@article{earnhardt-san_gray_knauer_2023, title={Genetic Parameter Estimates for Teat and Mammary Traits in Commercial Sows}, volume={13}, ISSN={["2076-2615"]}, DOI={10.3390/ani13152400}, abstractNote={The objective was to evaluate the genetics of sow teat and mammary traits at farrowing and at weaning. Data were recorded on 3099 Landrace × Large White F1 sows. Underline traits included the total teat number (TT), the functional teat number (FT), the non-functional teat number (NFT), the damaged teat number (DT), and the number of functional mammary glands (FMG). Variance components were estimated using AIREMLF90. Means for TT, FT, and NFT at farrowing were 14.93, 13.90, and 1.03, respectively. Heritability estimates for TT, FT, and NFT ranged from 0.18 to 0.37, 0.16 to 0.28, and 0.14 to 0.18, respectively. Estimates of heritability for DT and FMG at weaning were 0.03 and 0.06, respectively. Estimated genetic correlations between FT with TT and NFT were 0.68 to 0.78 and −0.19 to −0.57, respectively. Genetic correlation estimates between TT, FT, and NFT with the number weaned were 0.25, 0.50, and −0.38, respectively. An increase of one TT and FT enhanced (p < 0.05) the number weaned by 0.14 to 0.16 and 0.18 to 0.27 piglets, respectively. The results suggest that genetically increasing the number of functional teats on a sow at farrowing would improve the number of piglets at weaning.}, number={15}, journal={ANIMALS}, author={Earnhardt-San, Audrey L. and Gray, Kent A. and Knauer, Mark T.}, year={2023}, month={Aug} }
 @article{obermier_howard_gray_knauer_2023, title={The impact of functional teat number on reproductive throughput in swine}, volume={7}, ISSN={["2573-2102"]}, DOI={10.1093/tas/txad100}, abstractNote={Abstract
               The objective was to evaluate the impact of functional teat number on reproductive throughput in swine. Data included 735 multiparous Landrace × Large White F1 females. Sow underlined traits consisted of total teat number (TT), functional teat number (FT), nonfunctional teat number (NFT), and number of functional mammary glands (FMG). Weaning traits were calculated for both the biological and the nurse dam. For the biological dam, litter size at weaning (LSW) included a sow’s biological piglets regardless of cross-fostering. For nurse dam, number weaned (NW) included the piglets a sow weaned. For the biological dam, piglet survival (PS) was calculated as litter size at weaning / (total number born × 100). Linear regression estimates were calculated in RStudio v. 1.1.456 and variance components were estimated using GIBBS3F90. Average total number born, number born alive, TT, FT, NFT, and FMG were 14.22, 13.12, 14.43, 13.96, 0.42, and 10.7, respectively. An increase in one FT enhanced (P < 0.05) LSW by 0.32 piglets and NW by 0.33 piglets. Similarly, an increase in one FT improved (P < 0.05) PS by 1.63% and reduced (P < 0.05) preweaning mortality by 2.73%. However, an increase in one FT reduced (P < 0.05) average piglet weaning weight (WW) for biological and nurse dams by 35 and 94 g, respectively. Yet an increase in one FT enhanced (P < 0.05) litter weaning weight (LWW) for biological and nurse dams by 1.3 and 1.5 kg, respectively. Heritability estimates for TT, FT, NFT, FMG, WW, LWW, LSW, and PS were 0.25, 0.22, 0.53, 0.18, 0.21, 0.22, 0.16, and 0.18, respectively. Genetic correlation estimates between FT with TT, NFT, and FMG were 0.79, 0.09, and 0.28, respectively. Estimated genetic correlations between TT with WW, LWW, LSW, and PS were 0.37, 0.38, 0.11, and −0.19, respectively. Genetic correlation estimates between FT with WW, LWW, LSW, and PS were 0.44, 0.49, 0.39, and 0.35, respectively. Results suggest increasing functional teat number would enhance both piglet survival and reproductive throughput.}, number={1}, journal={TRANSLATIONAL ANIMAL SCIENCE}, author={Obermier, Dalton R. and Howard, Jeremy Thomas and Gray, Kent A. and Knauer, Mark T.}, year={2023}, month={Jan} }
 @article{he_tiezzi_jiang_howard_huang_gray_choi_maltecca_2022, title={Exploring methods to summarize gut microbiota composition for microbiability estimation and phenotypic prediction in swine}, volume={100}, ISSN={["1525-3163"]}, url={https://doi.org/10.1093/jas/skac231}, DOI={10.1093/jas/skac231}, abstractNote={Abstract
               The microbial composition resemblance among individuals in a group can be summarized in a square covariance matrix and fitted in linear models. We investigated eight approaches to create the matrix that quantified the resemblance between animals based on the gut microbiota composition. We aimed to compare the performance of different methods in estimating trait microbiability and predicting growth and body composition traits in three pig breeds. This study included 651 purebred boars from either breed: Duroc (n = 205), Landrace (n = 226), and Large White (n = 220). Growth and body composition traits, including body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content, were measured on live animals at the market weight (156 ± 2.5 d of age). Rectal swabs were taken from each animal at 158 ± 4 d of age and subjected to 16S rRNA gene sequencing. Eight methods were used to create the microbial similarity matrices, including 4 kernel functions (Linear Kernel, LK; Polynomial Kernel, PK; Gaussian Kernel, GK; Arc-cosine Kernel with one hidden layer, AK1), 2 dissimilarity methods (Bray-Curtis, BC; Jaccard, JA), and 2 ordination methods (Metric Multidimensional Scaling, MDS; Detrended Correspondence analysis, DCA). Based on the matrix used, microbiability estimates ranged from 0.07 to 0.21 and 0.12 to 0.53 for Duroc, 0.03 to 0.21 and 0.05 to 0.44 for Landrace, and 0.02 to 0.24 and 0.05 to 0.52 for Large White pigs averaged over traits in the model with sire, pen, and microbiome, and model with the only microbiome, respectively. The GK, JA, BC, and AK1 obtained greater microbiability estimates than the remaining methods across traits and breeds. Predictions were made within each breed group using four-fold cross-validation based on the relatedness of sires in each breed group. The prediction accuracy ranged from 0.03 to 0.18 for BW, 0.08 to 0.31 for BF, 0.21 to 0.48 for LD, and 0.04 to 0.16 for IMF when averaged across breeds. The BC, MDS, LK, and JA achieved better accuracy than other methods in most predictions. Overall, the PK and DCA exhibited the worst performance compared to other microbiability estimation and prediction methods. The current study shows how alternative approaches summarized the resemblance of gut microbiota composition among animals and contributed this information to variance component estimation and phenotypic prediction in swine.}, number={9}, journal={JOURNAL OF ANIMAL SCIENCE}, author={He, Yuqing and Tiezzi, Francesco and Jiang, Jicai and Howard, Jeremy and Huang, Yijian and Gray, Kent and Choi, Jung-Woo and Maltecca, Christian}, year={2022}, month={Sep} }
 @article{he_tiezzi_howard_huang_gray_maltecca_2022, title={Exploring the role of gut microbiota in host feeding behavior among breeds in swine}, volume={22}, ISSN={["1471-2180"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85122218151&partnerID=MN8TOARS}, DOI={10.1186/s12866-021-02409-6}, abstractNote={Abstract
                Background
                The interplay between the gut microbiota and feeding behavior has consequences for host metabolism and health. The present study aimed to explore gut microbiota overall influence on feeding behavior traits and to identify specific microbes associated with the traits in three commercial swine breeds at three growth stages. Feeding behavior measures were obtained from 651 pigs of three breeds (Duroc, Landrace, and Large White) from an average 73 to 163 days of age. Seven feeding behavior traits covered the information of feed intake, feeder occupation time, feeding rate, and the number of visits to the feeder. Rectal swabs were collected from each pig at 73 ± 3, 123 ± 4, and 158 ± 4 days of age. DNA was extracted and subjected to 16 S rRNA gene sequencing.
              
                Results
                Differences in feeding behavior traits among breeds during each period were found. The proportion of phenotypic variances of feeding behavior explained by the gut microbial composition was small to moderate (ranged from 0.09 to 0.31). A total of 21, 10, and 35 amplicon sequence variants were found to be significantly (q-value < 0.05) associated with feeding behavior traits for Duroc, Landrace, and Large White across the three sampling time points. The identified amplicon sequence variants were annotated to five phyla, with Firmicutes being the most abundant. Those amplicon sequence variants were assigned to 28 genera, mainly including Christensenellaceae_R-7_group, Ruminococcaceae_UCG-004, Dorea, Ruminococcaceae_UCG-014, and Marvinbryantia.
              
                Conclusions
                This study demonstrated the importance of the gut microbial composition in interacting with the host feeding behavior and identified multiple archaea and bacteria associated with feeding behavior measures in pigs from either Duroc, Landrace, or Large White breeds at three growth stages. Our study provides insight into the interaction between gut microbiota and feeding behavior and highlights the genetic background and age effects in swine microbial studies.
              }, number={1}, journal={BMC MICROBIOLOGY}, author={He, Yuqing and Tiezzi, Francesco and Howard, Jeremy and Huang, Yijian and Gray, Kent and Maltecca, Christian}, year={2022}, month={Jan} }
 @article{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_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{scanlan_putz_gray_serao_2019, title={Genetic analysis of reproductive performance in sows during porcine reproductive and respiratory syndrome (PRRS) and porcine epidemic diarrhea (PED) outbreaks}, volume={10}, ISSN={["2049-1891"]}, DOI={10.1186/s40104-019-0330-0}, abstractNote={Porcine reproductive and respiratory syndrome (PRRS) is one of the most infectious swine diseases in the world, resulting in over 600 million dollars of economic loss in the USA alone. More recently, the USA swine industry has been having additional major economic losses due to the spread of porcine epidemic diarrhea (PED). However, information regarding the amount of genetic variation for response to diseases in reproductive sows is still very limited. The objectives of this study were to identify periods of infection with of PRRS virus (PRRSV) and/or PED virus (PEDV), and to estimate the impact their impact on the phenotypic and genetic reproductive performance of commercial sows.Disease (PRRS or PED) was significant (P < 0.05) for all traits analyzed except for total piglets born. Heritability estimates for traits during Clean (without any disease), PRRS, and PED ranged from 0.01 (number of mummies; Clean and PED) to 0.41 (abortion; PED). Genetic correlations between traits within disease statuses ranged from -0.99 (proportion born dead with number weaned; PRRS) to 0.99 (number born dead with born alive; Clean). Within trait, between disease statuses, estimates ranged from - 0.17 (number weaned between PRRS and PED) to 0.99 (abortion between Clean and PRRS).Results indicate that selection for improved performance during PRRS and PED in commercial sows is possible and would not negatively impact performance in Clean environments.}, journal={JOURNAL OF ANIMAL SCIENCE AND BIOTECHNOLOGY}, author={Scanlan, Cassandra L. and Putz, Austin M. and Gray, Kent A. and Serao, Nick V. L.}, year={2019}, month={Mar} }
 @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={AbstractSwine industry breeding goals are mostly directed towards meat quality and carcass traits due to their high economic value. Yet, studies on meat quality and carcass traits including both phenotypic and genotypic information remain limited, particularly in commercial crossbred swine. The objectives of this study were to estimate the heritabilities for different carcass composition traits and meat quality traits and to estimate the genetic and phenotypic correlations between meat quality, carcass composition, and growth traits in 2 large commercial swine populations: The Maschhoffs LLC (TML) and Smithfield Premium Genetics (SPG), using genotypes and phenotypes data. The TML data set consists of 1,254 crossbred pigs genotyped with 60K SNP chip and phenotyped for meat quality, carcass composition, and growth traits. The SPG population included over 35,000 crossbred pigs phenotyped for meat quality, carcass composition, and growth traits. For TML data sets, the model included fixed effects of dam line, contemporary group (CG), gender, as well as random additive genetic effect and pen nested within CG. For the SPG data set, fixed effects included parity, gender, and CG, as well as random additive genetic effect and harvest group. Analyses were conducted using BLUPF90 suite of programs. Univariate and bivariate analyses were implemented to estimate heritabilities and correlations among traits. Primal yield traits were uniquely created in this study. Heritabilities [high posterior density interval] of meat quality traits ranged from 0.08 [0.03, 0.16] for pH and 0.08 [0.03, 0.1] for Minolta b* to 0.27 [0.22, 0.32] for marbling score, except intramuscular fat with the highest estimate of 0.52 [0.40, 0.62]. Heritabilities of primal yield traits were higher than that of primal weight traits and ranged from 0.17 [0.13, 0.25] for butt yield to 0.45 [0.36, 0.55] for ham yield. The genetic correlations of meat quality and carcass composition traits with growth traits ranged from moderate to high in both directions. High genetic correlations were observed for male and female for all traits except pH. The genetic parameter estimates of this study indicate that a multitrait approach should be considered for selection programs aimed at meat quality and carcass composition in commercial swine populations.}, number={9}, journal={JOURNAL OF ANIMAL SCIENCE}, publisher={Oxford University Press US}, author={Khanal, Piush and Maltecca, Christian and Schwab, Clint and Gray, Kent and Tiezzi, Francesco}, year={2019}, month={Sep}, pages={3669–3683} }