) while simultaneously decreasing preweaning mortality. The objective of this study was to determine the optimal litter size day for selection (i.e., other than d 5). Traits included TNB, number born alive (NBA), litter size at d 2, 5, 10, 30 (LS2, LS5, LS10, LS30, respectively), litter size at weaning (LSW), number weaned (NW), piglet mortality at d 30 (MortD30), and average piglet birth weight (BirthWt). Litter size traits were assigned to biological litters and treated as a trait of the sow. In contrast, NW was the number of piglets weaned by the nurse dam. Bivariate animal models included farm, year-season, and parity as fixed effects. Number born alive was fit as a covariate for BirthWt. Random effects included additive genetics and the permanent environment of the sow. Variance components were plotted for TNB, NBA, and LS2 to LS30 using univariate animal models to determine how variances changed over time. Additive genetic variance was minimized at d 7 in Large White and at d 14 in Landrace pigs. Total phenotypic variance for litter size traits decreased over the first 10 d and then stabilized. Heritability estimates increased between TNB and LS30. Genetic correlations between TNB, NBA, and LS2 to LS29 with LS30 plateaued within the first 10 d. A genetic correlation with LS30 of 0.95 was reached at d 4 for Large White and at d 8 for Landrace pigs. Heritability estimates ranged from 0.07 to 0.13 for litter size traits and MortD30. Birth weight had an h of 0.24 and 0.26 for Large White and Landrace pigs, respectively. Genetic correlations among LS30, LSW, and NW ranged from 0.97 to 1.00. In the Large White breed, genetic correlations between MortD30 with TNB and LS30 were 0.23 and -0.64, respectively. These correlations were 0.10 and -0.61 in the Landrace breed. A high genetic correlation of 0.98 and 0.97 was observed between LS10 and NW for Large White and Landrace breeds, respectively. This would indicate that NW could possibly be used as an effective maternal trait, given a low level of cross-fostering, to avoid back calculating litter size traits from piglet records. Litter size at d 10 would be a compromise between gain in litter size at weaning and minimizing the potentially negative effects of the nurse dam and direct additive genetics of the piglets, as they are expected to increase throughout lactation.}, number={11}, journal={JOURNAL OF ANIMAL SCIENCE}, publisher={American Society of Animal Science}, author={Putz, A. M. and Tiezzi, F. and Maltecca, C. and Gray, K. A. and Knauer, M. T.}, year={2015}, month={Nov}, pages={5153–5163} }
 @inproceedings{maltecca_2014, title={A multifaceted approach to the use of genomic selection in new traits}, booktitle={Proceedings of the 10th World Congress of Genetics Applied to Livestock Production}, author={Maltecca, C.}, year={2014} }
 @inproceedings{battagin_tiezzi_cassandro_maltecca_2014, title={Causal Relationships Between Milk Yield, Body Condition Score and Fertility in Italian Holstein Friesian Dairy Cattle}, booktitle={Proceedings, 10th World Congress of Genetics Applied to Livestock Production}, author={Battagin, M. and Tiezzi, F. and Cassandro, M. and Maltecca, C.}, year={2014} }
 @article{jiao_maltecca_gray_cassady_2014, title={Feed intake, average daily gain, feed efficiency, and real-time ultrasound traits in Duroc pigs: I. Genetic parameter estimation and accuracy of genomic prediction}, volume={92}, ISSN={["1525-3163"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84901594245&partnerID=MN8TOARS}, DOI={10.2527/jas.2013-7338}, abstractNote={The efficiency of producing salable products in the pork industry is largely determined by costs associated with feed and by the amount and quality of lean meat produced. The objectives of this paper were 1) to explore heritability and genetic correlations for growth, feed efficiency, and real-time ultrasound traits using both pedigree and marker information and 2) to assess accuracy of genomic prediction for those traits using Bayes A prediction models in a Duroc terminal sire population. Body weight at birth (BW at birth) and weaning (BW at weaning) and real-time ultrasound traits, including back fat thickness (BF), muscle depth (MD), and intramuscular fat content (IMF), were collected on the basis of farm protocol. Individual feed intake and serial BW records of 1,563 boars obtained from feed intake recording equipment (FIRE; Osborne Industries Inc., Osborne, KS) were edited to obtain growth, feed intake, and feed efficiency traits, including ADG, ADFI, feed conversion ratio (FCR), and residual feed intake (RFI). Correspondingly, 1,047 boars were genotyped using the Illumina PorcineSNP60 BeadChip. The remaining 516 boars, as an independent sample, were genotyped with a low-density GGP-Porcine BeadChip and imputed to 60K. Magnitudes of heritability from pedigree analysis were moderate for growth, feed intake, and ultrasound traits (ranging from 0.44 ± 0.11 for ADG to 0.58 ± 0.09 for BF); heritability estimates were 0.32 ± 0.09 for FCR but only 0.10 ± 0.05 for RFI. Comparatively, heritability estimates using marker information by Bayes A models were about half of those from pedigree analysis, suggesting "missing heritability." Moderate positive genetic correlations between growth and feed intake (0.32 ± 0.05) and back fat (0.22 ± 0.04), as well as negative genetic correlations between growth and feed efficiency traits (-0.21 ± 0.08, -0.05 ± 0.07), indicate selection solely on growth traits may lead to an undesirable increase in feed intake, back fat, and reduced feed efficiency. Genetic correlations among growth, feed intake, and FCR assessed by a multiple-trait Bayes A model resulted in increased genetic correlation between ADG and ADFI, a negative correlation between ADFI and FCR, and a positive correlation between ADG and FCR. Accuracies of genomic prediction for the traits investigated, ranging from 9.4% for RFI to 36.5% for BF, were reported that might provide new insight into pig breeding and future selection programs using genomic information.}, number={6}, journal={JOURNAL OF ANIMAL SCIENCE}, author={Jiao, S. and Maltecca, C. and Gray, K. A. and Cassady, J. P.}, year={2014}, month={Jun}, pages={2377–2386} }
 @article{jiao_maltecca_gray_cassady_2014, title={Feed intake, average daily gain, feed efficiency, and real-time ultrasound traits in Duroc pigs: II. Genomewide association}, volume={92}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84905009876&partnerID=MN8TOARS}, DOI={10.2527/jas.2014-7337}, abstractNote={Efficient use of feed resources has become a clear challenge for the U.S. pork industry as feed costs continue to be the largest variable expense. The availability of the Illumina Porcine60K BeadChip has greatly facilitated whole-genome association studies to identify chromosomal regions harboring genes influencing those traits. The current study aimed at identifying genomic regions associated with variation in feed efficiency and several production traits in a Duroc terminal sire population, including ADFI, ADG, feed conversion ratio, residual feed intake (RFI), real-time ultrasound back fat thickness (BF), ultrasound muscle depth, intramuscular fat content (IMF), birth weight (BW at birth), and weaning weight (BW at weaning). Single trait association analyses were performed using Bayes B models with 35,140 SNP on 18 autosomes after quality control. Significance of nonoverlapping 1-Mb length windows (n = 2,380) were tested across 3 QTL inference methods: posterior distribution of windows variances from Monte Carlo Markov Chain, naive Bayes factor, and nonparametric bootstrapping. Genes within the informative QTL regions for the traits were annotated. A region ranging from166 to 140 Mb (4-Mb length) on SSC 1, approximately 8 Mb upstream of the MC4R gene, was significantly associated with ADFI, ADG, and BF, where SOCS6 and DOK6 are proposed as the most likely candidate genes. Another region affecting BW at weaning was identified on SSC 4 (84-85 Mb), harboring genes previously found to influence both human and cattle height: PLAG1, CHCHD7, RDHE2 (or SDR16C5), MOS, RPS20, LYN, and PENK. No QTL were identified for RFI, IMF, and BW at birth. In conclusion, we have identified several genomic regions associated with traits affecting nutrient utilization that could be considered for future genomic prediction to improve feed utilization.}, number={7}, journal={Journal of Animal Science}, author={Jiao, S. and maltecca and Gray, K. A. and Cassady, J. P.}, year={2014}, pages={2846–2860} }
 @article{ogut_maltecca_whetten_mckeand_isik_2014, title={Genetic Analysis of Diallel Progeny Test Data Using Factor Analytic Linear Mixed Models}, volume={60}, ISSN={["1938-3738"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84893424508&partnerID=MN8TOARS}, DOI={10.5849/forsci.12-108}, abstractNote={Multienvironmental trials are commonly used in plant breeding programs to select superior genotypes for specific sites or across multiple sites for breeding and deployment decisions. We compared the efficiency of factor analytic (FA) and other covariance structures for genetic analysis of height growth in Pinus taeda L. diallel progeny trials to account for heterogeneity in variances and covariances among different environments. Among the models fitted, FA models produced the smallest Akaike information criterion (AIC) model fit statistic. An unstructured (US) variance-covariance matrix produced a log likelihood value similar to that for the FA model but had a large number of parameters. As a result, some models with US covariance failed to converge. FA models captured both variance and covariance at the genetic level better than simpler models and provided more accurate predictions of breeding values. Narrow-sense heritability estimates for height from 10 different sites were about 0.20 when more complex variance structures were used, compared with 0.13 when simpler variance structures such as identity and block-diagonal variance structures were used. FA models are robust for modeling genotype × environment interaction, and they reduce the computational requirements of mixed-model analysis. On average, all 10 environments had additive genetic correlation of 0.83 and dominance genetic correlation of 0.91, suggesting that genotype × environment interaction should not be a concern for this specific population in the environments in which the genotypes were tested.}, number={1}, journal={FOREST SCIENCE}, publisher={Oxford University Press (OUP)}, author={Ogut, Funda and Maltecca, Christian and Whetten, Ross and McKeand, Steven and Isik, Fikret}, year={2014}, month={Feb}, pages={119–127} }
 @inproceedings{morgante_sorensen_sørensen_maltecca_mackay_2014, title={Genetic Analysis of Micro-environmental Plasticity in Drosophila melanogaster}, booktitle={Proceedings, 10th World Congress of Genetics Applied to Livestock Production}, author={Morgante, F. and Sorensen, D.A. and Sørensen, P. and Maltecca, C. and Mackay, T.F.C.}, year={2014} }
 @article{tullo_biffani_maltecca_rizzi_2014, title={Genetic parameters for milk yield and persistency in Carora dairy cattle breed using random regression model}, volume={13}, ISSN={["1828-051X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84919617349&partnerID=MN8TOARS}, DOI={10.4081/ijas.2014.3484}, abstractNote={In tropical environments, lactation curves with lower peaks and higher persistency (PS) might be desirable from both an economical and a physiological point of view. The objective of this study was to obtain genetic parameters for test day (TD) yields, and PS for the tropical breed Carora and to compare these with results from a standard 305-d-milk yield animal model. Four random regression models (RRM) were used on a dataset composed of 95,606 TD records collected in Venezuela and tested to find the best fitting the data. Estimated daily heritabilities for milk yields ranged from 0.21 to 0.30, with the lowest values around the peak of lactation. Lactation repeatabilities ranged from 0.50 to 0.56. Correlations between the breeding values obtained with the RRM and the lactation model currently used in Venezuela [single trait Animal Model (stAM)] are quite high and positive (Pearson correlation=0.71 and Spearman correlation=0.72). Correlations between PS and 305-d-milk yield estimated breeding values (EBV) ranged from -0.18 (PS as the deviation of daily productions in the interval 50-279 days in milk from a point at the end of lactation) to 0.52 (PS as EBV difference between the second and the first stage of lactation). The use of PS indexes accounting for milk yield may allow the selection of individuals able to express their potential genetic values in tropical environment, without incurring in excessive heat stress losses.}, number={4}, journal={ITALIAN JOURNAL OF ANIMAL SCIENCE}, author={Tullo, Emanuela and Biffani, Stefano and Maltecca, Christian and Rizzi, Rita}, year={2014} }
 @inproceedings{howard_tiezzi_jiao_gray_maltecca_2014, title={Genome-Wide Association Study For Growth And Feed Intake in Duroc boars Utilizing Random Regression Models}, booktitle={Proceedings, 10th World Congress of Genetics Applied to Livestock Production}, author={Howard, J.T. and Tiezzi, F. and Jiao, S. and Gray, K.A. and Maltecca, C.}, year={2014} }
 @inproceedings{tiezzi_maltecca_2014, place={Vancouver, BC}, title={Genomic prediction using a weighted relationship matrix to account for trait architecture in US Holstein cattle}, booktitle={Proceedings of the 10th World Congress on Genetics Applied to Livestock Production}, publisher={American Society of Animal Science}, author={Tiezzi, F. and Maltecca, C.}, year={2014} }
 @article{gaddis_cole_clay_maltecca_2014, title={Genomic selection for producer-recorded health event data in US dairy cattle}, volume={97}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84899070158&partnerID=MN8TOARS}, DOI={10.3168/jds.2013-7543}, abstractNote={<h2>Abstract</h2> Emphasizing increased profit through increased dairy cow production has revealed a negative relationship of production with fitness and health traits. Decreased cow health can affect herd profitability through increased rates of involuntary culling and decreased or lost milk sales. The development of genomic selection methodologies, with accompanying substantial gains in reliability for low-heritability traits, may dramatically improve the feasibility of genetic improvement of dairy cow health. Producer-recorded health information may provide a wealth of information for improvement of dairy cow health, thus improving profitability. The principal objective of this study was to use health data collected from on-farm computer systems in the United States to estimate variance components and heritability for health traits commonly experienced by dairy cows. A single-step analysis was conducted to estimate genomic variance components and heritabilities for health events, including cystic ovaries, displaced abomasum, ketosis, lameness, mastitis, metritis, and retained placenta. A blended <b>H</b> matrix was constructed for a threshold model with fixed effects of parity and year-season and random effects of herd-year and sire. The single-step genomic analysis produced heritability estimates that ranged from 0.02 (standard deviation=0.005) for lameness to 0.36 (standard deviation=0.08) for retained placenta. Significant genetic correlations were found between lameness and cystic ovaries, displaced abomasum and ketosis, displaced abomasum and metritis, and retained placenta and metritis. Sire reliabilities increased, on average, approximately 30% with the incorporation of genomic data. From the results of these analyses, it was concluded that genetic selection for health traits using producer-recorded data are feasible in the United States, and that the inclusion of genomic data substantially improves reliabilities for these traits.}, number={5}, journal={JOURNAL OF DAIRY SCIENCE}, author={Gaddis, K. L. Parker and Cole, J. B. and Clay, J. S. and Maltecca, C.}, year={2014}, month={May}, pages={3190–3199} }
 @article{mcculloch_ashwell_maltecca_o'nan_mente_2014, title={Progression of Gene Expression Changes following a Mechanical Injury to Articular Cartilage as a Model of Early Stage Osteoarthritis}, volume={2014}, ISSN={2090-1984 2090-1992}, url={http://dx.doi.org/10.1155/2014/371426}, DOI={10.1155/2014/371426}, abstractNote={An impact injury model of early stage osteoarthritis (OA) progression was developed using a mechanical insult to an articular cartilage surface to evaluate differential gene expression changes over time and treatment. Porcine patellae with intact cartilage surfaces were randomized to one of three treatments: nonimpacted control, axial impaction (2000 N), or a shear impaction (500 N axial, with tangential displacement to induce shear forces). After impact, the patellae were returned to culture for 0, 3, 7, or 14 days. At the appropriate time point, RNA was extracted from full-thickness cartilage slices at the impact site. Quantitative real-time PCR was used to evaluate differential gene expression for 18 OA related genes from four categories: cartilage matrix, degradative enzymes and inhibitors, inflammatory response and signaling, and cell apoptosis. The shear impacted specimens were compared to the axial impacted specimens and showed that shear specimens more highly expressed type I collagen (Col1a1) at the early time points. In addition, there was generally elevated expression of degradative enzymes, inflammatory response genes, and apoptosis markers at the early time points. These changes suggest that the more physiologically relevant shear loading may initially be more damaging to the cartilage and induces more repair efforts after loading.}, journal={Arthritis}, publisher={Hindawi Limited}, author={McCulloch, R. S. and Ashwell, M. S. and Maltecca, C. and O'Nan, A. T. and Mente, P. L.}, year={2014}, month={Nov}, pages={1–9} }
 @article{howard_baynes_brooks_yeatts_bellis_ashwell_routh_o'nan_maltecca_2014, title={The effect of breed and sex on sulfamethazine, enrofloxacin, fenbendazole and flunixin meglumine pharmacokinetic parameters in swine}, volume={37}, ISSN={["1365-2885"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84911423325&partnerID=MN8TOARS}, DOI={10.1111/jvp.12128}, abstractNote={Drug use in livestock has received increased attention due to welfare concerns and food safety. Characterizing heterogeneity in the way swine populations respond to drugs could allow for group‐specific dose or drug recommendations. Our objective was to determine whether drug clearance differs across genetic backgrounds and sex for sulfamethazine, enrofloxacin, fenbendazole and flunixin meglumine. Two sires from each of four breeds were mated to a common sow population. The nursery pigs generated ( n = 114) were utilized in a random crossover design. Drugs were administered intravenously and blood collected a minimum of 10 times over 48 h. A non‐compartmental analysis of drug and metabolite plasma concentration vs. time profiles was performed. Within‐drug and metabolite analysis of pharmacokinetic parameters included fixed effects of drug administration date, sex and breed of sire. Breed differences existed for flunixin meglumine ( P ‐value<0.05; Cl, Vd ss ) and oxfendazole ( P ‐value<0.05, AUC 0→∞ ). Sex differences existed for oxfendazole ( P ‐value < 0.05; T max ) and sulfamethazine ( P ‐value < 0.05, Cl). Differences in drug clearance were seen, and future work will determine the degree of additive genetic variation utilizing a larger population.}, number={6}, journal={JOURNAL OF VETERINARY PHARMACOLOGY AND THERAPEUTICS}, author={Howard, J. T. and Baynes, R. E. and Brooks, J. D. and Yeatts, J. L. and Bellis, B. and Ashwell, M. S. and Routh, P. and O'Nan, A. T. and Maltecca, C.}, year={2014}, month={Dec}, pages={531–541} }
 @inproceedings{pellegrino_dolezal_maltecca_velayutham_strillacci_frigo_schlangen_samore_schiavini_santus_et al._2013, title={A medium resolution SNP array based CNV scan in Italian Brown Swiss dairy cattle}, volume={19}, booktitle={Annual Meeting of the European Association for Animal Production}, publisher={Wageningen Publishers}, author={Pellegrino, L. and Dolezal, M.A. and Maltecca, C. and Velayutham, D. and Strillacci, M.G. and Frigo, E. and Schlangen, K. and Samore, A.B. and Schiavini, F. and Santus, E. and et al.}, year={2013}, pages={545–545} }
 @article{snelling_cushman_keele_maltecca_thomas_fortes_reverter_2013, title={BREEDING AND GENETICS SYMPOSIUM: Networks and pathways to guide genomic selection}, volume={91}, ISSN={["1525-3163"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84875141123&partnerID=MN8TOARS}, DOI={10.2527/jas.2012-5784}, abstractNote={Many traits affecting profitability and sustainability of meat, milk, and fiber production are polygenic, with no single gene having an overwhelming influence on observed variation. No knowledge of the specific genes controlling these traits has been needed to make substantial improvement through selection. Significant gains have been made through phenotypic selection enhanced by pedigree relationships and continually improving statistical methodology. Genomic selection, recently enabled by assays for dense SNP located throughout the genome, promises to increase selection accuracy and accelerate genetic improvement by emphasizing the SNP most strongly correlated to phenotype although the genes and sequence variants affecting phenotype remain largely unknown. These genomic predictions theoretically rely on linkage disequilibrium (LD) between genotyped SNP and unknown functional variants, but familial linkage may increase effectiveness when predicting individuals related to those in the training data. Genomic selection with functional SNP genotypes should be less reliant on LD patterns shared by training and target populations, possibly allowing robust prediction across unrelated populations. Although the specific variants causing polygenic variation may never be known with certainty, a number of tools and resources can be used to identify those most likely to affect phenotype. Associations of dense SNP genotypes with phenotype provide a 1-dimensional approach for identifying genes affecting specific traits; in contrast, associations with multiple traits allow defining networks of genes interacting to affect correlated traits. Such networks are especially compelling when corroborated by existing functional annotation and established molecular pathways. The SNP occurring within network genes, obtained from public databases or derived from genome and transcriptome sequences, may be classified according to expected effects on gene products. As illustrated by functionally informed genomic predictions being more accurate than naive whole-genome predictions of beef tenderness, coupling evidence from livestock genotypes, phenotypes, gene expression, and genomic variants with existing knowledge of gene functions and interactions may provide greater insight into the genes and genomic mechanisms affecting polygenic traits and facilitate functional genomic selection for economically important traits.}, number={2}, journal={JOURNAL OF ANIMAL SCIENCE}, author={Snelling, W. M. and Cushman, R. A. and Keele, J. W. and Maltecca, C. and Thomas, M. G. and Fortes, M. R. S. and Reverter, A.}, year={2013}, month={Feb}, pages={537–552} }
 @article{cole_lewis_maltecca_newman_olson_tait_2013, title={BREEDING AND GENETICS SYMPOSIUM: Systems biology in animal breeding: Identifying relationships among markers, genes, and phenotypes}, volume={91}, ISSN={["1525-3163"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84882602264&partnerID=MN8TOARS}, DOI={10.2527/jas.2012-6166}, abstractNote={The Breeding and Genetics Symposium titled “Systems Biology in Animal Breeding: Identifying relationships among markers, genes, and phenotypes” was held at the Joint Annual Meeting of the American Society of Animal Science and the American Dairy Science Association in Phoenix, AZ, July 15–19, 2012. The primary goal of the symposium was to demonstrate the use of high-density SNP genotypes to determine the complex regulatory relationships among genotypes and phenotypes but also to present methods for studying complex relationships among phenotypes. Case studies from mammalian and avian species were included to emphasize the broad applicability of these methods. Modern systems biology attempts to explain how variations in DNA markers relate to phenotypes through transcription, protein, and regulatory networks. Systems biology is a rapidly growing field of study, and many animal breeders are unfamiliar with this area. Still, resources for such work are rapidly becoming available, including high-density SNP genotypes for thousands of phenotyped dairy cattle collected by the North American dairy industry in the National Dairy Database maintained by the Animal Improvement Programs Laboratory (Beltsville, MD), and several multi-institutional and some international projects are genotyping and phenotyping animals for novel traits such as efficiency of production. In addition, a number of competitive grants recently awarded by the USDA National Institute of Food and Agriculture's Agriculture and Food Research Initiative competitive grants program include 1 or more objectives incorporating systems biology approaches in several species, including cattle, swine, and poultry. There is substantial evidence that most QTL in the cattle genome explain only small proportions of additive genetic variance for important phenotypes (e.g., Cole et al., 2011). Recent papers, such as Fortes et al. (2011) have shown that gene network analysis can provide powerful insight into the genetic architecture underlying complex phenotypes.}, number={2}, journal={JOURNAL OF ANIMAL SCIENCE}, author={Cole, J. B. and Lewis, R. M. and Maltecca, C. and Newman, S. and Olson, K. M. and Tait, R. G., Jr.}, year={2013}, month={Feb}, pages={521–522} }
 @article{dhakal_maltecca_cassady_baloche_williams_washburn_2013, title={Calf birth weight, gestation length, calving ease, and neonatal calf mortality in Holstein, Jersey, and crossbred cows in a pasture system}, volume={96}, ISSN={["0022-0302"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84871620780&partnerID=MN8TOARS}, DOI={10.3168/jds.2012-5817}, abstractNote={Holstein (HH), Jersey (JJ), and crosses of these breeds were mated to HH or JJ bulls to form purebreds, reciprocal crosses, backcrosses, and other crosses in a rotational mating system. The herd was located at the Center for Environmental Farming Systems in Goldsboro, North Carolina. Data for calf birth weight (CBW), calving ease (0 for unassisted, n=1,135, and 1 for assisted, n=96), and neonatal calf mortality (0 for alive, n=1,150, and 1 for abortions recorded after mid-gestation, stillborn, and dead within 48 h, n=81) of calves (n=1,231) were recorded over 9 calving seasons from 2003 through 2011. Gestation length (GL) was calculated as the number of days from last insemination to calving. Linear mixed models for CBW and GL included fixed effects of sex, parity (first vs. later parities), twin status, and 6 genetic groups: HH, JJ, reciprocal F(1) crosses (HJ, JH), crosses >50% Holsteins (HX) and crosses >50% Jerseys (JX), where sire breed is listed first. The CBW model also included GL as a covariate. Logistic regression for calving ease and neonatal calf mortality included fixed effects of sex, parity, and genetic group. Genetic groups were replaced by linear regression using percentage of HH genes as coefficients on the above models and included as covariates to determine various genetic effects. Year and dam were included as random effects in all models. Female calves (27.57±0.54 kg), twins (26.39±1.0 kg), and calves born to first-parity cows (27.67±0.56 kg) had lower CBW than respective male calves (29.53±0.53 kg), single births (30.71±0.19 kg), or calves born to multiparous cows (29.43±0.52 kg). Differences in genetic groups were observed for CBW and GL. Increased HH percentage in the calf increased CBW (+9.3±0.57 kg for HH vs. JJ calves), and increased HH percentage in the dams increased CBW (+1.71±0.53 kg for calves from HH dams vs. JJ dams); JH calves weighed 1.33 kg more than reciprocal HJ calves. Shorter GL was observed for twin births (272.6±1.1 d), female calves (273.9±0.6 d), and for first-parity dams (273.8±0.6 d). Direct genetic effects of HH alleles shortened GL (-3.5±0.7 d), whereas maternal HH alleles increased GL (2.7±0.6 d). Female calves had lower odds ratio (0.32, confidence interval=0.10-0.99) for neonatal calf mortality in second and later parities than did male calves. Maternal heterosis in crossbred primiparous dams was associated with reduced calf mortality.}, number={1}, journal={JOURNAL OF DAIRY SCIENCE}, author={Dhakal, K. and Maltecca, C. and Cassady, J. P. and Baloche, G. and Williams, C. M. and Washburn, S. P.}, year={2013}, month={Jan}, pages={690–698} }
 @inproceedings{maltecca_gaddis_clay_cole_2013, title={Challenges and opportunities for farmer-recorded data in health and welfare selection}, booktitle={Proceedings of the ICAR Conference}, author={Maltecca, C. and Gaddis, K.L.P. and Clay, J. and Cole, J.B.}, year={2013}, pages={30–31} }
 @article{ashwell_gonda_gray_maltecca_audrey t. o'nan_cassady_mente_2013, title={Changes in chondrocyte gene expression following in vitro impaction of porcine articular cartilage in an impact injury model}, volume={31}, ISSN={["1554-527X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84872761673&partnerID=MN8TOARS}, DOI={10.1002/jor.22239}, abstractNote={Abstract Our objective was to monitor chondrocyte gene expression at 0, 3, 7, and 14 days following in vitro impaction to the articular surface of porcine patellae. Patellar facets were either axially impacted with a cylindrical impactor (25 mm/s loading rate) to a load level of 2,000 N or not impacted to serve as controls. After being placed in organ culture for 0, 3, 7, or 14 days, total RNA was isolated from full thickness cartilage slices and gene expression measured for 17 genes by quantitative real‐time RT‐PCR. Targeted genes included those encoding proteins involved with biological stress, inflammation, or anabolism and catabolism of cartilage extracellular matrix. Some gene expression changes were detected on the day of impaction, but most significant changes occurred at 14 days in culture. At 14 days in culture, 10 of the 17 genes were differentially expressed with col1a1 most significantly up‐regulated in the impacted samples, suggesting impacted chondrocytes may have reverted to a fibroblast‐like phenotype. © 2012 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 31: 385–391, 2013}, number={3}, journal={JOURNAL OF ORTHOPAEDIC RESEARCH}, author={Ashwell, Melissa S. and Gonda, Michael G. and Gray, Kent and Maltecca, Christian and Audrey T. O'Nan and Cassady, Joseph P. and Mente, Peter L.}, year={2013}, month={Mar}, pages={385–391} }
 @article{maltecca_2013, title={Fitter happier: the never-ending quest for a better cow}, volume={130}, ISSN={["1439-0388"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84878367795&partnerID=MN8TOARS}, DOI={10.1111/jbg.12032}, abstractNote={A clear challenge is now facing the food animal industry. We must devise methods for producing more food, using fewer inputs and minimizing environmental impact and at the same time ensure the welfare of animals. In this, dairy products provide an efficient and sustainable approach to meet the global food demand because of their efficient production, high nutritional value, diverse manufacturing capabilities and palatability. Achievement of these goals is too often in direct conflict with the short-term needs of farmers. If the contribution of genetic improvement to higher dairy productivity has been essential, with annual increases per milk produced of approximately 200lbs achieved in most of Western Europe and North America [Windig et al. (2005) J. Dairy Sci., 88, 335–347], dairy operations remain a low profit margin industry with the main (and often only) source of income tied to the price of milk. Increasing the net profit of the farmers remains an inescapable step towards the creation of a competitive and sustainable agriculture. Improving productivity has been the major goal of nearly all dairy cattle breeding programmes for a long time. This has neglected fitness and fertility parameters that are related to decreased costs of production and have a large impact on the farms' net profitability. Individual cow diseases particularly are associated with increased culling, loss of production and labour costs [Zwald et al. (2004) J. Dairy Sci., 87, 4287–4294; Hansen (2000) J. Dairy Sci., 83, 1145–1150]. Improving animal health needs to be a top priority in the dairy industry both from an economic as well as an ethical standpoint. In this, genetic selection for improved health will grant a permanent improvement in performance and profitability. Genetic improvement of traits related to survival has for the large part focused on longevity. Currently, only seven countries incorporate direct health information into their selection programme [Steine et al. (2008) J. Dairy Sci., 91, 418–426]. There has, however, been a growing interest in the health data, and the International Committee for Animal Recording's Functional Traits Working Group released a document detailing recommendations and best practices for the collection of producer-recorded health event data [Cole et al. (2012) J. Dairy Sci., 95 (Suppl. 2), 443]. Several obstacles remain to the widespread routine implementation of selection programmes for health traits. From a scientific perspective, the broad definition of ‘disease’ or ‘direct health’ traits makes little sense. The heterogeneity and complexity of these traits needs to be dealt with in much greater detail than has been done so far. The dynamics between resistance (the ability of a cow to avoid getting sick altogether) and tolerance (the ability of coping with a disease and maintaining reasonable production) need to be considered [Bishop et al. (2012) Front. Genet., 3, 114], and the interrelation among different diseases and between these and production practices is far from being completely elicited [Appuhamy et al. (2009) J. Dairy Sci., 92, 1785–1795; Parker Gaddis et al. (2012) J. Dairy Sci., 95, 5422–5435]. However, while the effort in understanding and refining statistical methods and biological understanding of health traits is vital, this cannot be separated from the development of meaningful and applicable tools for farmers and organizations. While selecting breeding animals to produce the next generation with known improvements for direct health traits represents the ultimate goal, it is important to understand that given the nature of most of these traits, a large role in the improvement of cow health and the reduction of disease incidence will be played by the managing elements of animal husbandry. While there is a virtually endless pool of phenotypes that could be potentially considered for selection, there needs to be a concerted effort in establishing a selection programme to identify a few key parameters for which a consistent and demonstrable improvement can be achieved, to avoid the risk of mixed bag of results undermining the perception of the usefulness of selecting for such traits. Moreover, the inherent discrepancy between what is conceivably and reliably measured in the field and the real underlying traits needs to be acknowledged. Special attention needs to be paid by both the scientific community and the industry in communicating what is realistically attainable through selection. Over-optimistic representation of selection efforts will ultimately undermine the credibility of any programme. Improvement has been recently made for several functional traits like SCS, stillbirth, dystocia and fertility. For some of these, the bottoming of a dangerous decline already represents a significant achievement. Realizing that for health traits, the short-term gain might be small and possibly limited to the curbing of a general decline is essential. There is an intrinsic heterogeneity of players, and a complex infrastructure in the collection and flow of information connected to health traits. On-farm computer management systems provide an efficient means for collecting health-related data for genetic analysis. These records currently provide one of the few, if not the only, opportunity for direct selection for disease resistance for countries where recording of health disorders is not mandatory [Zwald et al. 2004 J. Dairy Sci., 87, 4295–4302]. Furthermore, even where veterinary data collection is in place the data are seldom utilized as the only source of direct health information [Cole et al. (2012) J. Dairy Sci., 95 (Suppl. 2), 443]. A lively scientific discussion on the helpfulness and quality of the data is undergoing [Koeck et al. (2012) J. Dairy Sci., 95, 4099–4108]. Nevertheless, among the reasons for the slow implementation of health selection programmes, data privacy concerns are at the top of the list. Yet, discussions on this issue are often carried out behind closed doors. A frank and transparent approach is needed in addressing this theme, additionally keeping in mind that the collection, entry and correction of the data has often apparent as well as hidden additional burdens and costs for the farmers that need to be acknowledged. Genomic selection is largely redefining the notion of what traits we can select for. The success of genomic selection programmes in dairy is often used as a yardstick to gauge the gain achievable with the inclusion of molecular information in a selection programme. Yet, it is often forgotten that this success hinges on highly accurate conventional estimation of breeding values based on hundreds or thousands of progeny for sires that have been genotyped. Genomic selection can be tremendously rewarding for health traits, but its success cannot be granted without building a growing and integrated flow of phenotypic information. It cannot be stressed enough that a few scattered mid-sized resource populations cannot replace a concerted and continuous effort. Concerns about health and fertility in dairy cattle are widespread, and a general consensus exists on placing more emphasis on selection for health and welfare. In spite of this, the inclusion of factual information on selection programmes remains scarce, particularly for disease resistance traits. The time has come for all the different players, scientists, AI organizations, breed societies and private companies, to seize the opportunity for a broader collaborative effort in making the next step in selection for a profitable healthy cow possible. Comments and discussions with John B. Cole at USDA-AIPL are gratefully acknowledged.}, number={2}, journal={JOURNAL OF ANIMAL BREEDING AND GENETICS}, author={Maltecca, C.}, year={2013}, month={Apr}, pages={87–88} }
 @inproceedings{cole_gaddis_clay_maltecca_2013, title={Genomic evaluation of health traits in dairy cattle}, booktitle={ICAR Technical Series}, author={Cole, J. and Gaddis, K.L.P. and Clay, J.S. and Maltecca, C.}, year={2013}, pages={167–175} }
 @book{hickey_cleveland_maltecca_gorjanc_gredler_kranis_2013, title={Genotype imputation to increase sample size in pedigreed populations}, volume={1019}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84883141900&partnerID=MN8TOARS}, DOI={10.1007/978-1-62703-447-0_17}, abstractNote={Genotype imputation is a cost-effective way to increase the power of genomic selection or genome-wide association studies. While several genotype imputation algorithms are available, this chapter focuses on a heuristic algorithm, as implemented in the AlphaImpute software. This algorithm combines long-range phasing, haplotype library imputation, and segregation analysis and it is specifically designed to work with pedigreed populations. The chapter is organized in different sections. First the challenges related to genotype imputation in pedigreed populations are described, along with the specifics of the imputation algorithm used in AlphaImpute. In the second section, factors affecting the accuracy of genotype imputation using this algorithm are discussed. The different parameters that control AlphaImpute are detailed and examples of how to apply AlphaImpute are given.}, journal={Methods in Molecular Biology}, author={Hickey, J.M. and Cleveland, M.A. and Maltecca, C. and Gorjanc, G. and Gredler, B. and Kranis, A.}, year={2013}, pages={395–410} }
 @article{tiezzi_maltecca_penasa_cecchinato_bittante_2013, title={Short communication: Genetic analysis of dairy bull fertility from field data of Brown Swiss cattle}, volume={96}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84886285616&partnerID=MN8TOARS}, DOI={10.3168/jds.2013-6885}, abstractNote={<h2>Abstract</h2> The aim of this study was to estimate heritability and repeatability of dairy bull fertility in Italian Brown Swiss cattle. Bull fertility indicators were calving per service and nonreturn rate at 56d after service. Data included 124,206 inseminations carried out by 86 technicians on 28,873 heifers and cows in 1,400 herds. Services were recorded from 1999 to 2008 and were performed with semen from 306 AI Brown Swiss bulls. Data were analyzed with a threshold animal model, which included the fixed effects of parity by class of days in milk of the inseminated cow (age at insemination for heifers), year-season of insemination, and status of the service bull at the time of insemination (progeny testing or proven), and the random effects of herd, technician, additive genetic, and permanent environment of inseminated heifer/cow and service bull, and residual. Also, genetic covariance between heifer/cow and service bull effects was considered in the model. Heritability and repeatability were 0.0079 and 0.0100 for nonreturn rate at 56d after service, and 0.0153 and 0.0202 for calving per service, respectively. The low estimates obtained in the present study indicate that selection for male fertility using field data is hardly pursuable.}, number={11}, journal={JOURNAL OF DAIRY SCIENCE}, publisher={Elsevier}, author={Tiezzi, F. and Maltecca, C. and Penasa, M. and Cecchinato, A. and Bittante, G.}, year={2013}, month={Nov}, pages={7325–7328} }
 @article{cole_lewis_maltecca_newman_olson_tait_2013, title={Systems Biology in Animal Breeding}, volume={91}, journal={Identifying Relationships among Markers, Genes, and Phenotypes [Breeding and Genetics Symposium}, author={Cole, John B. and Lewis, R.M. and Maltecca, C. and Newman, S. and Olson, K.M. and Tait, R.G.}, year={2013}, pages={521–522} }
 @inproceedings{clay_gaddis_maltecca_2013, title={The value of health data from dairy farmers in the United States}, volume={17}, booktitle={ICAR Technical Series}, author={Clay, J.S. and Gaddis, K.L.P. and Maltecca, C.}, year={2013}, pages={137–148} }
 @article{tiezzi_maltecca_cecchinato_penasa_bittante_2013, title={Thin and fat cows, and the nonlinear genetic relationship between body condition score and fertility}, volume={96}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84884350834&partnerID=MN8TOARS}, DOI={10.3168/jds.2013-6863}, abstractNote={Thin and fat cows are often credited for low fertility, but body condition score (BCS) has been traditionally treated as a linear trait when genetic correlations with reproductive performance have been estimated. The aims of this study were to assess genetic parameters for fertility, production, and body condition traits in the Brown Swiss population reared in the Alps (Bolzano-Bozen Province, Italy), and to investigate the possible nonlinearity among BCS and other traits by analyzing fat and thin cows. Records of BCS measured on a 5-point scale were preadjusted for year-season and days in milk at scoring, and were considered positive (1) for fat cows if they exceeded the value of 1 residual standard deviation or null (0) otherwise, whereas positive values for thin cows were imputed to records below -1 residual standard deviation. Fertility indicators measured on first- and second-parity cows were interval from parturition to first service, interval from first service to conception, interval from parturition to conception, number of inseminations to conception, conception at first service, and nonreturn rate at 56 d after first service. Production traits were peak milk yield, lactation milk yield, and lactation length. Data were from 1,413 herds and included 16,324 records of BCS, fertility, and production for first-parity, and 10,086 fertility records for second-parity cows. Animals calved from 2002 to 2007 and were progeny of 420 artificial insemination bulls. Genetic parameters for the aforementioned traits were obtained under univariate and bivariate threshold and censored linear sire models implemented in a Bayesian framework. Posterior means of heritabilities for BCS, fat cows, and thin cows were 0.141, 0.122, and 0.115, respectively. Genetic correlations of body condition traits with contemporary production were moderate to high and were between -0.556 and 0.623. Body condition score was moderately related to fertility in first (-0.280 to 0.497) and second (-0.392 to 0.248) lactation. The fat cow trait was scarcely related to fertility, particularly in first-parity cows (-0.203 to 0.281). Finally, the genetic relationships between thin cows and fertility were higher than those between BCS and fertility, both in first (-0.456 to 0.431) and second (-0.335 to 0.524) lactation. Body condition score can be considered a predictor of fertility, and it could be included in evaluation either as linear measure or as thin cow. In the second case, the genetic relationship with fertility was stronger, exacerbating the poorest body condition and considering the possible nonlinearity between fertility and energy reserves of the cow.}, number={10}, journal={JOURNAL OF DAIRY SCIENCE}, publisher={Elsevier}, author={Tiezzi, F. and Maltecca, C. and Cecchinato, A. and Penasa, M. and Bittante, G.}, year={2013}, month={Oct}, pages={6730–6741} }
 @article{maltecca_parker_cassady_2012, title={Application of multiple shrinkage methods to genomic predictions}, volume={90}, ISSN={["1525-3163"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84861848308&partnerID=MN8TOARS}, DOI={10.2527/jas.2011-4350}, abstractNote={New challenges have arisen with the development of large marker panels for livestock species. Models easily become overparameterized when all available markers are included. Solutions have led to the development of shrinkage or regularization techniques. The objective of this study was the application and comparison of Bayesian LASSO (B-L), thick-tailed (Student-t), and semiparametric multiple shrinkage methods. The B-L and Student-t methods were also each analyzed within a single shrinkage and a multiple shrinkage framework. Simulated and real data were used to evaluate each method's performance. Real data consisted of SNP genotypes of 4,069 Holstein sires. Traits included in analysis of real data were milk, fat, protein yield, and somatic cell score. The performance of each model was compared based on correlations between true and predicted genomic predicted transmitting abilities. Model performance was also compared with the performance of routinely used methods such as Bayes-A and GBLUP through cross-validation techniques. When using simulated data regardless of shrinkage framework, shrinkage models outperformed genomic BLUP (GBLUP). The average advantage of shrinkage models ranged from 1% to approximately 8% depending on the prior specification. When analyzing real data, shrinkage models slightly outperformed GBLUP for most traits. Shrinkage models were better able to model traits for which 1 or more SNP of large effect have been identified. Overall, results suggested a relatively small advantage in multiple shrinkage models. Multiple shrinkage methods could represent a useful alternative to current methods of prediction; however, their performance in a variety of scenarios needs to be investigated further.}, number={6}, journal={JOURNAL OF ANIMAL SCIENCE}, author={Maltecca, Christian and Parker, Kristen L. and Cassady, Joseph P.}, year={2012}, month={Jun}, pages={1777–1787} }
 @article{huang_hickey_cleveland_maltecca_2012, title={Assessment of alternative genotyping strategies to maximize imputation accuracy at minimal cost}, volume={44}, ISSN={["0999-193X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84864376178&partnerID=MN8TOARS}, DOI={10.1186/1297-9686-44-25}, abstractNote={Abstract Background Commercial breeding programs seek to maximise the rate of genetic gain while minimizing the costs of attaining that gain. Genomic information offers great potential to increase rates of genetic gain but it is expensive to generate. Low-cost genotyping strategies combined with genotype imputation offer dramatically reduced costs. However, both the costs and accuracy of imputation of these strategies are highly sensitive to several factors. The objective of this paper was to explore the cost and imputation accuracy of several alternative genotyping strategies in pedigreed populations. Methods Pedigree and genotype data from a commercial pig population were used. Several alternative genotyping strategies were explored. The strategies differed in the density of genotypes used for the ancestors and the individuals to be imputed. Parents, grandparents, and other relatives that were not descendants, were genotyped at high-density, low-density, or extremely low-density, and associated costs and imputation accuracies were evaluated. Results Imputation accuracy and cost were influenced by the alternative genotyping strategies. Given the mating ratios and the numbers of offspring produced by males and females, an optimized low-cost genotyping strategy for a commercial pig population could involve genotyping male parents at high-density, female parents at low-density (e.g. 3000 SNP), and selection candidates at very low-density (384 SNP). Conclusions Among the selection candidates, 95.5 % and 93.5 % of the genotype variation contained in the high-density SNP panels were recovered using a genotyping strategy that costs respectively, $24.74 and $20.58 per candidate.}, number={1}, journal={GENETICS SELECTION EVOLUTION}, author={Huang, Yijian and Hickey, John M. and Cleveland, Matthew A. and Maltecca, Christian}, year={2012}, month={Jul} }
 @article{gray_cassady_huang_maltecca_2012, title={Effectiveness of genomic prediction on milk flow traits in dairy cattle.}, volume={44}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84864356964&partnerID=MN8TOARS}, DOI={10.1186/1297-9686-44-24}, abstractNote={Milkability, primarily evaluated by measurements of milking speed and time, has an economic impact in milk production of dairy cattle. Recently the Italian Brown Swiss Breeders Association has included milking speed in genetic evaluations. The main objective of this study was to investigate the possibility of implementing genomic selection for milk flow traits in the Italian Brown Swiss population and thereby evaluate the potential of genomic selection for novel traits in medium-sized populations. Predicted breeding values and reliabilities based on genomic information were compared with those obtained from traditional breeding values.Milk flow measures for total milking time, ascending time, time of plateau, descending time, average milk flow and maximum milk flow were collected on 37 213 Italian Brown Swiss cows. Breeding values for genotyped sires (n = 1351) were obtained from standard BLUP and genome-enhanced breeding value techniques utilizing two-stage and single-step methods. Reliabilities from a validation dataset were estimated and used to compare accuracies obtained from parental averages with genome-enhanced predictions.Genome-enhanced breeding values evaluated using two-stage methods had similar reliabilities with values ranging from 0.34 to 0.49 for the different traits. Across two-stage methods, the average increase in reliability from parental average was approximately 0.17 for all traits, with the exception of descending time, for which reliability increased to 0.11. Combining genomic and pedigree information in a single-step produced the largest increases in reliability over parent averages: 0.20, 0.24, 0.21, 0.14, 0.20 and 0.21 for total milking time, ascending time, time of plateau, descending time, average milk flow and maximum milk flow, respectively.Using genomic models increased the accuracy of prediction compared to traditional BLUP methods. Our results show that, among the methods used to predict genome-enhanced breeding values, the single-step method was the most successful at increasing the reliability for most traits. The single-step method takes advantage of all the data available, including phenotypes from non-genotyped animals, and can easily be incorporated into current breeding evaluations.}, journal={Genetics, selection, evolution : GSE}, author={Gray, K.A. and Cassady, J.P. and Huang, Y. and Maltecca, C.}, year={2012}, pages={24} }
 @article{huang_maltecca_cassady_alexander_snelling_macneil_2012, title={Effects of reduced panel, reference origin, and genetic relationship on imputation of genotypes in Hereford cattle}, volume={90}, ISSN={["1525-3163"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84882597849&partnerID=MN8TOARS}, DOI={10.2527/jas.2011-4728}, abstractNote={The objective of this study was to investigate alternative methods of designing and using reduced SNP panels for imputing SNP genotypes. Two purebred Hereford populations, an experimental population known as Line 1 Hereford (L1, n = 240) and registered Hereford with American Hereford Association (AHA, n = 311), were used. Using different reference samples of 62 to 311 animals with 39,497 SNP on 29 autosomes and study samples of 57 or 62 animals for which genotypes were available for ∼2,600 SNP (reduced panels), imputations were performed to predict the other ∼36,900 loci that had been masked. An imputation package, including LinkPHASE and DAGPHASE, was used for imputation. Four reduced panels differing in minor allele frequency (MAF) and marker spacing were evaluated. Reduced panels included every 15th SNP across the genome (SNP_space), commercial Illumina Bovine3K Beadchip (SNP_3K), SNP with the highest MAF (SNP_MAF), and SNP with high MAF that were also evenly spaced across the genome (SNP_MS). Imputation accuracy was defined as the correlation of imputed genotypes and real genotypes. Reference samples were either from L1 or AHA. Among animals with genotypes, genetic relationships were estimated based on molecular marker genotypes or pedigree. Reduced panel design, number of animals in the reference sample, reference origin and genetic relationship between animals in the reference, and study samples all affected imputation accuracy (P < 0.001). Across genotyping schemes, imputed genotypes from SNP_MS had the greatest accuracy. A 0.1 increase in average pedigree relationship or average molecular relationship between reference and study samples increased imputation accuracy 10 to 20%. Using reference samples from the L1 population resulted in lower imputation accuracy than using reference samples from the admixed population AHA (P < 0.001). Increasing the number of animals in the reference panel by 100 individuals increased imputation accuracy by 8% when pedigree relationship was used as a covariate and 6% when molecular relationship was used as a covariate. We concluded that imputation accuracy would be increased through optimization of reduced panel design and genotyping strategy.}, number={12}, journal={JOURNAL OF ANIMAL SCIENCE}, author={Huang, Y. and Maltecca, C. and Cassady, J. P. and Alexander, L. J. and Snelling, W. M. and MacNeil, M. D.}, year={2012}, month={Dec}, pages={4203–4208} }
 @article{gray_maltecca_bagnato_dolezal_rossoni_samore_cassady_2012, title={Estimates of marker effects for measures of milk flow in the Italian brown Swiss dairy cattle population}, volume={8}, ISSN={["1746-6148"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84867661823&partnerID=MN8TOARS}, DOI={10.1186/1746-6148-8-199}, abstractNote={Abstract Background Milkability is a complex trait that is characterized by milk flow traits including average milk flow rate, maximum milk flow rate and total milking time. Milkability has long been recognized as an economically important trait that can be improved through selection. By improving milkability, management costs of milking decrease through reduced labor and improved efficiency of the automatic milking system, which has been identified as an important factor affecting net profit. The objective of this study was to identify markers associated with electronically measured milk flow traits, in the Italian Brown Swiss population that could potentially improve selection based on genomic predictions. Results Sires (n = 1351) of cows with milk flow information were genotyped for 33,074 single nucleotide polymorphism (SNP) markers distributed across 29 Bos taurus autosomes (BTA). Among the six milk flow traits collected, ascending time, time of plateau, descending time, total milking time, maximum milk flow and average milk flow, there were 6,929 (time of plateau) to 14,585 (maximum milk flow) significant SNP markers identified for each trait across all BTA. Unique regions were found for each of the 6 traits providing evidence that each individual milk flow trait offers distinct genetic information about milk flow. This study was also successful in identifying functional processes and genes associated with SNPs that influences milk flow. Conclusions In addition to verifying the presence of previously identified milking speed quantitative trait loci (QTL) within the Italian Brown Swiss population, this study revealed a number of genomic regions associated with milk flow traits that have never been reported as milking speed QTL. While several of these regions were not associated with a known gene or QTL, a number of regions were associated with QTL that have been formerly reported as regions associated with somatic cell count, somatic cell score and udder morphometrics. This provides further evidence of the complexity of milk flow traits and the underlying relationship it has with other economically important traits for dairy cattle. Improved understanding of the overall milking pattern will aid in identification of cows with lower management costs and improved udder health.}, journal={BMC VETERINARY RESEARCH}, author={Gray, Kent A. and Maltecca, Christian and Bagnato, Alessandro and Dolezal, Marlies and Rossoni, Attilio and Samore, Antonia B. and Cassady, Joseph P.}, year={2012}, month={Oct} }
 @article{tiezzi_maltecca_cecchinato_penasa_bittante_2012, title={Genetic parameters for fertility of dairy heifers and cows at different parities and relationships with production traits in first lactation}, volume={95}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84869490318&partnerID=MN8TOARS}, DOI={10.3168/jds.2012-5775}, abstractNote={The objectives of this study were to estimate genetic parameters for fertility of Brown Swiss cattle, considering reproductive measures in different parities as different traits, and to estimate relationships between production traits of first lactation and fertility of heifers and first-parity and second-parity cows. Reproductive indicators were interval from parturition to first service, interval from first service to conception, interval from parturition to conception, number of inseminations to conception, conception rate at first service, and nonreturn rate at 56 d after first service. Production traits were peak milk yield (pMY), lactation milk yield, and lactation length (LL). Data included 37,546 records on heifers, and 24,098 and 15,653 records on first- and second-parity cows, respectively. Cows were reared in 2,035 herds, calved from 1999 to 2007, and were progeny of 527 AI bulls. Gibbs sampling was implemented to obtain (co)variance components using both univariate and bivariate threshold and censored linear sire models. Estimates of heritability for reproductive traits in heifers (0.016 to 0.026) were lower than those in first-parity (0.017 to 0.142) and second-parity (0.026 to 0.115) cows. Genetic correlations for fertility in first- and second-parity cows were very high (>0.920), whereas those between heifers and lactating cows were moderate (0.348 to 0.709). The latter result indicates that fertility in heifers is a different trait than fertility in lactating cows, and hence it cannot be used as robust indicator of cow fertility. Heifer fertility was not related to production traits in first lactation (genetic correlations between -0.215 and 0.251). Peak milk yield exerted a moderate and unfavorable effect on the interval from parturition to first service (genetic correlations of 0.414 and 0.353 after first and second calving, respectively), and a low and unfavorable effect on other fertility traits (genetic correlations between -0.281 and 0.295). Infertility after first calving caused a strong elongation of the lactation, and LL was negatively correlated with fertility of cows after second calving, so that LL can itself be regarded as a measure of fertility. Lactation milk yield depends on both pMY and LL, and, as such, is a cause and consequence of (in)fertility.}, number={12}, journal={Journal of Dairy Science}, author={Tiezzi, F. and Maltecca, C. and Cecchinato, A. and Penasa, M. and Bittante, G.}, year={2012}, pages={7355–7362} }
 @article{gaddis_cole_clay_maltecca_2012, title={Incidence validation and relationship analysis of producer-recorded health event data from on-farm computer systems in the United States}, volume={95}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84865338998&partnerID=MN8TOARS}, DOI={10.3168/jds.2012-5572}, abstractNote={<h2>Abstract</h2> The principal objective of this study was to analyze the plausibility of health data recorded through on-farm recording systems throughout the United States. Substantial progress has been made in the genetic improvement of production traits while health and fitness traits of dairy cattle have declined. Health traits are generally expensive and difficult to measure, but health event data collected from on-farm computer management systems may provide an effective and low-cost source of health information. To validate editing methods, incidence rates of on-farm recorded health event data were compared with incidence rates reported in the literature. Putative relationships among common health events were examined using logistic regression for each of 3 timeframes: 0 to 60, 61 to 90, and 91 to 150 d in milk. Health events occurring on average before the health event of interest were included in each model as predictors when significant. Calculated incidence rates ranged from 1.37% for respiratory problems to 12.32% for mastitis. Most health events reported had incidence rates lower than the average incidence rate found in the literature. This may partially represent underreporting by dairy farmers who record disease events only when a treatment or other intervention is required. Path diagrams developed using odds ratios calculated from logistic regression models for each of 13 common health events allowed putative relationships to be examined. The greatest odds ratios were estimated to be the influence of ketosis on displaced abomasum (15.5) and the influence of retained placenta on metritis (8.37), and were consistent with earlier reports. The results of this analysis provide evidence for the plausibility of on-farm recorded health information.}, number={9}, journal={JOURNAL OF DAIRY SCIENCE}, author={Gaddis, K. L. Parker and Cole, J. B. and Clay, J. S. and Maltecca, C.}, year={2012}, month={Sep}, pages={5422–5435} }
 @article{gonda_fang_perry_maltecca_2012, title={Measuring bovine viral diarrhea virus vaccine response: Using a commercially available ELISA as a surrogate for serum neutralization assays}, volume={30}, ISSN={["1873-2518"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84867013307&partnerID=MN8TOARS}, DOI={10.1016/j.vaccine.2012.08.047}, abstractNote={Genetic selection in livestock offers the opportunity to improve bovine viral diarrhea virus (BVDV) vaccine response, but first we must define how vaccine response should be measured. For measuring humoral vaccine response, serum neutralization (SN) measures antibodies that can neutralize BVDV, but relative to enzyme-linked immunosorbent assay (ELISA) is time consuming, technically demanding, and expensive. The ELISA, however, measures total BVDV-specific antibodies, regardless of whether the antibodies can neutralize BVDV. Our objective was to test whether a commercially available BVDV antibody ELISA could be used as a surrogate (or indicator trait) for neutralizing antibodies as measured by SN. Angus and Angus-cross calves (n = 193) from two South Dakota research herds were vaccinated for BVDV-1 and BVDV-2. Sera and plasma samples (n = 406) were collected from these calves at the time of vaccination and post-vaccination (20–72 days post-vaccination). The BVDV-specific antibody concentration was measured on each serum and plasma sample by (1) a commercially available total antibody ELISA, (2) BVDV-1 SN, and (3) BVDV-2 SN. Correlation between the ELISA and SN tests was estimated with a Spearman correlation coefficient. Higher BVDV ELISA sample-to-positive (S/P) ratios were positively correlated with higher BVDV-1 (ρ = 0.809) and BVDV-2 (ρ = 0.638) SN titers (P < 0.0001), although the relationship was weaker when SN titers were <1:64. Higher BVDV-1 SN titers were also positively correlated with higher BVDV-2 SN titers (ρ = 0.708; P < 0.0001). The correlation between ELISA S/P ratios and SN titers was lower when calves were ≤2 months of age (ρ = 0.344–0.566). Our results suggest that increased ELISA S/P ratios were associated with higher SN titers. We conclude that this BVDV antibody ELISA can be used as a surrogate for BVDV-1 and -2 SN titers when investigating genetic determinants of vaccine response, as long as samples are collected at 2 months of age or older.}, number={46}, journal={VACCINE}, author={Gonda, M. G. and Fang, X. and Perry, G. A. and Maltecca, C.}, year={2012}, month={Oct}, pages={6559–6563} }
 @article{zapata-valenzuela_isik_maltecca_wegrzyn_neale_mckeand_whetten_2012, title={SNP markers trace familial linkages in a cloned population of Pinus taeda-prospects for genomic selection}, volume={8}, ISSN={["1614-2950"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84869878413&partnerID=MN8TOARS}, DOI={10.1007/s11295-012-0516-5}, abstractNote={Advances in DNA sequencing technology have made possible the genotyping of thousands of single-nucleotide polymorphism (SNP) markers, and new methods of statistical analysis are emerging to apply these advances in plant breeding programs. We report the utility of markers for prediction of breeding values in a forest tree species using empirical genotype data (3,406 polymorphic SNP loci). A total of 526 Pinus taeda L. clones tested widely in field trials were phenotyped at age 5 years. Only 149 clones from 13 full-sib crosses were genotyped. Markers were fit simultaneously to predict marker additive and dominance effects. Subsets of the 149 genotyped clones were used to train a model using all markers. Cross-validation strategies were followed for the remaining subset of genotyped individuals. The accuracy of genomic estimated breeding values ranged from 0.61 to 0.83 for wood lignin and cellulose content, and from 0.30 to 0.68 for height and volume traits. The accuracies of predictions based on markers were comparable with the accuracies based on pedigree. Because of the small number of SNP markers used and the relatively small population size, we suggest that observed accuracies in this study trace familial linkage rather than historical linkage disequilibrium with trait loci. Prediction accuracies of models that use only a subset of markers were generally comparable with the accuracies of the models using all markers, regardless of whether markers are associated with the phenotype. The results suggest that using SNP loci for selection instead of phenotype is efficient under different relative lengths of the breeding cycle, which would allow cost-effective applications in tree breeding programs. Prospects for applications of genomic selection to P. taeda breeding are discussed.}, number={6}, journal={TREE GENETICS & GENOMES}, publisher={Springer Nature}, author={Zapata-Valenzuela, Jaime and Isik, Fikret and Maltecca, Christian and Wegrzyn, Jill and Neale, David and McKeand, Steve and Whetten, Ross}, year={2012}, month={Dec}, pages={1307–1318} }
 @article{huang_maltecca_macneil_alexander_snelling_cassady_2012, title={Using 50 K Single Nucleotide Polymorphisms to Elucidate Genomic Architecture of Line 1 Hereford Cattle}, volume={3}, ISSN={1664-8021}, url={http://dx.doi.org/10.3389/fgene.2012.00285}, DOI={10.3389/fgene.2012.00285}, abstractNote={Hereford is a major beef breed in the USA, and a sub-population, known as Line 1 (L1), was established in 1934 using two paternal half-sib bulls and 50 unrelated females. L1 has since been maintained as a closed population and selected for growth to 1 year of age. Objectives were to characterize the molecular genetic architecture of L1 (n = 240) by comparing a cross-section of L1 with the general US. Hereford population (AHA, n = 311), estimating effects of imposed selection within L1 based on allele frequencies at 50 K SNP loci, and examining loci-specific effects of heterozygosity on the selection criterion. Animals were genotyped using the Illumina BovineSNP50 Beadchip, and SNP were mapped to UMD3.0 assembly of the bovine genome sequence. Average linkage disequilibrium (LD), measured by square of Pearson correlation, of adjacent SNP was 0.36 and 0.16 in L1 and AHA, respectively. Difference in LD between L1 and AHA decreased as SNP spacing increased. Persistence of phase between L1 and AHA decreased from 0.45 to 0.14 as SNP spacing increased from 50 to 5,000 kb. Extended haplotype homozygosity was greater in L1 than in AHA for 95.6% of the SNP. Knowledge of selection applied to L1 facilitated a novel approach to QTL discovery. Minor allele frequency was (FDR < 0.01) affected by cumulative selection differential at 191 out of 25,901 SNP. With the FDR relaxed to 0.05, 13 regions on BTA2, 5, 6, 9, 11, 14, 15, 18, 23, and 26 are co-located with previously identified QTL for growth. After adjustment of postweaning gain phenotypes for fixed effects and direct additive genetic effects, regression of residuals on genome-wide heterozygosity was -235.3 ± 91.6 kg. However, no SNP-specific loci where heterozygotes were significantly superior to the average of homozygotes were revealed (FDR ≥ 0.17). In conclusion, genome-wide SNP genotypes clarified effects of selection and inbreeding within L1 and differences in genomic architecture between the population segment L1 and the AHA population.}, number={DEC}, journal={Frontiers in Genetics}, publisher={Frontiers Media SA}, author={Huang, Y. and Maltecca, C. and MacNeil, M. D. and Alexander, L. J. and Snelling, W. M. and Cassady, J. P.}, year={2012} }
 @article{maltecca_gray_weigel_cassady_ashwell_2011, title={A genome-wide association study of direct gestation length in US Holstein and Italian Brown populations}, volume={42}, ISSN={["0268-9146"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-80255123496&partnerID=MN8TOARS}, DOI={10.1111/j.1365-2052.2011.02188.x}, abstractNote={Direct gestation length influences economically important traits in dairy cattle that are related to birth and peri-natal survival of the calf. The objective of this study was to identify single nucleotide polymorphisms (SNPs) that are significantly associated with direct gestation length through a genome-wide association study. Data used in the analysis included 7 308 194 cow gestation lengths from daughters of 4743 United States Holstein sires in the Cooperative Dairy DNA Repository population and 580 157 gestation lengths from 749 sires in the Italian Brown population. Association analysis included 36 768 and 35 082 SNPs spanning all autosomes for Holstein and Brown Swiss, respectively. Multiple shrinkage Bayesian was employed. Estimates of heritability for both populations were moderate, with values of 0.32 (±0.03) and 0.29 (±0.02) for Holstein and Brown Swiss, respectively. A panel of SNPs was identified, which included SNPs that have significant effects on direct gestation length, of which the strongest candidate region is located on chromosome 18. Two regions not previously linked to direct calving ease and calf survival were identified on chromosome 7 and 28, corresponding to regions that contain genes related to embryonic development and foetal development. SNPs were also identified in regions that have been previously mapped for calving difficulty and longevity. This study identifies target regions for the investigation of direct foetal effects, which are a significant factor in determining the ease of calving.}, number={6}, journal={ANIMAL GENETICS}, author={Maltecca, C. and Gray, K. A. and Weigel, K. A. and Cassady, J. P. and Ashwell, M.}, year={2011}, month={Dec}, pages={585–591} }
 @article{samore_roman-ponce_vacirca_frigo_canavesi_bagnato_maltecca_2011, title={Bimodality and the genetics of milk flow traits in the Italian Holstein-Friesian breed}, volume={94}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-79960622116&partnerID=MN8TOARS}, DOI={10.3168/jds.2010-3611}, abstractNote={The overall goal of this study was to investigate milk flow traits in Italian Holstein-Friesian cows and, in particular, the bimodality of milk flow, defined as delayed milk ejection at the start of milking. Using a milkometer, 2,886 records were collected from 133 herds in northern Italy from 2001 to 2007. All records included 5 time-period measurements for milk flow, somatic cell score (SCS), milk yield, 8 udder type traits, and the presence or absence of bimodality in milk flow. Genetic parameters were estimated using linear animal models for continuous traits such as milk flow, udder type, SCS, and milk production, whereas bimodality was analyzed as a categorical trait. With the exception of decreasing time (which had a very small heritability value of 0.06), heritability values for milk flow traits were moderate, ranging from 0.10 (ascending time) to 0.41 (maximum milk flow). In addition, moderate to high genetic correlations were estimated between total milking time and other time measures (from 0.78 to 0.87), and among time flow traits (from 0.62 to 0.91). The decreasing time was the trait most genetically correlated with udder type traits, with correlation values of 0.92 with rear udder height, 0.85 with rear udder width, and 0.73 with teat placement. Large udders with strong attachments were also associated with greater milk production. Heritability estimated for bimodality was 0.43, and its genetic correlation with milk flow traits and SCS indicated a sizable genetic component underlying this trait. Bimodality was negatively associated with milk production; shorter milking times and greater peak milk levels were genetically correlated with more frequent bimodal flows, indicating that faster milk release would result in an increase in bimodal patterns. The negative genetic correlation of bimodality with SCS (-0.30) and the genetic correlation between milk flow traits and SCS suggest that the relationship between milkability and SCS is probably nonlinear and that intermediate flow rates are optimal with respect to mastitis susceptibility. Quicker milk flow over a shorter period would increase the frequency of bimodal curves in milking, whereas the correlation between bimodality and both ascending and descending time was less clear.}, number={8}, journal={JOURNAL OF DAIRY SCIENCE}, author={Samore, A. B. and Roman-Ponce, S. I. and Vacirca, F. and Frigo, E. and Canavesi, F. and Bagnato, A. and Maltecca, C.}, year={2011}, month={Aug}, pages={4081–4089} }
 @article{yoder_maltecca_cassady_flowers_price_see_2011, title={Breed differences in pig temperament scores during a performance test and their phenotypic relationship with performance}, volume={136}, ISSN={["1878-0490"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-79951959362&partnerID=MN8TOARS}, DOI={10.1016/j.livsci.2010.08.004}, abstractNote={Nucleus populations of Chester White, Duroc, Landrace, and Yorkshire boars and gilts (n = 4774) were used to estimate breed differences in temperament and the relationship with performance. Adjusted backfat, adjusted loin depth, days to 113.4 kilograms (DAYS), estimated percent fat-free lean (LEAN), and three temperament scores: load score, scale score, and vocal score were recorded, on a scale of 1 (calm) to 5 (highly excited), during a performance test. Logistic regression for temperament scores included fixed effects of breed, sex, contemporary group (barn-farm-test date), and body weight as a covariate was used. Order that a pig was loaded into the scale, within pen, was included as a fixed effect for load score. After initial analysis, it was determined that vocal score was best described as two categories, vocal or non-vocal, and was reanalyzed accordingly. Linear mixed models for backfat, loin depth, DAYS, and LEAN included fixed effects of breed, sex, and load score, scale score, or vocal score. Growth rate was adjusted to 113.4 kg, while backfat and loin depth were adjusted to 113.4 kg through regression on mean body weight of the respective breed. The odds of increased load score were greater for Landrace (1.62, 1.30; P < 0.01) than Duroc and Yorkshire respectively. Landrace had a greater (P < 0.01) probability of a higher scale and vocal scores compared to Chester White (1.77, 2.37), Duroc (3.31, 3.94) and Yorkshire (2.51, 2.46). Yorkshire had greater (P < 0.01) odds of increased load score (1.25), SS (1.32), and vocal score (1.60) than Duroc. Chester White had greater odds of increased load score and scale score than Duroc (1.58, 1.87) and Yorkshire (1.26, 1.42), respectively. Chester White were 1.66 (P < 0.01) times more likely to have a higher vocal scores than Duroc. Phenotypic correlations (P < 0.01) for scale score with load score, vocal score, backfat, loin depth, DAYS, and LEAN were 0.13, 0.32, − 0.15, − 0.07, 0.10 and 0.17, respectively. Landrace were more excited and vocal in the scale than Chester White, Durocs and Yorkshire. Landrace were more difficult to load into the scale than Durocs and Yorkshire. Chester White were more active in the scale than Durocs and Yorshire, and more vocal than Durocs. Yorkshire were harder to load, more active and vocal than Durocs. It was concluded that temperament differs between breeds, and pigs with lower temperament scores were fatter, had greater loin depth and grew faster.}, number={2-3}, journal={LIVESTOCK SCIENCE}, author={Yoder, C. L. and Maltecca, C. and Cassady, J. P. and Flowers, W. L. and Price, S. and See, M. T.}, year={2011}, month={Apr}, pages={93–101} }
 @article{gray_smith_maltecca_overton_parish_cassady_2011, title={Differences in hair coat shedding, and effects on calf weaning weight and BCS among Angus dams}, volume={140}, ISSN={["1871-1413"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-79960901958&partnerID=MN8TOARS}, DOI={10.1016/j.livsci.2011.02.009}, abstractNote={The objective of the study was to assess variation in hair coat shedding of Angus cows, and its effect on adjusted weaning weight (d205wt) and BCS. Data were available from 532 Angus cows over 3 years of age. Beginning in March and for 5 months at 30-d intervals, trained technicians scored cows on a scale from 1 to 5, with 1 representing slick coats and 5 winter coats. For each cow, the first month with a score of 3 or less (MFS, 5 levels) was considered the beginning of winter coat shedding and used in the analyses. Association between MFS and d205WT or BCS, was investigated using the mixed procedure of SAS. Data were further analyzed by dividing cows into two groups, group one (Group 1) were cows with a shedding score of 3 or less by June 1st and group two (Group 2) consisted of cows with a shedding score of 4 or 5 on June 1st (AS, 2 levels). Calves from Group1 dams were 11.1 ± 2.8 kg heavier at weaning (P < 0.01) than calves from Group 2 dams. No significant differences were found between shedding score and BCS. Variance components were estimated using THRGIBBS1F90 and heritability of AS was calculated (h2 = 0.35) with a moderate genetic correlation with d205WT (rg = − 0.58). Hair coat shedding is a heritable trait and could be altered by selection. Producers within the Southeastern or Southern United States who are concerned about heat stress may want to select for cattle that shed their winter hair coat earlier in the season. In conclusion, cows who shed their winter coat by June 1st will wean heavier calves on average.}, number={1-3}, journal={LIVESTOCK SCIENCE}, author={Gray, K. A. and Smith, T. and Maltecca, C. and Overton, P. and Parish, J. A. and Cassady, J. P.}, year={2011}, month={Sep}, pages={68–71} }
 @article{tiezzi_penasa_maltecca_cecchinato_bittante_2011, title={Exploring Different Model Structures for the Genetic Evaluation of Dairy Bull Fertility}, volume={76}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-80053913763&partnerID=MN8TOARS}, number={3}, journal={Agriculturae Conspectus Scientificus}, author={Tiezzi, Francesco and Penasa, M. and Maltecca, C. and Cecchinato, A. and Bittante, G.}, year={2011}, pages={239–243} }
 @article{tiezzi_maltecca_penasa_cecchinato_chang_bittante_2011, title={Genetic analysis of fertility in the Italian Brown Swiss population using different models and trait definitions}, volume={94}, ISSN={["0022-0302"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-82155171333&partnerID=MN8TOARS}, DOI={10.3168/jds.2011-4661}, abstractNote={The aim of this study was to estimate genetic parameters for fertility and production traits in the Brown Swiss population reared in the Alps (Bolzano-Bozen province, Italy). Fertility indicators were interval from parturition to first service, interval from first service to conception (iFC), and interval from parturition to conception, either expressed as days and as number of potential 21-d estrus cycles (cPF, cFC, and cPC, respectively); number of inseminations to conception; conception rate at first service; and non-return rate at 56 d post-first service. Production traits were peak milk yield, lactation milk yield, lactation length, average lactation protein percentage, and average lactation fat percentage. Data included 71,556 lactations (parities 1 to 9) from 29,582 cows reared in 1,835 herds. Animals calved from 1999 to 2007 and were progeny of 491 artificial insemination bulls. Gibbs sampling and Metropolis algorithms were implemented to obtain (co)variance components using both univariate and bivariate censored threshold and linear sire models. All of the analyses accounted for parity and year-month of calving as fixed effects, and herd, permanent environmental cow, additive genetic sire, and residual as random effects. Heritability estimates for fertility traits ranged from 0.030 (iFC) to 0.071 (cPC). Strong genetic correlations were estimated between interval from parturition to first service and cPF (0.97), and interval from parturition to conception and cPC (0.96). The estimate of heritability for cFC (0.055) was approximately double compared with iFC (0.030), suggesting that measuring the elapsed time between first service and conception in days or potential cycles is not equivalent; this was also confirmed by the genetic correlation between iFC and cFC, which was strong (0.85), but more distant from unity than the other 2 pairs of fertility traits. Genetic correlations between number of inseminations to conception, conception rate at first service, non-return rate at 56 d post-first service, cPF, cFC, and cPC ranged from 0.07 to 0.82 as absolute value. Fertility was unfavorably correlated with production; estimates ranged from -0.26 (cPC with protein percentage) to 0.76 (cPC with lactation length), confirming the genetic antagonism between reproductive efficiency and milk production. Although heritability for fertility is low, the contemporary inclusion of several reproductive traits in a merit index would help to improve performance of dairy cows.}, number={12}, journal={JOURNAL OF DAIRY SCIENCE}, author={Tiezzi, F. and Maltecca, C. and Penasa, M. and Cecchinato, A. and Chang, Y. M. and Bittante, G.}, year={2011}, month={Dec}, pages={6162–6172} }
 @article{gray_vacirca_bagnato_samore_rossoni_maltecca_2011, title={Genetic evaluations for measures of the milk-flow curve in the Italian Brown Swiss population}, volume={94}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-78751697355&partnerID=MN8TOARS}, DOI={10.3168/jds.2009-2759}, abstractNote={The objective of this study was to estimate heritabilities and genetic correlations between milk-release parameters, somatic cell score, milk yield, and udder functional traits in the Italian Brown Swiss population. Data were available from 37,511 cows over a span of 12 yr (1997-2008) from 1,592 herds. Milking flows were recorded for each individual once during lactation. Three different analyses were performed to estimate variance components for all the traits of interest. The first analysis included single control data milk yield, somatic cell score, maximum milk flow, average milk flow, time of plateau, decreasing time, and total milking time, whereas the second analysis included milk-release parameters as well as total udder score, udder depth, and 305-d milk yield and somatic cell score as dependent variables. The third analysis included total milking time, 305-d milk yield and somatic cell score, total udder score, udder depth, and ratios of maximum milk flow over total milking time (R1), time of plateau (R2), and decreasing time (R3) to estimate the relationship between the shape of the milk-release curves and important milking traits. Results from the first and second analysis found similar heritabilities for milkability traits ranging from 0.05 to 0.41 with genetic correlations between production traits and flow traits ranging from low to moderate values. Positive genetic correlations were found among production, somatic cell score, and milkability traits. The third analysis showed that R1 had the greatest heritability of the ratio traits (0.37) with large genetic correlations with R2 and R3, a low correlation with 305-d somatic cell score, and no correlation with 305-d milk yield. Estimated responses to selection over 5 generations were also calculated using different indexes, which included either flow or ratio traits. The results of this study show that it is possible to use information collected through portable flowmeters to improve milkability traits. Using a set of variables or traits to describe the overall release of milk can be an advantageous selection strategy to decrease management costs while maintaining milk production.}, number={2}, journal={JOURNAL OF DAIRY SCIENCE}, author={Gray, K. A. and Vacirca, F. and Bagnato, A. and Samore, A. B. and Rossoni, A. and Maltecca, C.}, year={2011}, month={Feb}, pages={960–970} }
 @article{zapata-valenzuela_isik_maltecca_wegryzn_neale_mckeand_whetten_2011, title={Genomic selection using a realized genomic relationship matrix in a Pinus taeda L. cloned population}, volume={5}, ISSN={1753-6561}, url={http://dx.doi.org/10.1186/1753-6561-5-s7-p60}, DOI={10.1186/1753-6561-5-s7-p60}, abstractNote={Genetic merit can be considered the finite sum of thousands of allelic effects, each physically located at some place on the genome, whose transmission can be traced through molecular markers.Traditionally, best linear unbiased prediction (BLUP) of breeding values relies on average additive genetic covariances (the numerator relationship matrix A) derived from pedigrees to utilize information from relatives.For example, all pairs of fullsib offspring of a cross are assumed to share 50% of alleles in common.Such assumptions ignore variation in Mendelian segregation of alleles among progeny within family.With advances in marker genotyping technology and reduction in genotyping cost, it is now feasible to estimate genetic covariances from markers.Linear mixed models that utilize realized genomic relationship matrices could predict genomic estimated breeding values (GEBV) more accurately than those that use expected average genetic covariances derived from pedigrees.Dense markers can be used to trace identity by descent probabilities at each locus, and those probabilities used to construct an incidence matrix.The incidence matrix is used to estimate the genomic relationship matrix (G), which is used in place of the A matrix in solving the mixed model equations.This may allow more accurate estimation of individual breeding values than the traditional model based on average genetic covariances.We estimated realized genetic covariances between cloned progeny of a P. taeda population.There were 165 cloned progeny derived from nine full-sib families.}, number={S7}, journal={BMC Proceedings}, publisher={Springer Science and Business Media LLC}, author={Zapata-Valenzuela, Jaime and Isik, Fikret and Maltecca, Christian and Wegryzn, Jill and Neale, David and McKeand, Steven and Whetten, Ross}, year={2011}, month={Sep} }
 @article{ashwell_fry_spears_o'nan_maltecca_2011, title={Investigation of breed and sex effects on cytochrome P450 gene expression in cattle liver}, volume={90}, ISSN={["0034-5288"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-79951771053&partnerID=MN8TOARS}, DOI={10.1016/j.rvsc.2010.05.029}, abstractNote={Many cytochrome P450 enzymes are involved in xenobiotic metabolism and elimination. In humans, genetic variation in some of these enzymes contributes to inter-individual drug responses, sometimes having significant clinical effects. Transcript levels of eight P450 genes were evaluated in liver to investigate potential differences in breed and sex in cattle. In Angus calves, heifers appeared to have higher gene expression than steers for two of the eight genes. In Angus and Simmental pregnant cows, Angus appeared to have higher gene expression for three of the eight genes. Transcript evaluation is just the first step toward determining if differences exist between breeds and sexes in enzyme catalytic activity. However, others (Giantin et al., 2008) have shown correlations between transcript levels and catalytic activity in other cattle breeds. Therefore breed and/or sex of an animal may need to be considered before administering a dose of a xenobiotic due to the potential for harmful drug residues in foodstuffs as well as improper treatment of disease conditions.}, number={2}, journal={RESEARCH IN VETERINARY SCIENCE}, author={Ashwell, M. S. and Fry, R. S. and Spears, J. W. and O'Nan, A. T. and Maltecca, C.}, year={2011}, month={Apr}, pages={235–237} }
 @inproceedings{tiezzi_maltecca_2011, title={Selecting for female fertility: What can be learned from the dairy experince}, booktitle={Proceedings of the Beef Improvement Federation's 43rd Annual Research Symposium and Annual Meeting}, author={Tiezzi, F. and Maltecca, C.}, year={2011}, pages={47–60} }
 @inproceedings{tiezzi_penasa_cecchinato_maltecca_bittante_2011, title={Threshold and linear models for the genetic analysis of bull fertility in the Italian Brown Swiss population}, booktitle={Book of Abstracts of the 62nd Annual Meeting of the European Association for Animal Production}, publisher={Wageningen Academic Publishers}, author={Tiezzi, F. and Penasa, M. and Cecchinato, A. and Maltecca, C. and Bittante, G.}, year={2011}, pages={174–174} }
 @misc{maltecca_parker_cassandro_2010, title={Accomplishments and new challenges in dairy genetic evaluations}, volume={9}, ISSN={["1828-051X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-78449279892&partnerID=MN8TOARS}, DOI={10.4081/ijas.2010.e68}, abstractNote={This review presents the evolution of dairy genetic methods to estimate breeding values. For centuries, human action has shaped animal populations by choosing progenitors of the next generation. Since the twentieth century, applied concepts were integrated into a new discipline, quantitative genetics. The past quarter-century in genetic evaluation of dairy cattle has been marked by evolution in methodology and computer capacity, expansion in the array of evaluated traits, and globalization. Selection index was replaced by mixed model procedures and animal models replaced sire and sire-maternal grandsire models. Recently, application of Bayesian theory to breeding values prediction and variance components estimation has become standard. Individual test-day observations have been used more effectively in the estimation of lactation yield as direct input to evaluation models. Computer speed and storage are less limiting in choosing procedures. National evaluations combined internationally provide evaluations for bulls from all participating countries on each of the national scales, facilitating choices from among many more bulls. Selection within countries has increased inbreeding and the use of similar genetics across countries reduces the previously available genetic diversity. Finally, considerable progress in genomics has created a new tool, genomic selection. The collection and analysis of several types of phenotypic data to evaluate genetic merit will continue to be the most important tool for genetic progress in the foreseeable future. Information will increasingly be obtained from smaller reference populations and the extrapolation from these data will require careful validation.}, number={4}, journal={ITALIAN JOURNAL OF ANIMAL SCIENCE}, author={Maltecca, Christian and Parker, Kristen L. and Cassandro, Martino}, year={2010}, pages={360–368} }
 @book{gray_cassady_maltecca_overton_parish_smith_2010, place={Mississippi State University}, title={Differences in Hair Coat Shedding and Effects on Calf Weaning Weight and Body Condition Score among Angus Dams}, journal={Mississippi State University Animal and Dairy Sciences Department Report}, institution={Starkville, MS}, author={Gray, K.A. and Cassady, J.P. and Maltecca, C. and Overton, P. and Parish, J.A. and Smith, T.}, year={2010}, pages={24–31} }
 @article{cleveland_forni_deeb_maltecca_2010, title={Genomic breeding value prediction using three Bayesian methods and application to reduced density marker panels}, volume={4}, ISSN={1753-6561}, url={http://dx.doi.org/10.1186/1753-6561-4-s1-s6}, DOI={10.1186/1753-6561-4-s1-s6}, abstractNote={Bayesian approaches for predicting genomic breeding values (GEBV) have been proposed that allow for different variances for individual markers resulting in a shrinkage procedure that uses prior information to coerce negligible effects towards zero. These approaches have generally assumed application to high-density genotype data on all individuals, which may not be the case in practice. In this study, three approaches were compared for their predictive power in computing GEBV when training at high SNP marker density and predicting at high or low densities: the well- known Bayes-A, a generalization of Bayes-A where scale and degrees of freedom are estimated from the data (Student-t) and a Bayesian implementation of the Lasso method. Twelve scenarios were evaluated for predicting GEBV using low-density marker subsets, including selection of SNP based on genome spacing or size of additive effect and the inclusion of unknown genotype information in the form of genotype probabilities from pedigree and genotyped ancestors. The GEBV accuracy (calculated as correlation between GEBV and traditional breeding values) was highest for Lasso, followed by Student-t and then Bayes-A. When comparing GEBV to true breeding values, Student-t was most accurate, though differences were small. In general the shrinkage applied by the Lasso approach was less conservative than Bayes-A or Student-t, indicating that Lasso may be more sensitive to QTL with small effects. In the reduced-density marker subsets the ranking of the methods was generally consistent. Overall, low-density, evenly-spaced SNPs did a poor job of predicting GEBV, but SNPs selected based on additive effect size yielded accuracies similar to those at high density, even when coverage was low. The inclusion of genotype probabilities to the evenly-spaced subsets showed promising increases in accuracy and may be more useful in cases where many QTL of small effect are expected. In this dataset the Student-t approach slightly outperformed the other methods when predicting GEBV at both high and low density, but the Lasso method may have particular advantages in situations where many small QTL are expected. When markers were selected at low density based on genome spacing, the inclusion of genotype probabilities increased GEBV accuracy which would allow a single low- density marker panel to be used across traits.}, number={S1}, journal={BMC Proceedings}, publisher={Springer Science and Business Media LLC}, author={Cleveland, Matthew A and Forni, Selma and Deeb, Nader and Maltecca, Christian}, year={2010}, month={Mar} }
 @article{velie_maltecca_cassady_2009, title={Genetic relationships among pig behavior, growth, backfat, and loin muscle area}, volume={87}, ISSN={["1525-3163"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-70249140947&partnerID=MN8TOARS}, DOI={10.2527/jas.2008-1328}, abstractNote={The objective of this study was to estimate repeatabilities and heritabilities for measures of pig behavior and their relationship with performance. Measures of behavior and performance included the backtest, resident-intruder test, human approach test (HAT), novel object test (NOT), d 1 BW, backfat depth (BF), loin muscle area (LMA), ADG in the farrowing house, ADG, 21-d BW, and 140-d BW (W). Each behavioral trait was measured twice. The study consisted of 95 litters from 31 sires with an average of 3 litters per sire (n >or= 457). Between 7 and 14 d of age, the backtest was conducted by placing each pig in a supine position for 60 s. Total time spent struggling (TTS) and total number of attempts to struggle (TAS) were recorded. The resident intruder test involved 2 nursery pigs, a resident pig and an unfamiliar intruder pig. The resident pen was divided in half by a solid partition. A resident pig was placed in the test area, and an intruder pig was then introduced. Latency until an attack occurred (LAT) and total number of attacks over 2 tests (RIS) were recorded. Amount of time taken for each finishing pig to make snout contact with an unfamiliar human or object was recorded. Dam and sire effects influenced all traits (P < 0.01). Sex and pen affected LAT, RIS, HAT, and NOT (P < 0.10). Repeatabilities of TTS, TAS, RIS, LAT, HAT, and NOT were 0.38, 0.21, 0.07, 0.08, 0.17, and 0.11, respectively. The phenotypic correlations of TTS with TAS and HAT with NOT were 0.61 and 0.34, respectively. Phenotypic correlation between RIS and LAT was -0.85. Total time spent struggling and TAS tended to be phenotypically correlated with 21-d BW and ADG in the farrowing house. Total attempts to struggle were phenotypically correlated with BF (0.15). Latency until an attack occurred was phenotypically correlated with LMA (0.23). Resident intruder score was phenotypically correlated with ADG (-0.13), W (-0.13), and LMA (-0.21) and estimated to be lowly heritable (h(2) = 0.12). Heritabilities of TTS and TAS were 0.31 and 0.53, respectively. Genetic correlation of TAS with ADG and W was 0.38. Genetic correlations of TTS with BF, W, and TAS were 0.14, 0.18, and 0.81, respectively. The backtest is a heritable and repeatable measure of a behavioral characteristic in pigs that is phenotypically and genetically correlated with performance.}, number={9}, journal={JOURNAL OF ANIMAL SCIENCE}, author={Velie, B. D. and Maltecca, C. and Cassady, J. P.}, year={2009}, month={Sep}, pages={2767–2773} }
 @article{khatib_maltecca_monson_schutzkus_rutledge_2009, title={Monoallelic maternal expression of STAT5A affects embryonic survival in cattle}, volume={10}, ISSN={["1471-2156"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-63449127928&partnerID=MN8TOARS}, DOI={10.1186/1471-2156-10-13}, abstractNote={Reproductive disorders and infertility are surprisingly common in the human population as well as in other species. The decrease in fertility is a major cause of cow culling and economic loss in the dairy herd. The conception rate has been declining for the past 30–50 years. Conception rate is the product of fertilization and embryonic survival rates. In a previous study, we have identified associations of several single nucleotide polymorphisms (SNPs) in the signal transducer and activator 5A (STAT5A) with fertilization and survival rates in an in vitro experimental system. The objectives of this study are to fine map the STAT5A region in a search for causative mutations and to investigate the parent of origin expression of this gene. We have performed a total of 5,222 fertilizations and produced a total of 3,696 in vitro fertilized embryos using gametes from 440 cows and eight bulls. A total of 37 SNPs were developed in a 63.4-kb region of genomic sequence that includes STAT5A, STAT3, and upstream and downstream sequences of these genes. SNP153137 (G/C) in exon 8 of STAT5A was associated with a significant variability in embryonic survival and fertilization rate compared to all other examined SNPs. Expression analysis revealed that STAT5A is primarily monoallelically expressed in early embryonic stages but biallelically expressed in later fetal stages. Furthermore, the occurrence of monoallelic maternal expression of STAT5A was significantly higher in blastocysts, while paternal expression was more frequent in degenerative embryos. Our results imply that STAT5A affects embryonic survival in a manner influenced by developmental stage and allele parent of origin.}, journal={BMC GENETICS}, author={Khatib, Hasan and Maltecca, Christian and Monson, Ricky L. and Schutzkus, Valerie and Rutledge, Jack J.}, year={2009}, month={Mar} }
 @article{maltecca_weigel_khatib_cowan_bagnato_2009, title={Whole-genome scan for quantitative trait loci associated with birth weight, gestation length and passive immune transfer in a Holstein x Jersey crossbred population}, volume={40}, ISSN={["1365-2052"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-58549114908&partnerID=MN8TOARS}, DOI={10.1111/j.1365-2052.2008.01793.x}, abstractNote={We herein report results from a daughter design genome-scan study aiming to identify quantitative trait loci (QTL) associated with birth weight, direct gestation length and passive immune transfer in a backcross (Holstein x Jersey) x Holstein population. Two-hundred and seventy-six calves, offspring of seven crossbred sires, were genotyped for 161 microsatellite markers distributed along the 29 bovine autosomes. The genome scan was performed through interval mapping using an animal model in order to identify QTL accounting for phenotypic differences between individual animals. Based on significant chi-squared values, we identified putative QTL on BTA7 and BTA14 for gestation length, on BTA2, BTA6 and BTA14 for birth weight and on BTA20 for passive immune transfer. In total, these QTL accounted for 12%, 18% and 1% of the phenotypic variance in gestation length, birth weight and passive immune transfer respectively. We also report results from a supplementary and independent influential grand-daughter Holstein family. In this family, findings on BTA7 and BTA14 for direct gestation length were in agreement with results in the crossbred population. Two other regions on BTA6 and BTA21 putatively underlying QTL for direct gestation length variability were discovered with this analysis.}, number={1}, journal={ANIMAL GENETICS}, author={Maltecca, C. and Weigel, K. A. and Khatib, H. and Cowan, M. and Bagnato, A.}, year={2009}, month={Feb}, pages={27–34} }
 @article{huang_maltecca_khatib_2008, title={A proline-to-histidine mutation in POU1F1 is associated with production traits in dairy cattle}, volume={39}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-52449123090&partnerID=MN8TOARS}, DOI={10.1111/j.1365-2052.2008.01749.x}, abstractNote={POU class 1 homeobox 1 (POU1F1) is a member of the tissue-specific POU-containing transcription factor family. The expression of POU1F1 in mammalian pituitary gland controls the transcription of the genes encoding growth hormone, prolactin (PRL) and the subunits of thyroid-stimulating hormone. In addition, some genes in the JAK/STAT signalling pathway downstream of POU1F1 have been shown to be associated with different production traits in dairy cattle. To investigate whether the POU1F1 gene is associated with economically important traits in dairy cattle, a pooled DNA sequencing approach was used to identify single nucleotide polymorphisms (SNPs) in the gene. An SNP in exon 3 of POU1F1 that changes a proline to a histidine was identified. A total of 2141 individuals from two North American Holstein cattle resource populations were genotyped for this SNP using a modified PCR-RFLP method. Statistical analyses revealed significant association of POU1F1 variants with milk yield and productive life, which makes POU1F1 a possible candidate for marker-assisted selection in dairy cattle breeding programmes.}, number={5}, journal={Animal Genetics}, author={Huang, W.X. and Maltecca, C. and Khatib, H.}, year={2008}, pages={554–557} }
 @article{wang_maltecca_tal-stein_lipkin_khatib_2008, title={Association of bovine fibroblast growth factor 2 (FGF2) gene with milk fat and productive life: An example of the ability of the candidate pathway strategy to identify quantitative trait genes}, volume={91}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-44949240403&partnerID=MN8TOARS}, DOI={10.3168/jds.2007-0877}, abstractNote={Fibroblast growth factor 2 (FGF2) is expressed in the bovine mammary gland and may play a role in the development and reorganization of the mammary gland. It is also expressed by the uterine endometrium throughout the estrous cycle and early pregnancy. The FGF2 was chosen for this study because it regulates the expression of interferon-tau, a key member of the signal transduction pathway involved in milk production. In previous studies, we reported the association of several genes in this pathway with milk production and health traits in dairy cattle. The objective of this study was to examine the association of FGF2 polymorphisms with milk composition, somatic cell score, and productive life in 3 Holstein cattle populations from the United States and Israel. The pooled DNA sequencing approach was used to identify single nucleotide polymorphisms (SNP) in FGF2. Sequencing of a total of 6.4 kb including 3 exons of the gene revealed only one SNP (A/G) in intron 1 at position 11646. This SNP was investigated for association with production traits in 2,773 individuals from 3 Holstein populations: the granddaughter-design Cooperative Dairy DNA Repository and the daughter-design University of Wisconsin populations from the United States and a daughter-design population from Israel. For both the Israeli and the UW populations, FGF2 variants were associated with fat yield and percentage, somatic cell score, and productive life with significant dominance and complete dominance effects. For the Cooperative Dairy DNA Repository population, no significant associations were observed for the examined traits. Given that FGF2 was chosen for this study because of its role in the interferon-tau signal transduction pathway and was found to be associated with production traits, results suggest that the candidate pathway could be an attractive strategy to search for candidate quantitative trait genes.}, number={6}, journal={Journal of Dairy Science}, author={Wang, X. and Maltecca, C. and Tal-Stein, R. and Lipkin, E. and Khatib, H.}, year={2008}, pages={2475–2480} }
 @article{khatib_maltecca_monson_schutzkus_rutledge_2008, title={Embryonic Mortality Is Associated with Monoallelic Expression of STAT5A.}, volume={78}, ISSN={0006-3363 1529-7268}, url={http://dx.doi.org/10.1093/biolreprod/78.s1.122b}, DOI={10.1093/biolreprod/78.s1.122b}, abstractNote={The signal transducer and activator (STAT) proteins are known to play an important role in cytokine signaling pathways as signal transducers in the cytoplasm and transcription activators in the nucleus. The STAT5A was chosen as a candidate gene affecting reproduction traits including embryonic survival and fertilization rate because it is a member of the signal transduction pathway of interferon-tau (IFNT), which has a key role in the initiation and maintenance of pregnancy in ruminants. We have performed a total of 5,222 fertilizations and produced a total of 3,696 in vitro fertilized embryos using 440 cows and eight sires. A total of 37 SNPs were developed in a region of 63.4 kb genomic sequence including STATA5A, STAT3, and upstream and downstream sequences of these genes. Among all examined SNPs, one highly conserved SNP in exon 8 of STAT5A showed the highest significant association with embryonic survival and fertilization rate. Expression analysis revealed that STAT5A is monoallelically expressed in early embryonic stages but biallelically-expressed in a wide range of fetal tissues. Furthermore, the occurrence of monoallelic maternal expression of STAT5A was significantly higher in degenerative embryos than in survived embryos. Paternal expression was more frequent in degenerative embryos. Analysis of STAT5A and its upstream and downstream sequences revealed high density of CpG islands and tandem repeats, which is in agreement with the sequence characteristics of monoallelically-expressed and imprinted genes. Our results imply that STAT5A affects embryonic mortality in a developmental-stage and in a parent-of-origin expression manner.}, number={Suppl_1}, journal={Biology of Reproduction}, publisher={Oxford University Press (OUP)}, author={Khatib, Hasan and Maltecca, Christian and Monson, Ricky L. and Schutzkus, Valerie and Rutledge, Jack}, year={2008}, month={May}, pages={122–122} }
 @article{bagnato_schiavini_rossoni_maltecca_dolezal_medugorac_sölkner_russo_fontanesi_friedmann_et al._2008, title={Quantitative trait loci affecting milk yield and protein percentage in a three-country brown swiss population}, volume={91}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-39049135439&partnerID=MN8TOARS}, DOI={10.3168/jds.2007-0507}, abstractNote={Quantitative trait loci (QTL) mapping projects have been implemented mainly in the Holstein dairy cattle breed for several traits. The aim of this study is to map QTL for milk yield (MY) and milk protein percent (PP) in the Brown Swiss cattle populations of Austria, Germany, and Italy, considered in this study as a single population. A selective DNA pooling approach using milk samples was applied to map QTL in 10 paternal half-sib daughter families with offspring spanning from 1,000 to 3,600 individuals per family. Three families were sampled in Germany, 3 in Italy, 1 in Austria and 3 jointly in Austria and Italy. The pools comprised the 200 highest and 200 lowest performing daughters, ranked by dam-corrected estimated breeding value for each sire-trait combination. For each tail, 2 independent pools, each of 100 randomly chosen daughters, were constructed. Sire marker allele frequencies were obtained by densitometry and shadow correction analyses of 172 genome-wide allocated autosomal markers. Particular emphasis was placed on Bos taurus chromosomes 3, 6, 14, and 20. Marker association for MY and PP with a 10% false discovery rate resulted in nominal P-values of 0.071 and 0.073 for MY and PP, respectively. Sire marker association tested at a 20% false discovery rate (within significant markers) yielded nominal P-values of 0.031 and 0.036 for MY and PP, respectively. There were a total of 36 significant markers for MY, 33 for PP, and 24 for both traits; 75 markers were not significant for any of the traits. Of the 43 QTL regions found in the present study, 10 affected PP only, 8 affected MY only, and 25 affected MY and PP. Remarkably, all 8 QTL regions that affected only MY in the Brown Swiss, also affected MY in research reported in 3 Web-based QTL maps used for comparison with the findings of this study (http://www.vetsci.usyd.edu.au/reprogen/QTL_Map/; http://www.animalgenome.org/QTLdb/cattle.html; http://bovineqtl.tamu.edu/). Similarly, all 10 QTL regions in the Brown Swiss that affected PP only, affected only PP in the databases. Thus, many QTL appear to be common to Brown Swiss and other breeds in the databases (mainly Holstein), and an appreciable fraction of QTL appears to affect MY or PP primarily or exclusively, with little or no effect on the other trait. Although QTL information available today in the Brown Swiss population can be utilized only in a within family marker-assisted selection approach, knowledge of QTL segregating in the whole population should boost gene identification and ultimately the implementation and efficiency of an individual genomic program.}, number={2}, journal={Journal of Dairy Science}, author={Bagnato, A. and Schiavini, F. and Rossoni, A. and Maltecca, C. and Dolezal, M. and Medugorac, I. and Sölkner, J. and Russo, V. and Fontanesi, L. and Friedmann, A. and et al.}, year={2008}, pages={767–783} }
 @article{khatib_maltecca_monson_schutzkus_wang_rutledge_2008, title={The fibroblast growth factor 2 gene is associated with embryonic mortality in cattle}, volume={86}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-52649153530&partnerID=MN8TOARS}, DOI={10.2527/jas.2007-0791}, abstractNote={The objective of this study was to investigate the association of the fibroblast growth factor 2 (FGF2) gene with embryonic survival and fertilization rate in cattle. This gene was chosen because of its role in regulating trophectoderm expression of interferon-tau, the maternal pregnancy recognition factor in ruminants. To evaluate the effect of FGF2 on fertility traits, we produced in vitro-fertilized embryos from 281 Holstein cows and from 7 sires. A total of 4,542 in vitro fertilizations were performed, from which a total of 3,171 embryos were produced. Survival and fertilization rates were assessed at d 7 of embryonic development. Using the pooled DNA sequencing approach, we identified 2 SNP in FGF2, SNP11646 and SNP23. All sires and cows were genotyped for these SNP. For fertilization rate, no significant differences between genotypes were found for either SNP, whereas the effect on survival rate was significant for SNP11646. The survival rate of embryos produced from GG cows for this SNP was 37%, compared with 28 and 29% for embryos produced from AG and AA cows, respectively. Although the molecular mechanisms that cause embryonic mortality have not yet been identified, this study provides the first evidence of association between FGF2 and embryonic mortality in cattle. Thus, we propose that FGF2 can be used in animal breeding strategies to test for improved reproductive performance.}, number={9}, journal={Journal of Animal Science}, author={Khatib, H. and Maltecca, C. and Monson, R.L. and Schutzkus, V. and Wang, X. and Rutledge, J.J.}, year={2008}, pages={2063–2067} }
 @article{gandini_maltecca_pizzi_bagnato_rizzi_2007, title={Comparing local and commercial breeds on functional traits and profitability: The case of reggiana dairy cattle}, volume={90}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-35748946307&partnerID=MN8TOARS}, DOI={10.3168/jds.2006-204}, abstractNote={The objective of this study was to compare fertility, longevity, milkability, and profitability of cows from the Reggiana and Holstein breeds in northern Italy. Profitability was gauged for each breed, with consideration of economic incentive programs and alternative milk pricing scenarios. Calving to first service interval, days open, and calving interval were significantly shorter in Reggiana than in Holstein cows. Reggiana cows conceived approximately one estrus cycle before Holstein and had a calving interval 33 d shorter. Holstein cows released a significantly higher quantity of milk per unit of time (1.81 vs. 1.28 kg/min). Reggiana cows had longer expected total and productive lives than Holstein cows, by 5.8 and 10.0 mo, respectively. Replacement rate was 26% higher in the Holstein. Standard 305-d milk production was 5,360 and 7,870 kg in Reggiana and Holstein, respectively. Comparing breeds on annual milk and meat production, instead of standard 305-d milk yield, changed marginally the difference in annual profitability between the Reggiana and Holstein, from -696 euros to -679 euros per cow per year. Including feeding, milking, replacement, and insemination costs reduced the gap between breeds by 32%, from -679 euros, measured on annual milk and meat production, to -460 euros. These differences in profitability assumed a pricing scenario referring to milk sold to the dairy industry where protein and fat contents are valued but not the breed origin of milk. Incentive payments to farmers of endangered cattle compensated partially (22%) the lower income from Reggiana cows. When Reggiana milk production was sold as branded Parmigiano Reggiano cheese, Reggiana cows were more profitable than Holstein cows by 1,953 euros per cow per year.}, number={4}, journal={Journal of Dairy Science}, author={Gandini, G. and Maltecca, C. and Pizzi, F. and Bagnato, A. and Rizzi, R.}, year={2007}, pages={2004–2011} }
 @article{maltecca_rossoni_nicoletti_santus_weigel_bagnato_2007, title={Estimation of genetic parameters for perinatal sucking behavior of Italian brown Swiss calves}, volume={90}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-35748967278&partnerID=MN8TOARS}, DOI={10.3168/jds.2007-0183}, abstractNote={Brown Swiss breeders sometimes experience difficulties in feeding calves because of the weak sucking ability of the calves in the early days of life. For the welfare of the calves, they should be suckled by their dams or should aggressively ingest colostrum immediately after birth. The composition of colostrum changes rapidly during the first few days of lactation, and the ability of calves to absorb the Ig decreases quickly as well. The aim of this study was to increase our knowledge of environmental and genetic components affecting the sucking response, to evaluate the possibility of selecting for this trait. Sucking ability was recorded in 3 categories (drank from the milk bucket nipple or bottle without help, drank with help, did not drink) at 5 post-natal meals (6, 12, 24, 48, and 72 h from birth). Records were analyzed with 2 different models: a single-trait threshold sire model, in which all observations were analyzed as a single trait with 5 levels, and a multiple-trait threshold liability sire model, in which meal-by-meal observations were analyzed as 5 different binary traits. Management procedures, the interval between birth and meals, parity, and season of birth were environmental factors affecting the variability in sucking ability. The heritability estimate for the single-trait analysis was 0.14, whereas heritabilities for the multiple-trait analysis were 0.26, 0.22, 0.21 0.12, and 0.13 for the first, second, third, fourth, and fifth meal, respectively. Estimated genetic correlations among traits were high (0.82 to 0.99). This study suggests the possibility of selection based on sucking ability. Future collection of larger data sets on the sucking response of calves in the first 2 meals after birth would increase the accuracy of genetic parameter estimates.}, number={10}, journal={Journal of Dairy Science}, author={Maltecca, C. and Rossoni, A. and Nicoletti, C. and Santus, E. and Weigel, K.A. and Bagnato, A.}, year={2007}, pages={4814–4820} }
 @article{khatib_zaitoun_chang_maltecca_boettcher_2007, title={Evaluation of association between polymorphism within the thyroglobulin gene and milk production traits in dairy cattle}, volume={124}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-33846919065&partnerID=MN8TOARS}, DOI={10.1111/j.1439-0388.2007.00634.x}, abstractNote={In dairy cattle, many studies have reported quantitative trait loci (QTL) on the centromeric end of chromosome 14 that affect milk production traits. One of the candidate genes in this QTL region - thyroglobulin (TG) - was previously found to be significantly associated with marbling in beef cattle. Thus, based on QTL studies in dairy cattle and because of possible effects of this gene on fat metabolism, we investigated the association of TG with milk yield and composition in Holstein dairy cattle. A total of 1279 bulls from the Cooperative Dairy DNA Repository Holstein population were genotyped for a single nucleotide polymorphism in TG used previously in beef cattle studies. Analysis of 29 sire families showed no significant association between TG variants and milk production traits. Within-sire family analysis suggests that TG is neither the responsible gene nor a genetic marker in association with milk production traits.}, number={1}, journal={Journal of Animal Breeding and Genetics}, author={Khatib, H. and Zaitoun, I. and Chang, Y.M. and Maltecca, C. and Boettcher, P.}, year={2007}, pages={26–28} }
 @article{weigel_halbach_maltecca_hoffman_2007, title={Performance and physical conformation of first parity backcross Holstein x Jersey cattle and their Holstein contemporaries}, volume={90}, journal={Journal of Dairy Science}, author={Weigel, K.A. and Halbach, T.J. and Maltecca, C. and Hoffman, P.C.}, year={2007}, pages={420–420} }
 @article{halbach_maltecca_hoffman_2007, title={Performance and physical conformation of first parity hackcross Holstein x Jersey cattle and their Holstein contemporaries}, volume={85}, journal={Journal of Animal Science}, author={Halbach, T.J. and Maltecca, C. and Hoffman, P.C.}, year={2007}, pages={420–420} }
 @article{maltecca_weigel_khatib_schutzkus_2007, title={Quantitative Trait Loci affecting IgG serum protein levels, birth weight and gestation length in a Holstein x (Holstein x Jersey) backcross population}, volume={90}, journal={Journal of Dairy Science}, author={Maltecca, C. and Weigel, K.A. and Khatib, H. and Schutzkus, V.R.}, year={2007}, pages={597–598} }
 @article{maltecca_weigel_khatib_schutzkus_hoffman_2006, title={349 Health, immune function, and survival of calves from Holstein dams and Holstein or crossbred Jersey x Holstein sires}, volume={84}, number={Supplement 1}, journal={Journal of Animal Science}, author={Maltecca, C. and Weigel, K. and Khatib, H. and Schutzkus, V. and Hoffman, P.}, year={2006}, pages={276} }
 @article{maltecca_khatib_schutzkus_hoffman_weigel_2006, title={Changes in conception rate, calving performance, and calf health and survival from the use of crossbred Jersey x Holstein sires as mates for Holstein dams}, volume={89}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-33745905948&partnerID=MN8TOARS}, DOI={10.3168/jds.S0022-0302(06)72351-7}, abstractNote={Differences in conception rates in matings of Holstein sires or F1 Jersey x Holstein sires to Holstein dams in the University of Wisconsin-Madison experimental herd were evaluated, as were differences in birth weight, dystocia, serum protein, serum IgG, fecal consistency, respiratory disease, and perinatal and pre-weaning mortality among the resulting calves. When mated to randomly chosen, lactating Holstein cows, Holstein sires (n = 74) and crossbred sires (n = 7) did not differ in male fertility. Calves from Holstein sires and multiparous Holstein dams (n = 99) were 1.9 kg heavier than calves from crossbred sires and multiparous Holstein dams (n = 211), leading to greater likelihood (odds ratio of 1.24) of dystocia. Furthermore, calves from crossbred sires and multiparous Holstein dams had higher serum protein and serum IgG levels between 24 and 72 h of age, as well as lower rates of perinatal and preweaning morality than calves from Holstein sires and multiparous or primiparous Holstein dams. Mean fecal consistency scores from birth to 7 d of age and number of days with scours also tended to be lower among calves from crossbred sires, compared with calves from Holstein sires. No differences were observed in the incidence or severity of respiratory disease. Results of this study suggest that introduction of Jersey genes via crossbreeding may lead to a reduction in dystocia and improvements in calf health and survival in Holstein herds. Future studies should address other traits related to dairy farm profitability, including milk composition, female fertility, longevity, feed efficiency, and resistance to infectious and metabolic diseases.}, number={7}, journal={Journal of Dairy Science}, author={Maltecca, C. and Khatib, H. and Schutzkus, V.R. and Hoffman, P.C. and Weigel, K.A.}, year={2006}, pages={2747–2754} }
 @inproceedings{weigel_maltecca_khatib_schutzkus_hoffman_2006, title={Health, immune function and survival of Holstein and crossbred Jersey × Holstein dairy calves}, booktitle={Proceedings of the 8th World Congress on Genetics Applied to Livestock Production}, author={Weigel, K.A. and Maltecca, C. and Khatib, H. and Schutzkus, V.R. and Hoffman, P.C.}, year={2006}, pages={01–03} }
 @article{maltecca_khatib_schutzkus_weigel_2006, title={Mapping quantitative trait loci affecting calves immune function and birth weight in a Holstein x (Holstein x Jersey) backcross population}, volume={84}, number={Supplement 1}, journal={Journal of Animal Science}, author={Maltecca, C. and Khatib, H. and Schutzkus, V.R. and Weigel, K.A.}, year={2006}, month={Aug}, pages={274} }
 @article{leonard_khatib_schutzkus_chang_maltecca_2005, title={Effects of the osteopontin gene variants on milk production traits in dairy cattle}, volume={88}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-27644525204&partnerID=MN8TOARS}, DOI={10.3168/jds.S0022-0302(05)73092-7}, abstractNote={Osteopontin (OPN) is a highly phosphorylated glycoprotein whose gene has been cloned and sequenced in different species. Several whole genome scans have identified quantitative trait loci (QTL) affecting milk production traits on bovine chromosome 6 close to the osteopontin gene (OPN) location. The presence of OPN in milk and its elevated expression in mammary gland epithelial cells together with previous QTL studies have prompted us to investigate the effects of OPN variants on milk production traits in the Holstein dairy cattle population. A single nucleotide polymorphism in intron 4 (C/T) was detected and primers were designed to amplify genomic DNA from 1362 bulls obtained from Cooperative Dairy DNA Repository and from 214 cows from the University of Wisconsin herd. For the Repository population, the C allele was associated with an increase in milk protein percentage and milk fat percentage. Correlation between milk protein percentage and milk fat percentage was about 0.57. For the University of Wisconsin herd, the estimates of the effects of allele C were in the same direction as for the Repository population, although these estimates did not reach statistical significance. Our results are consistent with other studies that showed a significant association of the microsatellite markers in the region of OPN with milk protein percentage and other correlated traits.}, number={11}, journal={Journal of Dairy Science}, author={Leonard, S. and Khatib, H. and Schutzkus, V. and Chang, Y.M. and Maltecca, C.}, year={2005}, pages={4083–4086} }
 @inproceedings{bagnato_romani_fontana_dubini_schiavini_rossoni_maltecca_vicario_2005, title={Identification of QTL for productive traits and milk somatic cell count in the Italian Simmental cattle}, booktitle={Proceedings of the  Associazione per la Scienza e le Produzioni Animali (ASPA) Congress}, author={Bagnato, A. and Romani, C. and Fontana, S. and Dubini, S. and Schiavini, F. and Rossoni, A. and Maltecca, C. and Vicario, D.}, year={2005} }
 @article{bagnato_schiavini_dolezal_dubini_rossoni_maltecca_santus_medugorac_sölkner_fontanesi_et al._2005, title={The BovMAS consortium: Identification of QTL for milk yield and milk protein percent on chromosome 14 in the Brown Swiss breed}, volume={4}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-33645795921&partnerID=MN8TOARS}, DOI={10.4081/ijas.2005.2s.13}, abstractNote={RiassuntoIdentificazione di QTL per quantità di latte e tenore proteico sul cromosoma 14 nella razza Bruna. Numerosi progetti di mappaggio di QTL hanno permesso di identificare diverse regioni cromosomiche associate a caratteri di interesse produttivo. In questo lavoro sono state identificate 2 possibili regioni QTL sul cromosoma 14 (0-20 cM e 70-110) nelle quali sono stati trovati marcatori in associazione con latte (kg) e proteina (%). Nella regione centromerica è stato inoltre studiato il genotipo per i geni DGAT1 e TG, il primo dei quali è stato recentemente proposto come causativo di un maggior contenuto lipidico nel latte.}, journal={Italian Journal of Animal Science}, author={Bagnato, A. and Schiavini, F. and Dolezal, M. and Dubini, S. and Rossoni, A. and Maltecca, C. and Santus, E. and Medugorac, I. and Sölkner, J. and Fontanesi, L. and et al.}, year={2005}, pages={13–15} }
 @article{maltecca_bagnato_weigel_2004, title={Comparison of International Dairy Sire Evaluations from Meta-Analysis of National Estimated Breeding Values and Direct Analysis of Individual Animal Performance Records}, volume={87}, ISSN={0022-0302}, url={http://dx.doi.org/10.3168/jds.s0022-0302(04)73385-8}, DOI={10.3168/jds.S0022-0302(04)73385-8}, abstractNote={Our objective was to assess the predictive ability of different methodologies for international genetic evaluation of milk yield and to determine the magnitude of differences in the resulting sire estimated breeding values (EBV). Data included first lactation records of 16,057,335 Holstein-sired cows from 237,049 herds in 14 countries. Meta-analysis of national sire EBV using the multiple-trait across country evaluation (MACE) procedure, single-trait analysis of individual animal performance records, multiple-trait analysis of individual animal performance records, and borderless herd cluster model were compared by assessing predictive ability. Comparisons were based on root mean square error of sire EBV from a subset of records from cows calving between 1990 and 1995 and corresponding pedigree indices for sires that received their first genetic evaluations in 1996 or 1997. The number of bulls first evaluated in 1996 or 1997 that were in common between the top 25, 100, and 250 for pedigree index and the top 25, 100, and 250 for EBV were also determined for each method. Average root mean square error of prediction was 10.3 kg2 for the borderless single-trait model, 6.6 kg2 for the borderless herd cluster model, and 6.7 kg2 for both the borderless multiple-trait model and meta-analysis of national sire EBV using MACE. The mean numbers of common bulls among the top 25, 100, and 250, respectively, when selected on pedigree index and subsequent EBV were 11, 48, and 154 for the borderless single-trait model; 16, 66, and 176 for the borderless multiple-trait model; 16, 66, and 178 for the borderless herd cluster model; and 15, 66, and 178 for meta-analysis of national sire EBV using MACE. Rank correlations between sire EBV from different models ranged from 0.77 for the single-trait borderless model and the meta-analysis using MACE to 0.92 for the borderless multiple-trait and the borderless herd cluster models.}, number={8}, journal={Journal of Dairy Science}, publisher={American Dairy Science Association}, author={Maltecca, C. and Bagnato, A. and Weigel, K.A.}, year={2004}, month={Aug}, pages={2599–2605} }
 @article{weigel_maltecca_2004, title={Comparison of the fertility of pure Holstein sires and F1 Jersey x Holstein sires mated to pure Holstein cows in an experimental herd}, volume={87}, journal={Journal of Dairy Science}, author={Weigel, K. and Maltecca, C.}, year={2004}, pages={282–282} }
 @article{maltecca_weigel_2004, title={Health parameters in F1 Jersey x Holstein, backcross (Jersey x Holstein) x Holstein, and pure Holstein calves}, volume={87}, journal={Journal of Dairy Science}, author={Maltecca, C. and Weigel, K.}, year={2004}, pages={87–87} }
 @inproceedings{bagnato_schiavini_dubini_rossoni_maltecca_santus_medjugorac_sölkner;_lipkin_soller_2004, title={The BovMAS Consortium: a complete genome scan of Brown Swiss cattle for milk yield and protein percent using selective DNA pooling with milk samples}, booktitle={29th International Conference on Animal Genetics}, author={Bagnato, A. and Schiavini, F. and Dubini, S. and Rossoni, A. and Maltecca, C. and Santus, E. and Medjugorac, I. and Sölkner;, J. and Lipkin, E. and Soller, M.}, year={2004} }
 @article{pizzi_rizzi_maltecca_bagnato_gandini_2003, title={Fertility and longevity in the Reggiana cattle breed}, volume={2}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-55449106382&partnerID=MN8TOARS}, number={SUPPL. 1}, journal={Italian Journal of Animal Science}, author={Pizzi, F. and Rizzi, R. and Maltecca, C. and Bagnato, A. and Gandini, G.}, year={2003}, pages={151–153} }
 @article{bagnato_rossoni_maltecca_vigo_ghiroldi_2003, title={Milk emission curves in different parities in Italian Brown Swiss cattle}, volume={2}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-55449090958&partnerID=MN8TOARS}, number={SUPPL. 1}, journal={Italian Journal of Animal Science}, author={Bagnato, A. and Rossoni, A. and Maltecca, C. and Vigo, D. and Ghiroldi, S.}, year={2003}, pages={46–48} }
 @inproceedings{bagnato_gandini_maltecca_orlandini_pizzi_2001, title={Comparison of milking speed in Reggiana and Italian Holstein cattle [Emilia-Romagna}, volume={2}, booktitle={Proceedings of the ASPA Congress - Recent Progress in Animal Production Science (Italy)}, author={Bagnato, A. and Gandini, G.C. and Maltecca, C. and Orlandini, P. and Pizzi, F.}, year={2001}, pages={329–221} }
 @inproceedings{gandini_maltecca_heinzl_pizzi_2001, title={Creation of the semen bank of Italian pig genetic resources}, booktitle={Proceedings of the  Associazione per la Scienza e le Produzioni Animali (ASPA) Congress}, author={Gandini, G. and Maltecca, C. and Heinzl, E. and Pizzi, F.}, year={2001} }
 @article{gandini_pizzi_maltecca_heinzl_pagnacco_2001, title={Gene banks for pig genetic resources: optimisation criteria}, volume={27}, number={6}, journal={Zootecnica E Nutrizione Animale}, author={Gandini, G. and Pizzi, F. and Maltecca, C. and Heinzl, E. and Pagnacco, G.}, year={2001}, pages={285–294} }