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Mitochondrial DNA copy number (mtDNA CN) is heritable and easily obtained from low-pass sequencing (LPS). This study investigated the genetic correlation of mtDNA CN with growth and carcass traits in a multi-breed and crossbred beef cattle population. Blood, leucocyte, and semen samples were obtained from 2,371 animals and subjected to LPS that resulted in nuclear DNA (nuDNA) and mtDNA sequence reads. Mitochondrial DNA CN was estimated as the ratio of mtDNA to nuDNA coverages. Variant calling was performed from mtDNA, and 11 single nucleotide polymorphisms (SNP) were identified in the population. Samples were classified in taurine haplogroups. Haplogroup and mtDNA type were further classified based on the 11 segregating SNP. Growth and carcass traits were available for between 7,249 and 60,989 individuals. Associations of mtDNA CN, mtDNA haplogroups, mtDNA types, and mtDNA SNP with growth and carcass traits were estimated with univariate animal models, and genetic correlations were estimated with a bivariate animal model based on pedigree. Mitochondrial DNA CN tended (P -value ≤0.08) to be associated with birth weight and weaning weight. There was no association (P -value >0.10) between mtDNA SNP, haplogroups, or types with growth and carcass traits. Genetic correlation estimates of mtDNA CN were −0.30 ± 0.16 with birth weight, −0.31 ± 0.16 with weaning weight, −0.15 ± 0.14 with post-weaning gain, −0.11 ± 0.19 with average daily dry-matter intake, −0.04 ± 0.22 with average daily gain, −0.29 ± 0.13 with mature cow weight, −0.11 ± 0.13 with slaughter weight, −0.14 ± 0.13 with carcass weight, −0.07 ± 0.14 with carcass backfat, 0.14 ± 0.14 with carcass marbling, and −0.06 ± 0.14 with ribeye area. In conclusion, mtDNA CN was negatively correlated with most traits investigated, and the genetic correlation was stronger with growth traits than with carcass traits. [ABSTRACT FROM AUTHOR]

Understanding the genetic relationship between mature cow weight (MWT) and body condition score (BCS) is useful to implement selection programs focused on cow efficiency. The objectives of this study were to estimate genetic parameters, heterosis, and breed effects for MWT and BCS. In total, 25,035 and 24,522 overlapping records were available for MWT and BCS on 6,138 and 6,131 cows, respectively, from the Germplasm Evaluation program, a crossbred beef population at the U.S. Meat Animal Research Center. Pedigree was available for 48,013 individuals. Univariate animal models were used to estimate heritabilities for each trait by parity. Bivariate animal models were used to estimate genetic correlations between parities within a trait and between traits within parities. Bivariate repeatability animal models were used to estimate genetic correlations between traits across parities. Estimates of heritability for different parities ranged from 0.43 ± 0.05 to 0.55 ± 0.07 for MWT and from 0.12 ± 0.03 to 0.25 ± 0.04 for BCS and were lower with the repeatability model at 0.40 ± 0.02 and 0.11 ± 0.01 for MWT and BCS, respectively. Estimates of repeatability were high for MWT (0.67 ± 0.005) and low for BCS (0.22 ± 0.006). Estimates of genetic correlation for MWT and BCS between parities were, in general, high, especially between consecutive parities. Estimates of genetic correlation between MWT and BCS were positive and moderate, ranging from 0.32 ± 0.09 to 0.68 ± 0.14. The direct heterosis estimates were 21.56 ± 3.53 kg (P ≤ 0.001) for MWT and 0.095 ± 0.034 (P ≤ 0.001) for BCS. Ordered by decreasing MWT, the breeds ranked Brahman, Charolais, Angus, Simmental, Salers, Hereford, Santa Gertrudis, Chiangus, Brangus, Red Angus, Shorthorn, Maine-Anjou, Gelbvieh, Beefmaster, Limousin, and Braunvieh. Ordered by decreasing BCS, the breeds ranked Brahman, Red Angus, Charolais, Angus, Hereford, Brangus, Beefmaster, Chiangus, Salers, Simmental, Maine-Anjou, Limousin, Santa Gertrudis, Shorthorn, Gelbvieh, and Braunvieh. Estimates of breed differences for MWT were also adjusted for BCS (AMWT), and in general, AMWT depicted smaller differences between breeds with some degree of re-ranking (r = 0.59). These results suggest that MWT and BCS are at least moderately genetically correlated and that they would respond favorably to selection. Estimates of breed differences and heterotic effects could be used to parameterize multibreed genetic evaluations for indicators of cow maintenance energy requirements. [ABSTRACT FROM AUTHOR]

Our objectives were to evaluate the interaction between host genetics and vaginal microbiota and their relationships with antibody (Ab) response to porcine reproductive and respiratory syndrome virus (PRRSV) vaccination and farrowing performance in commercial gilts. The farrowing performance traits were number born alive, number weaning (NW), total number born, number born dead, stillborn, mummies and preweaning mortality (PWM). The vaginal microbiota was collected on days 4 (D4) and 52 (D52) after vaccination for PRRSV. Blood samples were collected on D52 for Ab measurement. Actinobacteria, Bacterioidetes, Firmicutes, Proteobacteria and Tenericutes were the most abundant Phyla identified in the vaginal microbiota. Heritability ranged from ~0 to 0.60 (Fusobacterium) on D4 and from ~0 to 0.63 (Terrisporobacter) on D52, with 43 operational taxonomic units (OTUs) presenting moderate to high heritability. One major QTL on chromosome 12 was identified for 5 OTUs (Clostridiales, Acinetobacter, Ruminococcaceae, Campylobacter and Anaerococcus), among other 19 QTL. The microbiability for Ab response to PRRSV vaccination was low for both days (<0.07). For farrowing performance, microbiability varied from <0.001 to 0.15 (NW on D4). For NW and PWM, the microbiability was greater than the heritability estimates. Actinobacillus, Streptococcus, Campylobacter, Anaerococcus, Mollicutes, Peptostreptococcus, Treponema and Fusobacterium showed different abundance between low and high Ab responders. Finally, canonical discriminant analyses revealed that vaginal microbiota was able to classify gilts in high and low Ab responders to PRRSV vaccination with a misclassification rate of <0.02. Although the microbiota explained limited variation in Ab response and farrowing performance traits, there is still potential to explore the use of vaginal microbiota to explain variation in traits such as NW and PWM. In addition, these results revealed that there is a partial control of host genetic over vaginal microbiota, suggesting a possibility for genetic selection on the vaginal microbiota. [ABSTRACT FROM AUTHOR]

Gene-editing offers the potential to increase genetic gain for complex traits, but practical issues for routine genetic evaluations need to be addressed to ensure kinship-based predictions are not biased. The objective of this study was to quantify differences in estimated breeding values (EBV) using a 8-generation simulated beef cattle population with progeny of gene-edited (GE) sires. After generating haplotypes, 99 quantitative trait nucleotides (QTN) and 6,000 single nucleotide polymorphisms (SNP) were distributed across 3 chromosomes. A single trait (h2=0.4) was simulated. After 8 generations, GE sires (n=1, 25, or 50) were introduced. The number of QTN edited (% additive variation) was 1 (2%), 3 (5%), or 13 (20%). All scenarios were replicated 15 times. Genetic evaluations were performed using pedigree (A), genomic (G), or combined (H) kinship matrices. Relationships were also weighted (w) based on the proportion of genetic variance explained by the edited QTN. To create Aw, weights were based on the expected difference between G and Gw. Hw was formed using Aw and Gw. Scenarios were compared using accuracy (correlation of true BV (TBV) and EBV), average absolute bias, and the slope (b1) of the regression of TBV on EBV. Accuracy of EBV between GE vs. non-GE sired progeny did not differ. EBV of GE sired progeny were under-dispersed (b1 from 0.61 to 0.92) and associated with greater average absolute bias (0.8 to 3.8), and these issues increased in severity with the number of edited QTN. Weighting kinship reduced the average absolute bias (from 2.7 to 0.9 with 13 edited QTN) and the dispersion of EBV (from 0.7 to 0.9 with 13 edited QTN). The same pattern was observed across all kinship matrices. Results indicate that including GE individuals in genetic evaluations could lead to biased EBV, but weighting kinship could partially mitigate this concern. [ABSTRACT FROM AUTHOR]

Genotypes generated from common single nucleotide polymorphisms (SNP) chips have been the sole source of genomic information for current beef cattle genetic evaluations. More recently, the development of low-pass whole-genome sequencing (LPS) followed by imputation to full sequence and variant calls has provided an alternative option to generate genotypes for inclusion into genetic evaluations. The objective of this study was to estimate the genotype concordance between SNP chip and LPS genotypes. Ear samples, via tissue sampling units, were obtained from 226 individuals. A subset of 109 individuals also had blood samples collected. Samples were sent to GeneSeek (Neogen company, Lincoln, NE) for genotyping (100K or 50K) and LPS and imputation was performed by Gencove. A total of 33,451 and 78,032 SNP overlapping between SNP panel and LPS for ear and blood samples, respectively, were used for further analyses. Genotype concordance was performed by counting the number of matching genotypes per SNP between (1) SNP chip vs. LPS in ear and blood samples, and (2) LPS in blood vs. LPS in ear samples. The average concordances between SNP chip vs. LPS in ear and blood samples were 0.97±0.04 and 0.98±0.06, respectively. The average concordance between LPS in blood vs. ear samples was 0.97±0.03. In conclusion, the concordance was high regardless of the tissue type, and, at least in these data, differences in resulting genetic predictions would not be expected between the use of genotypes derived from SNP chips or LPS. Additional work is warranted to determine sensitivity to relationships between target animals and the reference set used for imputation. The USDA is an equal opportunity provider and employer. [ABSTRACT FROM AUTHOR]

Studies on differences in resilience to porcine reproductive and respiratory syndrome (PRRS) virus (PRRSV) between breeds are scarce in the literature. Thus, the objective of this work was to assess PRRSV resilience in PRRSV wild-type infected sows from two breeds. Farrowing data included 2546 and 2522 litters from 894 Duroc and 813 Landrace sows, respectively, which were housed together and experienced the same PRRSV outbreak. Traits used for this study were number of piglets born alive (NBA), number born dead (NBD), total number born (TNB), and number weaned (NW). The impact of PRRSV infection was evaluated by comparing the reproductive performance of breeds between PRRS phases (pre-PRRS, PRRS, and post-PRRS). PRRS phases were defined based on the reproductive performance data. NBA, NBD, and NW were analyzed as a proportion of TNB using a Poisson mixed model. Pre-defined contrasts were used to evaluate the effect of breed on PRRSV resilience and on return to PRRSV-free performance, representing the differences between breeds for the difference between pre-PRRS and PRRS phases, and pre-PRRS and post-PRRS phases, respectively. There was a significant (P ≤ 0.003) interaction between PRRS phase and breed for all traits, as shown in Table 1. In general, reproductive performance reduced from pre-PRRS to PRRS, and then increased from PRRS to post-PRRS, as expected. The resilience contrast was significant for all traits (P ≤ 0.003). In all cases, the drop in percent reproductive performance from pre-PRRS to PRRS was lower for Duroc than for Landrace, indicating that Duroc sows have greater PRRSV resilience than Landrace sows. The return to PRRSV-free performance contrast had a trending effect for NBD (P = 0.055), and it was not significant for the other traits (P ≥ 0.515). These results indicate that Duroc sows have overall greater phenotypic PRRSV resilience for reproductive performance than Landrace sows. [ABSTRACT FROM AUTHOR]

Previous studies on genomics of antibody response, measured as sample-to-positive (S/P) ratio, to Porcine Reproductive and Respiratory Syndrome virus (PRRSV) have reported a major quantitative trait locus (QTL) on the major histocompatibility complex (MHC) on chromosome 7, explaining ~25% of the genetic variance of this trait. S/P ratio following modified live PRRSV vaccination in crossbred commercial gilts has been proposed as genetic indicator for reproductive performance in non-infected purebred sows and PRRSV-vaccinated crossbred sows. This motivated further genomic study for this trait by performing haplotype-based genome-wide association study (GWAS). 906 naïve F1 (Landrace x Large White) had blood samples taken at ~50d after vaccination for measuring PRRSV ELISA S/P ratio and genotyping. Haplotype-based GWAS identified 8 genomic regions on chromosomes 4 (108 Mb), 7 (15, 21, and 24-27 Mb), and 9 (33 Mb) that were associated (q-value < 0.07) with S/P ratio. From those, only the MHC region (chromosome 7; 24 - 26 Mb) had been identified in the SNP based GWAS. The main SNP identified in the SNP based GWAS (H3GA0020505) was not in LD with the haplotype; thus, we added this SNP to the haplotype model. We observed that the haplotype explained more of the genetic variance compared to the H3GA0020505 SNP, indicating that the MHC haplotype is in stronger LD with the QTL than the H3GA0020505 SNP. All the significant regions associated with S/P ratio included immune-related candidate genes, such as SLA-DOB, TAP2, TAPBP, TMIGD3, and ADORA. This study validated the QTL identified on the MHC region, narrowing the search for causal genes in this region, and identified new genomic regions, along with candidate genes associated with S/P ratio. Identifying novel genomic regions provides more resources for markerassisted selection and genomic prediction of S/P ratio in purebred and commercial pig populations. [ABSTRACT FROM AUTHOR]

Immunoglobulin G antibody response, measured as sample-to-positive (S/P) ratio, to Porcine Reproductive and Respiratory Syndrome virus (PRRSV) has been proposed as an indicator trait for improved reproductive performance in PRRSV-infected purebred sows and PRRSV-vaccinated crossbred gilts. In this study, we investigated the genetic correlations (rg) of S/P ratio following a PRRSV outbreak and PRRSVvaccination with performance in non-exposed and PRRSV-exposed sows. PRRSV outbreak phase was defined based on previously described methodologies after the detection of typical clinical signs of PRRSV infection. 541 Landrace sows had S/P ratio measured at ~54 days after the beginning of the PRRSV outbreak (S/Poutbreak), and 906 Landrace x Large White naïve F1 gilts had S/P ratio measured at ~50 days after vaccination with a commercial modified live PRRSV vaccine (S/PVx). 711 and 428 Landrace sows had reproductive performance recorded before and during the PRRSV outbreak, respectively. 811 vaccinated F1 animals had farrowing performance for up to 3 parities. All animals were genotyped for ~28K SNPs. The estimate of rg of S/Poutbreakwith S/PVx was high (rg±SE = 0.72±0.18). Estimates of rg of S/Poutbreak with reproductive performance in F1 sows were low to moderate, ranging from 0.05±0.23 (number stillborn) to 0.30±0.20 (total number born). Estimates of rg of S/PVxwith reproductive performance in non-infected purebred sows were moderate and favorable with number born alive (0.50±0.23), but low (0 to -0.11±0.23) with litter mortality traits. Estimates of rg of S/PVx were moderate and negative (-0.47±0.18) with the number of mummies in PRRSV-infected purebred sows and low with other traits (-0.29±0.18 for total number born to 0.05±0.18 for number stillborn). These results indicate that selection for antibody response following a PRRSV outbreak collected in purebred sows and to PRRSV vaccination collected in commercial crossbred gilts may increase litter size of non-infected and PRRSVexposed purebred and commercial crossbred sows. [ABSTRACT FROM AUTHOR]

Statistical training is a major component in the education of animal science graduate students (ASGS). Although most graduate courses in animal science require ASGS to take credits in statistics courses, ASGS differ on their educational and professional interest in statistics. Thus, the aim of this study was to identify associations between the overall graduate and statistical training of ASGS with the statistical interest related to their professional career path. Data from a nationwide statistical training survey was used and included information on 381 ASGS from 42 universities. Analyses focused on identifying overall graduate and statistical training factors associated with ASGS's answers to the following questions: "Gives statistical advices to lab peers" (No/Yes), "Gives statistical advices to others outside the lab" (No/Yes), "Desired career path after graduation" (Academia/Industry/Other), and "Desire to be involved in statistical analysis in desired career path" (No/Somewhat/Yes). Data were analyzed using logistic regression model including 11 fixed effects, such as degree being pursued (MS/PhD), years of graduate education (covariate), previous professional experience (No/Yes), preferred stats software (SAS/R/SAS and R/Other), etc. There were significant associations (P < 0.05) for all four analyses. ASGS were more likely to give statistical advice to lab peers as they had more years of graduate education [odds-ratio (OR)=1.33], had additional training in statistics (OR=1.92), and had analyzed their own dataset (OR=4.46). ASGS with and without previous professional experience had greater desire to go to industry (OR=1.3) and academia (OR=1.23), respectively. ASGS interested in being involved in statistics in their future career had greater years of graduate education (OR=1.31) and credits in stats courses (OR=1.31), had previously completed a graduate degree (OR=1.49), had previous research experience (OR=1.5), and had additional training in statistics (OR=1.54). These results indicate that a variety of experiences prior to and during graduate ASGS education are associated with their interest in statistics. [ABSTRACT FROM AUTHOR]

Previous studies proposed the use of antibody response to Porcine Reproductive and Respiratory Syndrome virus (PRRSV), measured as sample-to-positive (S/P) ratio, as a potential indicator trait to improve the reproductive performance of PRRSV-infected Landrace sows. However, this indicator trait has not yet been validated in Landrace sows or evaluated in a terminal sire line, such as Duroc. The main objective of this work was to perform host-genetic analyses of S/P ratio and reproductive traits during a PRRSV outbreak in maternal and terminal breeds. The data consisted of 690 Duroc and 541 Landrace multiparous sows (1.9±1.2 and 2.3±1.5, respectively) with S/P ratio collected at approximately 54 days after the predicted beginning of the outbreak. Of these, 644 Duroc and 528 Landrace sows also had reproductive data recorded during the PRRSV outbreak comprising number of piglets born alive (NBA), stillborn piglets (NSB), mummified piglets (NBM), number born dead (NBD; sum of NSB and NBM), total number born (TNB; sum of NBA and NBD), and number weaned (NW). All animals had genotype data on ~30K SNPs common across both breeds. Heritability estimates (± standard error) of S/P ratio during the PRRSV outbreak were moderate, with 0.33±0.06 for Duroc and 0.28±0.07 for Landrace. Reproductive traits during the PRRSV outbreak had overall low heritability estimates (≤0.18). Favorable genetic correlations of S/P ratio with NBA (0.65±0.33), in accordance with previous studies, and NBD (-0.33±0.28) were observed for Landrace sows only. Estimates of genetic correlation with other traits were -0.21±0.30 (NBM), -0.12±0.29 (NSB), 0.10±0.38 (NW), and 0.54±1.29 (TNB) for Landrace. For Duroc, these estimates were weaker: -0.33±0.40 (NBA), 0.26±0.27 (NBA), and 0.28±0.30 (NW), with convergence issues for mortality traits. These results further support the use of S/P ratio as an indicator trait for improved reproductive performance during a PRRSV outbreak in Landrace sows. [ABSTRACT FROM AUTHOR]

Losses due to infectious diseases are one of the main factors affecting productivity in the swine industry, motivating the investigation of disease resilience-related traits for genetic selection. However, these traits are not expected to be expressed in the nucleus herds, where selection is performed. One alternative is to use information from the commercial level to identify and select nucleus animals genetically superior for coping with pathogen challenges. In this study, we analyzed the genetic basis of antibody (Ab) response to common infectious pathogens in health-challenged commercial swine herds as potential indicator traits for disease resilience, including Ab response to influenza A virus of swine (IAV), Mycoplasma hyopneumoniae (MH), porcine circovirus (PCV2), and Actinobacillus pleuropneumoniae (APP; different serotypes). Ab response was measured in blood at entry into gilt rearing, post-acclimation (∼40 days after entering the commercial herd), and parities 1 and 2. Heritability estimates for Ab response to IAV, MH, and PCV2 ranged from 0 to 0.76. Ab response to APP ranged from 0 to 0.40. The genetic correlation (r G ) of Ab response to IAV with MH, PCV2, PRRSV, and APP mean (average Ab responses for all serotypes of APP) were positive (>0.29) at entry. APP mean was negatively correlated with PCV2 and MH at entry and parity 2 but positively correlated with MH at post-acclimation and parity 1. Genomic regions associated with Ab response to different APP serotypes were identified on 13 chromosomes. The region on chromosome 14 (2 Mb) was associated with several serotypes of APP, explaining up to 4.3% of the genetic variance of Ab to APP7 at entry. In general, genomic prediction accuracies for Ab response were low to moderate, except average Ab response to all infectious pathogens evaluated. These results suggest that genetic selection of Ab response in commercial sows is possible, but with variable success depending on the trait and the time-point of collection. Future work is needed to determine genetic correlations of Ab response with disease resilience, reproductive performance, and other production traits., (Copyright © 2020 Sanglard, PigGen Canada, Mote, Willson, Harding, Plastow, Dekkers and Serão.)