@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 (IAm) and by individual (IAi) in US crossbred dairy cattle. Holstein × Jersey crossbreds were used to evaluate imputation accuracy from a low (7K) to medium density (50K) SNP chip. Crossbred animals, as well as their sires (53), dams (77), and maternal grandsires (63), were all genotyped with a 78K SNP chip. Seven different scenarios of reference populations were tested in which some scenarios used different family relationships and some other added random unrelated purebred and crossbred individuals to those different family relationship scenarios. The same scenarios were tested on Holstein and Jersey purebred animals to compare these outcomes to those attained in crossbred animals. The genotype imputation was performed with findhap (version 4) software. There were no significant differences in IA results depending on whether the sire of imputed individuals was Holstein and the dam was Jersey, or vice versa. The IA increased significantly with the addition of related individuals in the reference population, from 86.70 ± 0.06% when only sires or dams were included in the reference population to 90.09 ± 0.06% when sires, dams, and maternal grandsires genomic information were combined in the reference population. In all scenarios including related individuals in the reference population, IAm and IAi were significantly superior in purebred Jersey and Holstein animals than in crossbreds, ranging from 90.75 ± 0.06 to 94.02 ± 0.06%, and from 90.88 ± 0.11 to 94.04 ± 0.10%, respectively. Additionally, a scenario called SPB+DLD that is similar to the genomic evaluations performed on US crossbred dairy was tested. In this scenario, the information from the 5 evaluated breeds (Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey) genotyped with a 50K SNP chip and genomic information from the dams genotyped with a 7K SNP chip were combined in the reference population and the IAm and IAi were 80.87 ± 0.06%, and 80.85 ± 0.08%, respectively. Adding randomly nonrelated genotyped individuals in the reference population reduced IA for both purebred and crossbred cows, except for scenario SPB+DLD, where adding crossbreds to the reference population increased IA values. Our findings demonstrate that IA for US Holstein × Jersey crossbred ranged from 85 to 90%, and emphasize the significance of designing and defining the reference population for improved 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{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}, 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}, 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} }
 @article{sartori_tiezzi_guzzo_mancin_tuliozi_mantovani_2022, title={Genotype by Environment Interaction and Selection Response for Milk Yield Traits and Conformation in a Local Cattle Breed Using a Reaction Norm Approach}, volume={12}, url={https://doi.org/10.3390/ani12070839}, DOI={10.3390/ani12070839}, abstractNote={Local breeds are often reared in various environmental conditions (EC), suggesting that genotype by environment interaction (GxE) could influence genetic progress. This study aimed at investigating GxE and response to selection (R) in Rendena cattle under diverse EC. Traits included milk, fat, and protein yields, fat and protein percentage, and somatic cell score, three-factor scores and 24 linear type traits. The traits belonged to 11,085 cows (615 sires). Variance components were estimated in a two-step reaction norm model (RNM). A single trait animal model was run to obtain the solutions of herd-EC effect, then included in a random regression sire model. A multivariate response to selection (R) in different EC was computed for traits under selection including beef traits from a performance test. GxE accounted on average for 10% of phenotypic variance, and an average rank correlation of over 0.97 was found between bull estimated breeding values (EBVs) by either including or not including GxE, with changing top ranks. For various traits, significantly greater genetic components and R were observed in plain farms, loose housing rearing system, feeding total mixed ration, and without summer pasture. Conversely, for beef traits, a greater R was found for mountain farms, loose housing, hay-based feeding and summer pasture.}, number={7}, journal={Animals}, publisher={MDPI AG}, author={Sartori, Cristina and Tiezzi, Francesco and Guzzo, Nadia and Mancin, Enrico and Tuliozi, Beniamino and Mantovani, Roberto}, year={2022}, month={Mar}, pages={839} }
 @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={Abstract}, 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} }
 @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}, 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{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_fleming_malchiodi_2022, title={Use of Milk Infrared Spectral Data as Environmental Covariates in Genomic Prediction Models for Production Traits in Canadian Holstein}, volume={12}, ISSN={["2076-2615"]}, url={https://doi.org/10.3390/ani12091189}, DOI={10.3390/ani12091189}, abstractNote={The purpose of this study was to provide a procedure for the inclusion of milk spectral information into genomic prediction models. Spectral data were considered a set of covariates, in addition to genomic covariates. Milk yield and somatic cell score were used as traits to investigate. A cross-validation was employed, making a distinction for predicting new individuals’ performance under known environments, known individuals’ performance under new environments, and new individuals’ performance under new environments. We found an advantage of including spectral data as environmental covariates when the genomic predictions had to be extrapolated to new environments. This was valid for both observed and, even more, unobserved families (genotypes). Overall, prediction accuracy was larger for milk yield than somatic cell score. Fourier-transformed infrared spectral data can be used as a source of information for the calculation of the ‘environmental coordinates’ of a given farm in a given time, extrapolating predictions to new environments. This procedure could serve as an example of integration of genomic and phenomic data. This could help using spectral data for traits that present poor predictability at the phenotypic level, such as disease incidence and behavior traits. The strength of the model is the ability to couple genomic with high-throughput phenomic information.}, number={9}, journal={ANIMALS}, publisher={MDPI AG}, author={Tiezzi, Francesco and Fleming, Allison and Malchiodi, Francesca}, year={2022}, month={May} }
 @article{romero_park_joo_zhao_killerby_reyes_tiezzi_gutierrez-rodriguez_castillo_2021, title={A combination of Lactobacillus buchneri and Pediococcus pentosaceus extended the aerobic stability of conventional and brown midrib mutants-corn hybrids ensiled at low dry matter concentrations by causing a major shift in their bacterial and fungal community}, volume={99}, ISSN={["1525-3163"]}, url={https://doi.org/10.1093/jas/skab141}, DOI={10.1093/jas/skab141}, abstractNote={Abstract}, number={8}, journal={JOURNAL OF ANIMAL SCIENCE}, publisher={Oxford University Press (OUP)}, author={Romero, Juan J. and Park, Jin and Joo, Younghoo and Zhao, Yuchen and Killerby, Marjorie and Reyes, Diana C. and Tiezzi, Francesco and Gutierrez-Rodriguez, Eduardo and Castillo, Miguel S.}, year={2021}, month={Aug} }
 @article{freitas_johnson_chen_oliveira_tiezzi_lazaro_huang_gu_schinckel_brito_2021, title={Definition of Environmental Variables and Critical Periods to Evaluate Heat Tolerance in Large White Pigs Based on Single-Step Genomic Reaction Norms}, volume={12}, ISSN={["1664-8021"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85120846720&partnerID=MN8TOARS}, DOI={10.3389/fgene.2021.717409}, abstractNote={Properly quantifying environmental heat stress (HS) is still a major challenge in livestock breeding programs, especially as adverse climatic events become more common. The definition of critical periods and climatic variables to be used as the environmental gradient is a key step for genetically evaluating heat tolerance (HTol). Therefore, the main objectives of this study were to define the best critical periods and environmental variables (ENV) to evaluate HT and estimate variance components for HT in Large White pigs. The traits included in this study were ultrasound backfat thickness (BFT), ultrasound muscle depth (MDP), piglet weaning weight (WW), off-test weight (OTW), interval between farrowing (IBF), total number of piglets born (TNB), number of piglets born alive (NBA), number of piglets born dead (NBD), number of piglets weaned (WN), and weaning to estrus interval (IWE). Seven climatic variables based on public weather station data were compared based on three criteria, including the following: (1) strongest G×E estimate as measured by the slope term, (2) ENV yielding the highest theoretical accuracy of the genomic estimated breeding values (GEBV), and (3) variable yielding the highest distribution of GEBV per ENV. Relative humidity (for BFT, MDP, NBD, WN, and WW) and maximum temperature (for OTW, TNB, NBA, IBF, and IWE) are the recommended ENV based on the analyzed criteria. The acute HS (average of 30 days before the measurement date) is the critical period recommended for OTW, BFT, and MDP in the studied population. For WN, WW, IBF, and IWE, a period ranging from 34 days prior to farrowing up to weaning is recommended. For TNB, NBA, and NBD, the critical period from 20 days prior to breeding up to 30 days into gestation is recommended. The genetic correlation values indicate that the traits were largely (WN, WW, IBF, and IWE), moderately (OTW, TNB, and NBA), or weakly (MDP, BFT, and NBD) affected by G×E interactions. This study provides relevant recommendations of critical periods and climatic gradients for several traits in order to evaluate HS in Large White pigs. These observations demonstrate that HT in Large White pigs is heritable, and genetic progress can be achieved through genetic and genomic selection.}, journal={FRONTIERS IN GENETICS}, author={Freitas, P. H. F. and Johnson, J. S. and Chen, S. and Oliveira, H. R. and Tiezzi, F. and Lazaro, S. F. and Huang, Y. and Gu, Y. and Schinckel, A. P. and Brito, L. F.}, year={2021}, month={Nov} }
 @article{lozada‐soto_maltecca_wackel_flowers_gray_he_huang_jiang_tiezzi_2021, title={Evidence for recombination variability in purebred swine populations}, volume={138}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85091428844&partnerID=MN8TOARS}, DOI={10.1111/jbg.12510}, abstractNote={Abstract}, number={2}, journal={Journal of Animal Breeding and Genetics}, 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={2021}, pages={259–273} }
 @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={https://doi.org/10.1016/j.csbj.2020.12.038}, 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{khanal_maltecca_schwab_fix_tiezzi_2021, title={Microbiability of meat quality and carcass composition traits in swine}, volume={138}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85091404054&partnerID=MN8TOARS}, DOI={10.1111/jbg.12504}, abstractNote={Abstract}, 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}, 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={http://dx.doi.org/10.1186/s42523-021-00118-z}, DOI={10.1186/s42523-021-00118-z}, abstractNote={Abstract}, 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} }
 @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={https://doi.org/10.3390/ani11061833}, 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{oliveira junior_schaeffer_schenkel_tiezzi_baes_2021, title={Potential effects of hormonal synchronized breeding on genetic evaluations of fertility traits in dairy cattle: A simulation study}, volume={104}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85101065741&partnerID=MN8TOARS}, DOI={10.3168/jds.2020-18944}, abstractNote={About 30% of producers use hormone protocols to synchronize ovulation and perform timed artificial insemination (AI) in Canada. Days from calving to first service (CTFS) and first service to conception (FSTC) become masked phenotypes leading to biased genetic evaluations of cows for these fertility traits. The objectives of this study were to (1) demonstrate and quantify the potential amount of bias in genetic evaluations, and (2) find a procedure that could remove the bias. Simulation was used for both objectives. The proposed solution was to identify cows that have been treated by hormone protocols, make their CTFS and FSTC missing, and perform a multiple trait analysis including traits that have high genetic correlations with CTFS and FSTC, and which are not affected by the hormone protocols themselves. A total of 12 scenarios (S1-S12) were tested, changing the percentage of herds and cows that were randomly selected to be under timed AI. Cows that were given hormone protocols had CTFS of 86 d and FSTC of 0, which were used in genetic evaluation. Four criteria were used to indirectly measure the presence of bias: (1) the correlation between true (TBV) and estimated (EBV) breeding values (accuracy); (2) the differences in the mean EBV of top 25, 50, and 75 sires; (3) changes in correlation between TBV and EBV rankings; and (4) the changes in mean EBV over the simulated generations. All criteria changed unfavorably and proportionally to the increased use of timed AI. The accuracy within each class of animals (cows, dams, or sires) decreased proportionally with increased use of timed AI, varying from 0.32 (S12) to 0.52 (S1) for bull EBV for CTFS. The average EBV of the top sires (best 25, 50, 75, or 100 sires) approached population average EBV values when increasing the number of treated animals. The sire rank correlation between EBV and TBV within simulated scenarios was smaller for scenarios with more synchronized animals, going from 0.38 (S12) to 0.67 (S1). The long-term use of hormonal synchronized cows clearly decreased the mean EBV over generations in the population for CTFS and FSTC. The inclusion of genetically correlated traits in a multiple trait model was effective in removing the bias due to the presence of hormonal synchronized cows. However, given the constraints within the simulation, it is important that further investigation with real data is conducted to determine the true effect of including timed AI records within genetic evaluations of fertility traits in dairy cattle.}, number={4}, journal={JOURNAL OF DAIRY SCIENCE}, author={Oliveira Junior, G. A. and Schaeffer, L. R. and Schenkel, F. and Tiezzi, F. and Baes, C. F.}, year={2021}, month={Apr}, pages={4404–4412} }
 @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}, 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} }
 @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}, 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}, 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}, 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={http://www.scopus.com/inward/record.url?eid=2-s2.0-85075433094&partnerID=MN8TOARS}, 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}, pages={572–582} }
 @article{biffani_tiezzi_fresi_stella_minozzi_2020, title={Genetic parameters of weeping teats in Italian Saanen and Alpine dairy goats and their relationship with milk production and somatic cell score}, volume={103}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85088386959&partnerID=MN8TOARS}, DOI={10.3168/jds.2020-18175}, abstractNote={This paper reports a quantitative genetics analysis of weeping teats (WT), an abnormality of the mammary gland in goats. Weeping teats are characterized by milk oozing out of the teat or by the presence of multiple cysts near its base. This abnormality has been routinely recorded in Italian Alpine and Saanen goats since 2000 using a score of 0 or 1 (0 = defect not present, 1 = defect present). No information is available on the genetic background of WT or its relationship with production or other udder-related traits. Data obtained by the Italian National Sheep and Goat Breeders Association (Rome, Italy) were used to estimate both heritability of WT and its genetic correlation with milk yield, somatic cell score, and udder traits. The final data set used in the analysis included 2,178 Saanen and 2,309 Alpine primiparous goats kidding from 2009 to 2014. The pedigree data included 7,333 Saanen and 7,421 Alpines, respectively. A threshold-linear multivariate animal model was used to estimate variance and covariance components. A genealogical data analysis was also implemented, including genealogical data completeness, inbreeding, and identification of possible most recent common ancestors. On average, around 4 and 13% of primiparous Saanen and Alpine females kidding from 2009 to 2014 showed mammary gland abnormality, respectively. Weeping teats heritability was 0.27 and 0.26 for Saanen and Alpine, respectively. Genetic correlations between milk production or somatic cell score ranged from -0.16 in Saanen to 0.43 in Alpine, but the standard error of the estimates was very large. Positive genetic correlations were observed among WT and teat characteristics in both Saanen and Alpine. The average inbreeding of abnormality carriers was 2.4 and 5.1 for Saanen and Alpine, respectively. The genealogical data analysis identified 4 common ancestors of affected does in Saanen and 2 in Alpine. These results indicate that WT have a possible genetic background. A genome-wide association study might help in understanding the polygenic or monogenic determination of this abnormality.}, number={10}, journal={JOURNAL OF DAIRY SCIENCE}, author={Biffani, Stefano and Tiezzi, Francesco and Fresi, Pancrazio and Stella, Alessandra and Minozzi, Giulietta}, year={2020}, month={Oct}, pages={9167–9176} }
 @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}, 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} }
 @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={https://doi.org/10.1186/s40168-020-00888-9}, DOI={10.1186/s40168-020-00888-9}, abstractNote={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. 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. 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}, month={Dec} }
 @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}, 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{tiezzi_maisano_chessa_luini_biffani_2020, title={Heritability of Teat Condition in Italian Holstein Friesian and Its Relationship with Milk Production and Somatic Cell Score}, volume={10}, url={https://doi.org/10.3390/ani10122271}, DOI={10.3390/ani10122271}, abstractNote={In spite of the impressive advancements observed on both management and genetic factors, udder health still represents one of most demanding objectives to be attained in the dairy cattle industry. Udder morphology and especially teat condition might represent the first physical barrier to pathogens’ access. The objectives of this study were to investigate the genetic component of teat condition and to elucidate its relationship with both milk yield and somatic cell scores in dairy cattle. Moreover, the effect of selection for both milk yield and somatic cell scores on teat condition was also investigated. A multivariate analysis was conducted on 10,776 teat score records and 30,160 production records from 2469 Italian Holstein cows. Three teat scoring traits were defined and included in the analysis. Heritability estimates for the teat score traits were moderate to low, ranging from 0.084 to 0.238. When teat score was based on a four-classes ordinal scoring, its genetic correlation with milk yields and somatic cell score were 0.862 and 0.439, respectively. The scale used to classify teat-end score has an impact on the magnitude of the estimates. Genetic correlations suggest that selection for milk yield could deteriorate teat health, unless more emphasis is given to somatic cell scores. Considering that both at national and international level, the current selection objectives are giving more emphasis to health traits, a further genetic deterioration in teat condition is not expected.}, number={12}, journal={Animals}, publisher={MDPI AG}, author={Tiezzi, Francesco and Maisano, Antonio Marco and Chessa, Stefania and Luini, Mario and Biffani, Stefano}, year={2020}, month={Dec}, pages={2271} }
 @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 (ra) 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{bergamaschi_tiezzi_howard_huang_gray_schillebeeckx_mcnulty_maltecca_2020, title={Microbiome composition differences among breeds impact feed efficiency in swine}, volume={2}, url={https://doi.org/10.21203/rs.2.22531/v1}, DOI={10.21203/rs.2.22531/v1}, abstractNote={Abstract}, publisher={Research Square}, 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}, month={Feb} }
 @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}, 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={http://www.scopus.com/inward/record.url?eid=2-s2.0-85083707526&partnerID=MN8TOARS}, 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}, pages={5302–5313} }
 @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}, 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}, 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{castillo_tiezzi_franzluebbers_2020, title={Tree species effects on understory forage productivity and microclimate in a silvopasture of the Southeastern USA}, volume={295}, ISSN={["1873-2305"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85081013835&partnerID=MN8TOARS}, DOI={10.1016/j.agee.2020.106917}, abstractNote={Ecosystem services provided by silvopastoral systems are mediated by specific management practices, environmental conditions, and overall design of the system. We hypothesized that selection of tree species affects understory forage nutritive value and productivity, light/shade environment, and microclimate. The silvopastoral system was located at the Center for Environmental Farming Systems in Goldsboro, North Carolina, USA. Three overstory tree-species were Pinus palustris (PP; longleaf pine), Pinus taeda (PT; lobloblly pine), and Quercus pagoda (QP; cherrybark oak). The understory forage component consisted of a four-way mixture of native warm-season grasses [big bluestem (Andropogon gerardii, ‘Eastern’, KY origin), eastern gamagrass (Tripsacum dactyloides, MO origin), indiangrass (Sorghastrum nutans, ‘NC ecotype’), and switchgrass (Panicum virgatum, ‘Alamo’)]. The experimental design was an RCBD with 3 replicates. There was no effect of seedbed preparation (till versus no-till) on forage establishment. Understory dry matter yield, crude protein and total digestible nutrient concentrations of the harvested forage were not affected by tree species, with the exception at the 3.5 south sampling point. Overstory effects on microclimate variables were not different among tree-species, but were more noticeable during the daytime of the summer months, and were at the most 1-degree point for temperature and temperature-humidity index and 3 points for relative humidity. The silvopasture design in our study provided year-round shade by the tree-component, with varying levels of shade (ranging from 90 to 6% of incident photosynthetic active radiation) due to geographic location, tree species, and season. Our results describe and highlight the potential of trees in a silvopasture design in the southeastern USA to mitigate changes in temperature, humidity, the temperature-humidity index, and forage productivity and as a function of tree species and at different distance from the trees.}, journal={AGRICULTURE ECOSYSTEMS & ENVIRONMENT}, author={Castillo, Miguel S. and Tiezzi, Francesco and Franzluebbers, Alan J.}, year={2020}, month={Jun} }
 @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}, number={Supplement_3}, journal={Journal of Animal Science}, publisher={Oxford University Press US}, author={Tiezzi, Francesco and Schwab, Clint and Fix, Justin and Maltecca, Christian}, year={2019}, 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}, number={Supplement_3}, journal={Journal of Animal Science}, publisher={Oxford University Press US}, author={Bergamaschi, Matteo and Maltecca, Christian and Schwab, Clint and Fix, Justin and Tiezzi, Francesco}, year={2019}, 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}, number={Supplement_3}, journal={Journal of Animal Science}, publisher={Oxford University Press US}, author={Khanal, Piush and Maltecca, Christian and Schwab, Clint and Fix, Justin and Tiezzi, Francesco}, year={2019}, pages={44–44} }
 @article{he_maltecca_tiezzi_flowers_2019, title={381 Investigation of heat stress on differential gene expression in tolerant and susceptible pigs}, volume={97}, number={Supplement_3}, journal={Journal of Animal Science}, publisher={Oxford University Press US}, author={He, Yuqing and Maltecca, Christian and Tiezzi, Francesco and Flowers, Billy}, year={2019}, pages={144–144} }
 @article{lozada-soto_maltecca_anderson_tiezzi_2019, title={Analysis of milk leukocyte differential measures for use in management practices for decreased mastitis incidence}, journal={Journal of dairy science}, publisher={Elsevier}, author={Lozada-Soto, E and Maltecca, C and Anderson, K and Tiezzi, F}, 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}, 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{bergamaschi_maltecca_fix_schwab_tiezzi_2019, title={Genome-wide association study for carcass quality traits and growth in purebred and crossbred pigs}, journal={Journal of animal science}, author={Bergamaschi, Matteo and Maltecca, Christian and Fix, Justin and Schwab, Clint and Tiezzi, Francesco}, year={2019} }
 @inproceedings{tiezzi_2019, title={Incorporating Different Environmental and Phenotypic Information into Genomic Predictions for Dairy Cattle}, booktitle={Plant and Animal Genome XXVII Conference (January 12-16, 2019)}, author={Tiezzi, Francesco}, 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}, journal={bioRxiv}, author={Khanal, P. and Maltecca, C. and Schwab, C. and Fix, J. and Tiezzi, F.}, year={2019} }
 @article{khanal_maltecca_schwab_fix_tiezzi_2019, title={Microbiability of meat quality and carcass composition traits in swine}, journal={bioRxiv}, publisher={Cold Spring Harbor Laboratory}, author={Khanal, Piush and Maltecca, Christian and Schwab, Clint and Fix, Justin and Tiezzi, Francesco}, year={2019}, pages={833731} }
 @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}, 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} }
 @article{maltecca_bergamaschi_tiezzi_2019, title={The interaction between microbiome and pig efficiency: A review}, journal={Journal of Animal Breeding and Genetics}, publisher={Wiley Online Library}, author={Maltecca, Christian and Bergamaschi, Matteo and Tiezzi, Francesco}, 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}, number={suppl_2}, journal={Journal of Animal Science}, publisher={Oxford University Press US}, author={Wackel, H and Tiezzi, F and Gray, KA and Flowers, WL and Huang, Y and Maltecca, C}, year={2018}, 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}, number={suppl_3}, journal={Journal of Animal Science}, publisher={Oxford University Press US}, author={Lu, D and Tiezzi, F and Maltecca, C}, year={2018}, 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}, number={suppl_3}, journal={Journal of Animal Science}, publisher={Oxford University Press US}, author={Khanal, P and Maltecca, C and Schwab, C and Gray, K and Tiezzi, F}, year={2018}, pages={116–116} }
 @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}, 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{romero_joo_park_tiezzi_gutierrez-rodriguez_castillo_2018, title={Bacterial and fungal communities, fermentation, and aerobic stability of conventional hybrids and brown midrib hybrids ensiled at low moisture with or without a homo- and heterofermentative inoculant}, volume={101}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85041241637&partnerID=MN8TOARS}, DOI={10.3168/jds.2017-13754}, abstractNote={We evaluated the effects of adding a combination inoculant to 4 corn (Zea mays L.) hybrids harvested at low moisture on the nutritive value, fermentation profile, aerobic stability, bacterial and fungal populations, and community structure. The treatment design was the factorial combination of 4 corn hybrids ensiled with (INO) and without (CON) inoculant. The hybrids were TMF2R737 (MCN), F2F817 (MBR), P2089YHR (PCN), and PI144XR (PBR), ensiled at 44.0, 38.1, 42.0, and 41.3% of dry matter, respectively; MBR and PBR were brown midrib mutants. The inoculant contained Lactobacillus buchneri and Pediococcus pentosaceus (4 × 105 and 1 × 105 cfu/g of fresh corn). The experimental design was a complete randomized design with treatments replicated 6 times. Corn was chopped, treated or not with inoculant, packed into 7.6-L bucket silos, and stored for 100 d. At d 0, we found higher bacterial observed operational taxonomic units in the brown midrib mutants (MBR and PBR) relative to MCN and PCN (654 and 534 vs. 434 and 444 ± 15.5, respectively). The bacterial and fungal families with the highest relative abundance (RA) were Enterobacteriaceae (61.4%) and incertae sedis Tremellales (12.5%). At silo opening, we observed no effects of INO treatment on dry matter recovery (∼94.3 ± 1.07%), but aerobic stability was extended for all INO-treated hybrids (∼217 vs. ∼34.7 h), except for MBR (∼49 ± 38 h), due to a decreased yeast population (3.78 vs. 5.13 ± 0.440 log cfu/g of fresh corn) and increased acetic acid concentration (1.69 vs. 0.51 ± 0.132%) compared with the control. Furthermore, INO treatment reduced bacterial (61.2 vs. 276 ± 8.70) and increased fungal (59.8 vs. 43.6 ± 2.95) observed operational taxonomic units compared with CON. We observed that INO treatment increased the RA of Lactobacillaceae across all hybrids (∼99.1 vs. ∼58.9), and to larger extent MBR (98.3 vs. 34.3 ± 5.29), and decreased Enterobacteriaceae (0.614 vs. 23.5 ± 2.825%) among 4 other bacterial families relative to CON. For fungi, INO treatment increased the RA of Debaryomycetaceae (63.1 vs. 17.3 ± 8.55) and 5 other fungal families and decreased the RA of Pichiaceae (6.47 vs. 47.3 ± 10.95) and incertae sedis Saccharomycetales (8.47 vs. 25.9 ± 5.748) compared with CON. The bacterial and fungal community structures changed, due to ensiling, to a distinct and more stable community dominated by Lactobacillaceae and Debaryomycetaceae, respectively, when INO treatment was applied relative to CON. In conclusion, the INO treatment used in this study improved low-moisture whole-crop corn silage quality because of a shift in the bacterial and fungal community composition during ensiling.}, number={4}, journal={JOURNAL OF DAIRY SCIENCE}, publisher={Elsevier}, author={Romero, J. J. and Joo, Y. and Park, J. and Tiezzi, F. and Gutierrez-Rodriguez, E. and Castillo, M. S.}, year={2018}, month={Apr}, pages={3057–3076} }
 @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{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={http://www.scopus.com/inward/record.url?eid=2-s2.0-85044818561&partnerID=MN8TOARS}, 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={BioMed Central}, author={Tiezzi, Francesco and Arceo, Maria E. and Cole, John B. and Maltecca, Christian}, year={2018}, month={Apr} }
 @article{vogel_patisaul_arambula_tiezzi_mcgraw_2018, title={Individual Variation in Social Behaviours of Male Lab-reared Prairie voles (Microtus ochrogaster) is Non-heritable and Weakly Associated with V1aR Density}, volume={8}, ISSN={2045-2322}, url={http://dx.doi.org/10.1038/S41598-018-19737-9}, DOI={10.1038/S41598-018-19737-9}, abstractNote={Abstract}, number={1}, journal={Scientific Reports}, publisher={Springer Science and Business Media LLC}, author={Vogel, Andrea R. and Patisaul, Heather B. and Arambula, Sheryl E. and Tiezzi, Francesco and McGraw, Lisa A.}, year={2018}, month={Jan}, pages={1396} }
 @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}, volume={16}, booktitle={Proceedings, 10th World Congress of Genetics Applied to Livestock Production. Auckland, New Zealand Feb10}, 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, Volume Biology-Feed Intake and Efficiency}, author={Lu, D and Tiezzi, F and Schillebeeckx, C and McNulty, NP and Schwab, C and Maltecca, C}, year={2018}, pages={614} }
 @article{maltecca_lu_schillebeeckx_mcnulty_schawb_schull_tiezzi_2018, title={Predicting Growth and Carcass Traits in Swine Using Metagenomic Data and Machine Learning Algorithms}, journal={bioRxiv}, publisher={Cold Spring Harbor Laboratory}, author={Maltecca, Christian and Lu, Duc and Schillebeeckx, Constantino and McNulty, Nathan and Schawb, Clint and Schull, Caleb and Tiezzi, Francesco}, year={2018}, pages={363309} }
 @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}, 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} }
 @article{bobbo_tiezzi_penasa_de marchi_cassandro_2018, title={Short communication: Association analysis of diacylglycerol acyltransferase (DGAT1) mutation on chromosome 14 for milk yield and composition traits, somatic cell score, and coagulation properties in Holstein bulls}, volume={101}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85049731630&partnerID=MN8TOARS}, DOI={10.3168/jds.2018-14533}, abstractNote={The aim of the present study was to determine the allele frequencies of the diacylglycerol acyltransferase (DGAT1) K232A mutation in Italian Holstein bulls and to estimate the effect of the mutation on milk yield, composition, somatic cell score, and coagulation traits (rennet coagulation time and curd firmness). For this purpose, 349 Italian Holstein bulls were genotyped for the DGAT1 mutation on chromosome 14. Association analysis was performed by regressing the number of copies for the K allele on the deregressed estimated breeding value of the individual. Breeding values were calculated using field data routinely collected in Northeast Italy. The frequencies of the AA, KA, and KK genotypes were 59.6, 32.1, and 8.3%, respectively, and the minor allele frequency (K variant) was 24.7%. The K allele was significantly associated with greater fat yield and fat, protein, and casein percentages and with reduced protein:fat ratio. The association between the DGAT1 mutation and somatic cell score was not significant, whereas a favorable association between presence of the K allele and milk coagulation properties was found. Results from the present study confirmed the effect of the diallelic DGAT1 polymorphism K232A on milk production traits and, for the first time, provided evidence that this mutation also affects milk coagulation properties in the Italian Holstein breed.}, number={9}, journal={JOURNAL OF DAIRY SCIENCE}, publisher={Elsevier}, author={Bobbo, T. and Tiezzi, F. and Penasa, M. and De Marchi, M. and Cassandro, M.}, year={2018}, month={Sep}, pages={8087–8091} }
 @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}, 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={Journal Article A heuristic method to identify runs of homozygosity associated with reduced performance in livestock Get access J. T. Howard, J. T. Howard *Department of Animal Science, North Carolina State University, Raleigh 27695-7627 1Corresponding author: jthoward@ncsu.edu Search for other works by this author on: Oxford Academic PubMed Google Scholar F. Tiezzi, F. Tiezzi *Department of Animal Science, North Carolina State University, Raleigh 27695-7627 Search for other works by this author on: Oxford Academic PubMed Google Scholar Y. Huang, Y. Huang †Smithfield Premium Genetics, Rose Hill, NC 28458 Search for other works by this author on: Oxford Academic PubMed Google Scholar K. A. Gray, K. A. Gray †Smithfield Premium Genetics, Rose Hill, NC 28458 Search for other works by this author on: Oxford Academic PubMed Google Scholar C. Maltecca C. Maltecca *Department of Animal Science, North Carolina State University, Raleigh 27695-7627‡Genetics Program, North Carolina State University, Raleigh 27695-7627 Search for other works by this author on: Oxford Academic PubMed Google Scholar Journal of Animal Science, Volume 95, Issue 10, October 2017, Pages 4318–4332, https://doi.org/10.2527/jas2017.1664 Published: 01 October 2017 Article history Received: 25 April 2017 Accepted: 15 August 2017 Published: 01 October 2017}, number={10}, journal={Journal of Animal Science}, publisher={Oxford University Press}, author={Howard, J.T. and Tiezzi, F. and Huang, Y. and Gray, K.A. and Maltecca, C.}, year={2017}, pages={4318–4332} }
 @article{viale_tiezzi_maretto_de marchi_penasa_cassandro_2017, title={Association of candidate gene polymorphisms with milk technological traits, yield, composition, and somatic cell score in Italian Holstein-Friesian sires}, volume={100}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85023779188&partnerID=MN8TOARS}, DOI={10.3168/jds.2017-12666}, abstractNote={Advances in DNA-based marker technology have enabled the identification of genomic regions underlying complex phenotypic traits in livestock species. The incorporation of detected quantitative trait loci into genetic evaluation provides great potential to enhance selection accuracies, hence expediting the genetic improvement of economically important traits. The objective of the present study was to investigate 96 single nucleotide polymorphisms (SNP) located in 53 candidate genes previously reported to have effects on milk production and quality traits in a population of highly selected Holstein-Friesian bulls. A total of 423 semen samples were used to genotype the bulls through a custom oligo pool assay. Forty-five SNP in 32 genes were found to be associated with at least 1 of the tested traits. Most significant and favorable SNP trait associations were observed for polymorphisms located in CCL3 and AGPAT6 genes for fat yield (0.037 and 0.033 kg/d, respectively), DGKG gene for milk yield (0.698 kg/d), PPARGC1A, CSN1S1, and AGPAT6 genes for fat percentage (0.127, 0.113, and 0.093%, respectively), GHR gene for protein (0.064%) and casein percentage (0.053%), and TLR4 gene for fat (0.090%), protein (0.066%), and casein percentage (0.050%). Somatic cell score was favorably affected by GHR (-0.095) and POU1F1 (-0.137), and interesting SNP-trait associations were observed for polymorphisms located in CSN2, POU1F1, and AGPAT6 genes for rennet coagulation time (-0.592, -0.558, and -0.462 min, respectively), and GHR and CSN2 genes for curd firmness 30 min after rennet addition (1.264 and 1.183 mm, respectively). In addition to the influence of individual SNP, the effects of composite genotypes constructed by grouping SNP according to their individual effects on traits considered in the analysis were also examined. Favorable and significant effects on milk traits were observed for 2 composite genotypes, one including 10 SNP and the other 4 SNP. The former was associated with an increase of milk (0.075 kg/d), fat (0.097 kg/d), protein (0.083 kg/d), and casein yields (0.065 kg/d), and the latter was associated with an increase of fat (0.244%), protein (0.071%), and casein percentage (0.047%). Although further research is required to validate the identified SNP loci in other populations and breeds, our results can be considered as a preliminary foundation for further replication studies on gene-assisted selection programs.}, number={9}, journal={JOURNAL OF DAIRY SCIENCE}, publisher={Elsevier}, author={Viale, E. and Tiezzi, F. and Maretto, F. and De Marchi, M. and Penasa, M. and Cassandro, M.}, year={2017}, month={Sep}, pages={7271–7281} }
 @article{bonfatti_tiezzi_miglior_carnier_2017, title={Comparison of Bayesian regression models and partial least squares regression for the development of infrared prediction equations}, volume={100}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85021148382&partnerID=MN8TOARS}, DOI={10.3168/jds.2016-12203}, abstractNote={The objective of this study was to compare the prediction accuracy of 92 infrared prediction equations obtained by different statistical approaches. The predicted traits included fatty acid composition (n = 1,040); detailed protein composition (n = 1,137); lactoferrin (n = 558); pH and coagulation properties (n = 1,296); curd yield and composition obtained by a micro-cheese making procedure (n = 1,177); and Ca, P, Mg, and K contents (n = 689). The statistical methods used to develop the prediction equations were partial least squares regression (PLSR), Bayesian ridge regression, Bayes A, Bayes B, Bayes C, and Bayesian least absolute shrinkage and selection operator. Model performances were assessed, for each trait and model, in training and validation sets over 10 replicates. In validation sets, Bayesian regression models performed significantly better than PLSR for the prediction of 33 out of 92 traits, especially fatty acids, whereas they yielded a significantly lower prediction accuracy than PLSR in the prediction of 8 traits: the percentage of C18:1n-7 trans-9 in fat; the content of unglycosylated κ-casein and its percentage in protein; the content of α-lactalbumin; the percentage of αS2-casein in protein; and the contents of Ca, P, and Mg. Even though Bayesian methods produced a significant enhancement of model accuracy in many traits compared with PLSR, most variations in the coefficient of determination in validation sets were smaller than 1 percentage point. Over traits, the highest predictive ability was obtained by Bayes C even though most of the significant differences in accuracy between Bayesian regression models were negligible.}, number={9}, journal={JOURNAL OF DAIRY SCIENCE}, publisher={Elsevier}, author={Bonfatti, V. and Tiezzi, F. and Miglior, F. and Carnier, P.}, year={2017}, month={Sep}, pages={7306–7319} }
 @article{kick_tiezzi_pena_2017, title={Food Production or Food Distribution: The Key to Global Food Security?}, volume={16}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85038416671&partnerID=MN8TOARS}, DOI={10.1163/15691497-12341455}, abstractNote={Abstract}, number={6}, journal={Perspectives on Global Development and Technology}, publisher={Brill}, author={Kick, E.L. and Tiezzi, F. and Pena, D.C.}, year={2017}, pages={666–682} }
 @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}, 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} }
 @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} }
 @article{tiezzi_los campos_gaddis_maltecca_2017, title={Genotype by environment (climate) interaction improves genomic prediction for production traits in US Holstein cattle}, volume={100}, number={3}, journal={Journal of Dairy Science}, publisher={Elsevier}, author={Tiezzi, F and Los Campos, G and Gaddis, KL Parker and Maltecca, C}, year={2017}, pages={2042–2056} }
 @article{romero_zhao_balseca-paredes_tiezzi_gutierrez-rodriguez_castillo_2017, title={Laboratory silo type and inoculation effects on nutritional composition, fermentation, and bacterial and fungal communities of oat silage}, volume={100}, ISSN={["1525-3198"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85009486520&partnerID=MN8TOARS}, DOI={10.3168/jds.2016-11642}, abstractNote={The objectives were to evaluate (1) the use of 2 types of experimental silos (S) to characterize whole-crop oat (Avena sativa L.) silage with or without addition of an inoculant (I), and (2) the effect of inoculation on the microbial community structure of oats ensiled using only plastic bucket silos (BKT). From each of 6 sections in a field, oats were harvested, treated (INO) or not (CON) with inoculant, packed into 19-L BKT or vacuum bags (BG), and ensiled for 217 d. The inoculant added contained Lactobacillus buchneri and Pediococcus pentosaceus (4 × 105 and 1 × 105 cfu/g of fresh oats, respectively). The experimental design was a complete randomized design replicated 6 times. Treatment design was the factorial combination of 2 S × 2 I. Some differences existed between BG versus BKT at silo opening (217 d), including a decreased CP (7.73 vs. 7.04 ± 0.247% of DM) and ethanol (1.93 vs. 1.55 ± 0.155) and increased lactic acid (4.28 vs. 3.65 ± 0.241), respectively. Also, WSC and mold counts were reduced in BG versus BKT for CON (1.78 vs. 2.70 ± 0.162% of DM and 0.8 vs. 2.82 ± 0.409 log cfu/fresh g) but not for INO (∼1.53 and 1.55), respectively. Application of INO increased DM recovery (96.1 vs. 92.9 ± 0.63%), aerobic stability (565 vs. 133 ± 29.2 h), acetic acid (2.38 vs. 1.22 ± 0.116% of DM), and reduced NDF (65.0 vs. 67.0 ± 0.57), ADF (36.7 vs. 38.1 ± 0.60), ethanol (0.63 vs. 2.85 ± 0.155), and yeast counts (1.10 vs. 4.13 ± 0.484 log cfu/fresh g) in INO versus CON, respectively. At d 0, no differences were found for S and I on the nutritional composition and background microbial counts. Leuconostocaceae (82.9 ± 4.27%) and Enterobacteriaceae (15.2 ± 3.52) were the predominant bacterial families and unidentified sequences were predominant for fungi. A higher relative abundance of the Davidiellaceae fungal family (34.3 vs. 19.6 ± 4.47) was observed in INO versus CON. At opening (217 d), INO had a lower relative abundance of Leuconostocaceae (42.3 vs. 95.8 ± 4.64) and higher Lactobacillaceae (57.4 vs. 3.9 ± 4.65) versus CON. Despite several differences were found between BKT and BG, both techniques can be comparable for characterizing effects of INO on the most basic measures used in silage evaluation. The use of inoculant improved oat silage quality partially by a shift in the bacterial community composition during ensiling, which mainly consisted of an increased relative abundance of Lactobacillaceae and reduction of Leuconostocaceae relative to CON.}, number={3}, journal={JOURNAL OF DAIRY SCIENCE}, publisher={Elsevier}, author={Romero, J. J. and Zhao, Y. and Balseca-Paredes, M. A. and Tiezzi, F. and Gutierrez-Rodriguez, E. and Castillo, M. S.}, year={2017}, month={Mar}, pages={1812–1828} }
 @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-R2of 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-R2of 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} }
 @article{kick_tiezzi_pena_alexandratos_bruinsma_blalock hubert_diamond_fao_wfp_flora_et al._2017, title={Structural Position in the World System and Economic Growth, 1955-1970: A Multiple-Network Analysis of Transnational Interactions.}, volume={16}, number={6}, journal={Perspectives on Global Development and Technology}, publisher={Academic Press The Netherlands}, author={Kick, Edward L and Tiezzi, Francesco and Pena, Diego Castedo and Alexandratos, Nikos and Bruinsma, Jelle and Blalock Hubert, M and Diamond, Jared and fao, ifad and wfp and Flora, Cornelia Butler and et al.}, year={2017}, pages={81–117} }
 @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}, publisher={Oxford University Press}, 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} }
 @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}, number={suppl_5}, journal={Journal of Animal Science}, publisher={Oxford University Press}, author={Moretti, R and Bozzi, R and Maltecca, C and Tiezzi, F and Chessa, S and Bar, D and Biffani, S}, year={2016}, pages={187–188} }
 @article{howard_tiezzi_pryce_maltecca_2016, title={A combined coalescence forward in time simulator software for pedigreed populations undergoing selection for complex traits}, volume={94}, journal={Journal of Animal Science}, publisher={Oxford University Press, UK}, author={Howard, JT and Tiezzi, F and Pryce, JE and Maltecca, C}, year={2016}, pages={143–144} }
 @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}, journal={Journal of Animal Science}, publisher={Oxford University Press, UK}, author={Howard, JT and Tiezzi, F and Huang, Y and Gray, KA and Maltecca, C}, year={2016}, pages={26–27} }
 @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{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{bryan_maltecca_gray_huang_tiezzi_2016, title={Mitigating the effect of seasonality on sow reproductive performance using genetic selection}, volume={94}, journal={Journal of Animal Science}, publisher={Oxford University Press, UK}, author={Bryan, MR and Maltecca, C and Gray, KA and Huang, Y and Tiezzi, F}, year={2016}, pages={14–14} }
 @article{howard_tiezzi_huang_gray_maltecca_2016, title={The use of alternative genomic metrics in swine nucleus herds to manage the diversity of purebred and crossbred animals}, volume={94}, journal={Journal of Animal Science}, publisher={Oxford University Press, UK}, author={Howard, JT and Tiezzi, F and Huang, Y and Gray, KA and Maltecca, C}, year={2016}, pages={13–13} }
 @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}, publisher={American Society 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_gaddis_clay_maltecca_2015, title={Accounting for genotype by environment interaction in genomic predictions for US Holstein dairy cattle.}, number={49}, journal={Interbull Bulletin}, author={Tiezzi, Francesco and Gaddis, Kristen Parker and Clay, John S and Maltecca, Christian}, year={2015} }
 @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} }
 @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{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{penasa_tiezzi_gottardo_cassandro_de marchi_2015, title={Genetics of milk fatty acid groups predicted during routine data recording in Holstein dairy cattle}, volume={173}, ISSN={["1878-0490"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84922949081&partnerID=MN8TOARS}, DOI={10.1016/j.livsci.2014.12.014}, abstractNote={The aim of this paper was to estimate genetic parameters for groups of milk fatty acids (FA), namely saturated (SFA), unsaturated (UFA), monounsaturated (MUFA) and polyunsaturated (PUFA), in Holstein cows. Mid-infrared spectroscopy (MIRS) was used to predict FA groups (g/100 g of milk) of 72,848 samples recorded on 17,873 cows between September 2011 and November 2012. Univariate and multivariate models were implemented in a Bayesian framework to estimate (co)variance components for SFA, UFA, MUFA, PUFA, daily milk yield, milk fat and milk protein. Statistical models included fixed effect of parity by stage of lactation, and random effects of herd-test-date, cow permanent environmental, animal additive genetic and residual. Posterior means of heritability estimates for SFA, UFA, MUFA and PUFA were 0.246, 0.069, 0.082 and 0.078, respectively. Estimates of genetic correlations between FA groups ranged from 0.405 (SFA and PUFA) to 0.952 (MUFA and UFA). The increase of fat content led to an increase of all groups of FA, in particular SFA, with undesirable effects on the healthy quality of the product. The study highlighted the existence of exploitable additive genetic variation for groups of FA routinely predicted by MIRS and thus there is potential to address the selection to healthy milk FA composition.}, journal={LIVESTOCK SCIENCE}, publisher={Elsevier}, author={Penasa, Mauro and Tiezzi, Francesco and Gottardo, Paolo and Cassandro, Martino and De Marchi, Massimo}, year={2015}, month={Mar}, pages={9–13} }
 @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{dhakal_tiezzi_clay_maltecca_2015, title={Genomic selection for hoof lesions in first-parity US Holsteins}, volume={98}, number={5}, journal={Journal of dairy science}, publisher={Elsevier}, author={Dhakal, K and Tiezzi, F and Clay, JS and Maltecca, C}, year={2015}, pages={3502–3507} }
 @article{arceo_tiezzi_cole_maltecca_2015, title={Identification of gene networks underlying dystocia in dairy cattle}, volume={98}, journal={Journal of Dairy Science}, author={Arceo, Maria and Tiezzi, Francesco and Cole, John and Maltecca, Christian}, year={2015} }
 @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{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={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 H 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} }
 @article{penasa_tiezzi_sturaro_cassandro_de marchi_2014, title={A comparison of the predicted coagulation characteristics and composition of milk from multi-breed herds of Holstein-Friesian, Brown Swiss and Simmental cows}, volume={35}, ISSN={["1879-0143"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84887522327&partnerID=MN8TOARS}, DOI={10.1016/j.idairyj.2013.10.004}, abstractNote={The milk coagulation properties (MCP) and composition, as predicted by mid-infrared spectroscopy, were compared between Holstein-Friesian (HF), Brown Swiss (BS) and Simmental (SI) cows from mixed herds. Records (n = 8524) of rennet coagulation time (RCT, min) and curd firmness (a30, mm) were analysed using a mixed linear model. Milk from BS coagulated earlier and showed a firmer curd than milk from HF and SI breeds. Rennet coagulation time was shortest in the first 90 d of lactation, and a30 was lowest at the beginning and end of lactation. Herd exerted a strong effect on MCP, as the differences between the best and the worst farm for RCT and a30 were 7.8 min and 13.1 mm, respectively. In conclusion, the BS breed produced milk more suitable for cheese production than that from SI and HF. Further research is required to understand how farm management can improve coagulation characteristics of milk.}, number={1}, journal={INTERNATIONAL DAIRY JOURNAL}, publisher={Elsevier}, author={Penasa, M. and Tiezzi, F. and Sturaro, A. and Cassandro, M. and De Marchi, M.}, year={2014}, month={Mar}, pages={6–10} }
 @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={10th World Congress on Genetics Applied to Livestock Production}, author={Howard, Jeremy and Tiezzi, F and Jiao, S and Gray, KA and Maltecca, C}, year={2014} }
 @inproceedings{tiezzi_maltecca_2014, title={Genomic prediction using a weighted relationship matrix to account for trait architecture in US Holstein cattle}, booktitle={10th World Congress on Genetics Applied to Livestock Production (Vancouver, BC: American Society of Animal Science)}, author={Tiezzi, Francesco and Maltecca, C}, year={2014} }
 @article{tiezzi_pretto_de marchi_penasa_cassandro_2013, title={Heritability and repeatability of milk coagulation properties predicted by mid-infrared spectroscopy during routine data recording, and their relationships with milk yield and quality traits}, volume={7}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84883429649&partnerID=MN8TOARS}, DOI={10.1017/S1751731113001195}, abstractNote={The aim of this study was to estimate (co)variance components for milk coagulation properties (MCP) predicted by mid-infrared spectroscopy (MIRS) during routine milk recording, and to assess their relationships with yield and quality traits. A total of 63 470 milk samples from Holstein-Friesian cows were analyzed for MCP, pH and quality characteristics using MIRS. Casein to protein and protein to fat ratios were calculated from information obtained by MIRS. Records were collected across 1 year on 16 089 cows in 345 herds. The model used for genetic analysis included fixed effects of parity and stage of lactation, and random effects of herd-test-day, cow permanent environmental, animal additive genetic and residual. (Co)variance components were assessed in a Bayesian framework using the Gibbs Sampler. Estimates of heritabilities were consistent with those reported in the literature, being moderate for MCP (0.210 and 0.238 for rennet coagulation time (RCT) and curd firmness (a30), respectively), milk contents (0.213 to 0.333) and pH (0.262), and low for somatic cell score (0.093) and yield traits (0.098 to 0.130). Repeatabilities were 0.391 and 0.434 for RCT and a30, respectively, and genetic correlations were generally low, with estimates greater than 0.30 (in absolute value) only for a30 with fat, protein and casein contents. Overall, results suggest that genetic evaluation for MCP predicted by MIRS is feasible at population level, and several repeated measures per cow during a lactation are required to estimate reliable breeding values for coagulation traits.}, number={10}, journal={Animal}, publisher={Cambridge Univ Press}, author={Tiezzi, F. and Pretto, D. and De Marchi, M. and Penasa, M. and Cassandro, M.}, year={2013}, pages={1592–1599} }
 @article{gottardo_tiezzi_penasa_toffanin_cassandro_de marchi_2013, title={Milk Fatty Acids Predicted by Mid-infrared Spectroscopy in Mixed Dairy Herds}, volume={78}, number={3}, journal={Agriculturae Conspectus Scientificus}, publisher={Agronomski fakultet Zagreb}, author={Gottardo, Paolo and Tiezzi, Francesco and Penasa, Mauro and Toffanin, Valentina and Cassandro, Martino and De Marchi, Massimo}, year={2013}, pages={263–266} }
 @article{gottardo_tiezzi_penasa_toffanin_cassandro_marchi_2013, title={Milk fatty acids predicted by midinfrared spectroscopy in mixed dairy herds}, volume={78}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84883535305&partnerID=MN8TOARS}, number={3}, journal={Agriculturae Conspectus Scientificus}, author={Gottardo, P. and Tiezzi, F. and Penasa, M. and Toffanin, V. and Cassandro, M. and Marchi, M.}, year={2013}, pages={263–266} }
 @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={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 56 d 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 56 d 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{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} }
 @inproceedings{cecchinato_bonfatti_ribeca_penasa_bittante_carnier_tiezzi_others_2012, title={Effects of $β$-casein, k-casein and $β$-lactoglobulin gene allelic variants on milk production and protein composition traits of Brown Swiss cows}, booktitle={Joint ADSA-ASAS Annual Meeting}, author={Cecchinato, A and Bonfatti, V and Ribeca, C and Penasa, M and Bittante, G and Carnier, P and Tiezzi, F and others}, year={2012}, pages={402–402} }
 @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}, publisher={Elsevier}, author={Tiezzi, F. and Maltecca, C. and Cecchinato, A. and Penasa, M. and Bittante, G.}, year={2012}, pages={7355–7362} }
 @inproceedings{de marchi_penasa_tiezzi_toffanin_cassandro_2012, title={Prediction of milk coagulation properties by Fourier transform mid-infrared spectroscopy (FTMIR) for genetic purposes, herd management and dairy profitability}, booktitle={Proceedings of the 38th International Committee for Animal Recording (ICAR) Meeting; Cork, Ireland}, author={De Marchi, M and Penasa, M and Tiezzi, F and Toffanin, V and Cassandro, M}, year={2012} }
 @article{sturaro_tiezzi_penasa_de marchi_cassandro_2012, title={Study of milk coagulation properties in multibreed Italian dairy herds}, volume={3}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84866840227&partnerID=MN8TOARS}, number={SUPPL.3}, journal={Acta Agriculturae Slovenica}, author={Sturaro, Alba and Tiezzi, F and Penasa, M and De Marchi, M and Cassandro, M}, year={2012}, pages={89–92} }
 @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}, publisher={Agronomski fakultet Zagreb}, author={Tiezzi, Francesco and Penasa, Mauro and Maltecca, Christian and Cecchinato, Alessio and Bittante, Giovanni}, year={2011}, pages={239–243} }
 @article{bittante_cecchinato_cologna_penasa_tiezzi_de marchi_2011, title={Factors affecting the incidence of first-quality wheels of Trentingrana cheese}, volume={94}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-79959348212&partnerID=MN8TOARS}, DOI={10.3168/jds.2010-3746}, abstractNote={Trentingrana (or Grana Trentino) is a Protected Designation of Origin hard cheese produced in the eastern Italian Alps by small cooperative dairy factories. To obtain the certification of quality, wheels are evaluated at 9±1 mo of ripening and those classified as first quality are revaluated at 18±1 mo. Traditionally, the assessment is based on 2 sensory features: namely, the external aspect of the wheel and the internal texture; the latter is evaluated through the sound produced by beating the wheel with a special hammer. Traits considered in the study were the percentage of first-quality wheels of total wheels examined at 9±1 (QW(9 mo)) and 18±1 (QW(18 mo)) mo of ripening, and their combination [i.e., the percentage of first-quality wheels at 18±1 mo of ripening of the number of wheels evaluated at 9±1 mo (QW(tot))]. The experimental unit was the batch of 2 mo of production of each of 10 cooperative dairy factories from 2002 to 2008. Data were analyzed with a model that included fixed effects of dairy factory, year and season of production, and interactions between dairy factory and year, and dairy factory and season. The coefficients of determination of the models were 0.57, 0.68, and 0.67 for QW(9mo), QW(18 mo), and QW(tot), respectively. All factors significantly influenced the traits, with dairy factory being the most important source of variation, followed by season and year of production. Remarkable differences were found between the best and the worst dairy factory for QW(9 mo) (11.5%), QW(18 mo) (21.1%), and QW(tot) (25.6%). The first 4 yr of production had a negative effect on the percentage of wheels labeled as first quality and QW(tot) decreased from 74 to 64%; nevertheless, a complete recovery was detected in the following years. The season of production strongly influenced the studied traits with the best results in spring and summer, and the worst in autumn and winter. Compared with average, the 3 best dairy factories were smaller, with smaller associated farms, and showed lower variation across years and seasons of production. Results support the relevance of routinely assessing and monitoring the quality of Trentingrana cheese.}, number={7}, journal={Journal of Dairy Science}, publisher={Elsevier}, author={Bittante, G. and Cecchinato, A. and Cologna, N. and Penasa, M. and Tiezzi, F. and De Marchi, M.}, year={2011}, pages={3700–3707} }
 @article{tiezzi_de marchi_2011, title={G. Bittante, A. Cecchinato, N. Cologna, M. Penasa}, journal={Sources of variation in the Trentingrana cheese production chain}, author={Tiezzi, F and De Marchi, M}, year={2011}, pages={49} }
 @article{penasa_tiezzi_gasperi_2011, title={G. Bittante, N. Cologna, A. Cecchinato, M. De Marchi}, journal={Sources of variation in the Trentingrana cheese production chain}, author={Penasa, M and Tiezzi, F and Gasperi, F}, year={2011}, pages={75} }
 @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}, publisher={Elsevier}, 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{malchiodi_penasa_tiezzi_bittante_2011, title={Milk yield traits, somatic cell score, milking time and age at calving of pure Holstein versus crossbred cow}, volume={76}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-80053925477&partnerID=MN8TOARS}, number={3}, journal={Agriculturae Conspectus Scientificus}, publisher={Agronomski fakultet Zagreb}, author={Malchiodi, Francesca and Penasa, Mauro and Tiezzi, Francesco and Bittante, Giovanni}, year={2011}, pages={259–261} }
 @article{bittante_cologna_cecchinato_de marchi_penasa_tiezzi_endrizzi_gasperi_2011, title={Monitoring of sensory attributes used in the quality payment system of Trentingrana cheese}, volume={94}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-80054923606&partnerID=MN8TOARS}, DOI={10.3168/jds.2011-4319}, abstractNote={Trentingrana is a Protected Designation of Origin (PDO) hard cheese manufactured in the valleys of Trento province (eastern Italian Alps) by several small cooperative dairies linked in a consortium. Nine months after production, wheels are delivered to a shared facility, ripened up to 18 mo, and assessed by a panel of 8 experts for 7 sensory attributes; namely, external aspect, rind thickness, paste color, texture, odor, taste, and aroma. The evaluation takes place every 2 mo on wheels sampled within each dairy. Based on the results of the assessment, dairies receive a price premium or penalty depending on a quality index, which is the weighted sum of the scores attributed to each sampled wheel. Sensory scores and quality index of 652 wheels representing 11 dairies and 10 yr of production were analyzed using a model that included fixed effects of dairy, year, and season of production, and first-order interactions between them. The coefficients of determination ranged from 0.50 (texture) to 0.66 (aroma). All factors significantly affected the studied traits, with the exception of interactions between dairy and season of production for texture and external aspect, and between year and season of production for odor. Dairy was the most important source of variation for visually assessed traits (external aspect, rind thickness, paste color, and texture) and for quality index, whereas year of production was the most important for flavor attributes (odor, taste, and aroma). The latter traits were always highly correlated among them and with the quality index, whereas correlations among visually assessed attributes, between them and flavor attributes, and between them and the quality index were more erratic. The sensory evaluation performed by the panel of experts has proven to be a useful tool to define the quality index and address the payment system of Trentingrana cheese, but it has some limitations in correctly describing the sensory profile of cheese and identifying specific defects and possible remedies.}, number={11}, journal={Journal of Dairy Science}, publisher={Elsevier}, author={Bittante, G. and Cologna, N. and Cecchinato, A. and De Marchi, M. and Penasa, M. and Tiezzi, F. and Endrizzi, I. and Gasperi, F.}, year={2011}, pages={5699–5709} }
 @article{tiezzi_maltecca_2011, title={Selecting for female fertility: What can be learned from the dairy experince}, journal={Role of Genetic Evaluation Technology in Enhancing Global Competitiveness…………... 18}, author={Tiezzi, F and Maltecca, C}, year={2011}, pages={47} }
 @article{tiezzi_cecchinato_de marchi_gallo_bittante_2010, title={Characterization of buffalo production of northeast of Italy}, volume={8}, number={3s}, journal={Italian Journal of Animal Science}, author={Tiezzi, Francesco and Cecchinato, Alessio and De Marchi, Massimo and Gallo, Luigi and Bittante, Giovanni}, year={2010}, pages={160–162} }
 @inproceedings{cologna_tiezzi_de marchi_penasa_cecchinato_bittante_2010, title={Sources of variation of quality traits of herd bulk milk used for Trentingrana cheese production}, booktitle={Book of Abstracts of the 61st Annual Meeting of the European Association for Animal Production}, author={Cologna, N and Tiezzi, F and De Marchi, M and Penasa, M and Cecchinato, A and Bittante, G}, year={2010}, pages={23–27} }
 @article{tiezzi_cecchinato_marchi_gallo_bittante_2009, title={Characterization of buffalo production of northeast of Italy}, volume={8}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-70350068741&partnerID=MN8TOARS}, DOI={10.4081/ijas.2009.s3.160}, abstractNote={Abstract Aim of this study was to characterize the buffalo production in the Veneto region of Italy. Test day records of milk production traits (milk yield, protein, fat, and somatic cell count) of 845 buffalo cows from two herds were analyzed using a linear model. The effects included in the model were herd-test-day, days in milk, and parity. Days in milk was the most important source of variation for milk yield, protein, and fat. The patterns of milk yield traits across lactation followed the typical trend of buffalo cows. Results allowed a preliminary characterization of buffalo production in north of Italy.}, number={SUPPL. 3}, journal={Italian Journal of Animal Science}, author={Tiezzi, F. and Cecchinato, A. and Marchi, M. and Gallo, L. and Bittante, G.}, year={2009}, pages={160–162} }
 @article{battagin_tiezzi_cassandro_maltecca, title={Causal Relationships Between Milk Yield, Body Condition Score and Fertility in Italian Holstein Friesian Dairy Cattle.}, author={Battagin, M and Tiezzi, F and Cassandro, M and Maltecca, C} }
 @inproceedings{maltecca_lu_tiezzi, title={GENETICS AND GENOMICS OF SWINE LEAN GROWTH AT THE INTERFACE BETWEEN HOST AND COMMENSAL GUT BACTERIA}, volume={22}, booktitle={Proc. Assoc. Advmt. Anim. Breed. Genet}, author={Maltecca, C and Lu, B and Tiezzi, F}, pages={221–228} }
 @article{tiezzi, title={Genetic improvement for the Latte Nobile production system: threat or opportunity?}, journal={El modelo de LaƩe Nobile otra v́ıa de producción de leche}, author={Tiezzi, Francesco}, pages={153} }
 @article{li_tiezzi_schwab, title={Genomic analysis of pre-weaning survival in a commercial swine population}, author={Li, H and Tiezzi, F and Schwab, C} }
 @article{sartori_tiezzi_battagin_guzzo_mantovani, title={Genotype by environment interactions in productive traits in a local cattle breed due to breeding area, farming systems and feeding strategies}, author={Sartori, C and Tiezzi, F and Battagin, M and Guzzo, N and Mantovani, R} }
 @article{tiezzi_bryan_lu_gray_maltecca, title={Opportunities in modeling Genotype by Environment interaction in dairy cattle and swine.}, author={Tiezzi, F and Bryan, M and Lu, D and Gray, C Schwab2 K and Maltecca, C} }