Search

Ocular squamous cell carcinoma and infectious keratoconjunctivitis are common ocular pathologies in Hereford cattle with considerable economic impact. Both pathologies have been associated with low eyelid pigmentation, and thus, genetic selection for higher eyelid pigmentation could reduce their incidence. The objective of the present study was to reveal the genetic basis of eyelid pigmentation in Hereford cattle. The analysis included a single-step genome-wide association study (ssGWAS) and a subsequent gene-set analysis in order to identify individual genes, genetic mechanisms, and biological pathways implicated in this trait. Data consisted of eyelid pigmentation records in 1,165 Hereford bulls and steers, visually assessed in five categories between 0% and 100%. Genotypic data for 774,660 single-nucleotide polymorphism markers were available for 886 animals with pigmentation records. Pedigree information of three generations of ancestors of animals with phenotype was considered in this study, with a total of 4,929 animals. Our analyses revealed that eyelid pigmentation is a moderately heritable trait, with heritability estimates around 0.41. The ssGWAS identified at least eight regions, located on BTA1, BTA3, BTA5, BTA14, BTA16, BTA18, BTA19, and BTA24, associated with eyelid pigmentation. These regions harbor genes that are directly implicated in melanocyte biology and skin pigmentation, such as ADCY8, PLD1, KITLG, and PRKCA. The gene-set analysis revealed several functional terms closely related to melanogenesis, such as positive regulation of melanocyte differentiation and regulation of ERK1 and ERK2 cascade. Overall, our findings provide evidence that eyelid pigmentation is a heritable trait influenced by many loci. Indeed, the ssGWAS detected several candidate genes that are directly implicated in melanocyte biology, including melanogenesis. This study contributes to a better understanding of the genetic and biological basis of eyelid pigmentation and presents novel information that could aid to design breeding strategies for reducing the incidence of ocular pathologies in cattle. Additional research on the genetic link between eyelid pigmentation and ocular pathologies is needed.Low eyelid pigmentation is considered as a predisposing factor associated with common ocular pathologies in cattle, such as ocular squamous cell carcinoma and infectious keratoconjunctivitis, with considerable economic impact. The aim of our study was to investigate the genetic basis of eyelid pigmentation in Hereford cattle. The analysis included estimation of genetic parameters, a genome-wide association study, and a subsequent gene-set analysis to identify individual genes, genetic mechanisms, and biological pathways implicated in eyelid pigmentation. Our results indicate that eyelid pigmentation is a complex trait, with a moderate heritability around 0.41, and affected by multiple loci, including genes related to melanocyte biology, melanogenesis, skin pigmentation, and development of melanoma. Evidence that biological processes such as melanocyte development and melanocyte differentiation explain part of the observed variation in eyelid pigmentation were also found. Overall, this study provides a better understanding of the genetics underlying eyelid pigmentation in Hereford. Our findings could contribute to point out breeding strategies for reducing the incidence of ocular pathologies in cattle.

Composite breeds are widely used in the beef industry. Composites allow producers to combine desirable traits from the progenitor breeds and simplify herd management, without repeated crossbreeding and maintenance of purebreds. In this study, genomic information was used to evaluate the genetic composition and characteristics of a three-breed beef cattle composite. This composite population referred to as Composite Gene Combination (CGC) consisted of 50% Red Angus, 25% Charolais, and 25% Tarentaise. A total of 248 animals were used in this study: CGC (n = 79), Red Angus (n = 61), Charolais (n = 79), and Tarentaise (n = 29). All animals were genotyped with 777k HD panel. Principal component and ADMIXTURE analyses were carried out to evaluate the genetic structure of CGC animals. The ADMIXTURE revealed the proportion of Tarentaise increased to approximately 57%, whereas Charolais decreased to approximately 5% and Red Angus decreased to 38% across generations. To evaluate these changes in the genomic composition across different breeds and in CGC across generations, runs of homozygosity (ROH) were conducted. This analysis showed Red Angus to have the highest total length of ROH segments per animal with a mean of 349.92 Mb and lowest in CGC with a mean of 141.10 Mb. Furthermore, it showed the formation of new haplotypes in CGC around the sixth generation. Selection signatures were evaluated through Fst and HapFlk analyses. Several selection sweeps in CGC were identified especially in chromosomes 5 and 14 which have previously been reported to be associated with coat color and growth traits. The study supports our previous findings that progenitor combinations are not stable over generations and that either direct or natural selection plays a role in modifying the progenitor proportions. Furthermore, the results showed that Tarentaise contributed useful attributes to the composite in a cool semi-arid environment and suggests a re-exploration of this breed's role may be warranted.Composite breeds are commonly used in the U.S. beef industry since they provide producers with benefits such as breed complementarity and retained heterosis. However, cattle composite genomes are not well characterized. Therefore, in this study, genomic information was used to evaluate the genetic composition and characteristics of a three-breed composite (50% Red Angus, 25% Charolais, and 25% Tarentaise). The analysis showed an increase in the proportion of Tarentaise to approximately 57%, whereas Charolais decreased to approximately 5% and Red Angus decreased to 38%. Furthermore, new genome segments formed around the sixth generation. These changes show that progenitor breed proportions are not stable over generations and that either direct or natural selection plays a role in modifying the proportions. The increase in Tarentaise proportion suggests useful attributes to the composite in a cool semi-arid environment.

Heat stress (HS) triggers oxidative stress, systemic inflammation, and disrupts growth efficiency of livestock. β-adrenergic agonists supplemented to ruminant livestock improve growth performance, increase skeletal muscle mass, and decrease carcass fat. The objective of this study was to understand the independent and interacting effects of HS and zilpaterol hydrochloride (ZH) supplementation on the transcriptome of subcutaneous white adipose tissue and the longissimus dorsi muscle in steers. Twenty-four Red Angus-based steers were assigned to thermoneutral (TN; Temperature Humidity Index [THI] = 68) or HS (THI = 73-85) conditions and were not supplemented or supplemented with ZH (8.33 mg/kg/d) for 21 d in a 2 × 2 factorial. Steers in the TN condition were pair-fed to the average daily feed intake of HS steers. RNA was isolated from adipose tissue and skeletal muscle samples collected via biopsy on 3, 10, and 21 d and sequenced using 3' Tag-Seq to an achieved average depth of 3.6 million reads/sample. Transcripts, mapped to ARS-UCD1.2, were quantified. Differential expression (DE) analyses were performed in DESeq2 with a significance threshold for false discovery rate of 0.05. In adipose, 4 loci (MISP3, APOL6, SLC25A4, and S100A12) were DE due to ZH on day 3, and 2 (RRAD, ALB) were DE due to the interaction of HS and ZH on day 10 (Padj0.05). In muscle, 40 loci (including TENM4 and OAZ1) were DE due to ZH on day 10, and 6 loci (HIF1A, LOC101903734, PDZD9, HNRNPU, MTUS1, and TMCO6) were DE due to environment on day 21 (Padj0.05). To explore biological pathways altered by environment, supplement, and their interaction, loci with DE (Praw0.05) were evaluated in Ingenuity Pathway Analysis. In adipose, 509 pathways were predicted to be altered (P0.01): 202 due to HS, 126 due to ZH, and 181 due to the interaction; these included inflammatory pathways predicted to be upregulated due to HS but downregulated due to the interaction of HS and ZH. In muscle, 113 pathways were predicted to be altered (P0.01): 23 due to HS, 66 due to ZH, and 24 due to the interaction of HS and ZH. Loci and pathway data in muscle suggest HS induced oxidative stress and that the stress response was moderated by ZH. Metabolic pathways were predicted to be altered due to HS, ZH, and their interaction in both tissues. These data provide evidence that HS and ZH interact to alter expression of genes in metabolic and immune function pathways and that ZH moderates some adverse effects of HS.Heat stress (HS) negatively impacts livestock health and carcass quality. Supplementation of livestock with β-adrenergic agonists (β-AA) increases muscle mass and decreases fat deposition. The purpose of this study was to understand how HS and zilpaterol hydrochloride (ZH), a β-AA, alter gene expression in muscle and in adipose of cattle. Twenty-four steers were assigned to thermoneutral (TN) or HS conditions and were not supplemented (NS) or supplemented with ZH for 21 d. RNA was isolated from muscle and adipose collected on days 3, 10, and 21 to identify changes in gene expression. Several individual loci were differentially expressed (DE) due to HS or ZH in both tissues while the interaction of HS and ZH altered expression in adipose. A less stringent definition of DE used to explore biological pathways predicted that both treatments alter metabolism. Pathway analyses also supported that HS increased inflammation in adipose, but that these inflammatory pathways were downregulated by ZH. HS also was predicted to induce oxidative stress in muscle although ZH moderated this response. This study provides information on how HS and β-AA act independently and interact to alter physiology, lending insight useful for the development of management and mitigation strategies for stress.

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.The current study estimated the genetic relationship between mature cow weight (MWT) and body condition score (BCS), heterosis, and breed effects for these traits in a crossbred beef population. In total, 25,035 and 24,522 overlapping records were available for MWT and BCS, respectively. Pedigree was available for 48,013 individuals. Heritability and genetic correlations were estimated within a trait between parities, between traits within parities, and between traits across parities. Estimates of heritability ranged from 0.40 ± 0.02 to 0.55 ± 0.07 for MWT and from 0.11 ± 0.01 to 0.25 ± 0.04 for BCS. Genetic correlations within a trait and between parities were, in general, high. Estimates of genetic correlation between MWT and BCS were positive and moderate, ranging from 0.32 ± 0.09 to 0.68 ± 0.14. Heterosis effects were 21.56 ± 3.53 kg for MWT and 0.095 ± 0.034 for BCS. For both traits, Brahman and Braunvieh were associated with the highest and lowest breed effects, respectively. These results suggest that MWT and BCS would respond favorably to selection and are moderately genetically correlated. Breed differences and heterotic effects could be used to parameterize multibreed genetic evaluations for indicators of cow maintenance energy requirements.

Backfat thickness (BFT) is complex and economically important traits in the pig industry, since it reflects fat deposition and can be used to measure the carcass lean meat percentage in pigs. In this study, all 6,550 pigs were genotyped using the Geneseek Porcine 50K SNP Chip to identify SNPs related to BFT and to search for candidate genes through genome-wide association analysis in two Duroc populations. In total, 80 SNPs, including 39 significant and 41 suggestive SNPs, and 6 QTLs were identified significantly associated with the BFT. In addition, 9 candidate genes, including a proven major gene MC4R, 3 important candidate genes (RYR1, HMGA1, and NUDT3) which were previously described as related to BFT, and 5 novel candidate genes (SIRT2, NKAIN2, AMH, SORCS1, and SORCS3) were found based on their potential functional roles in BFT. The functions of candidate genes and gene set enrichment analysis indicate that most important pathways are related to energy homeostasis and adipogenesis. Finally, our data suggest that most of the candidate genes can be directly used for genetic improvement through molecular markers, except that the MC4R gene has an antagonistic effect on growth rate and carcass lean meat percentage in breeding. Our results will advance our understanding of the complex genetic architecture of BFT traits and laid the foundation for additional genetic studies to increase carcass lean meat percentage of pig through marker-assisted selection and/or genomic selection.Backfat thickness (BFT) is a complex and economically important trait in the pig industry because it reflects fat deposition and can be used to measure the carcass lean meat percentage in pigs. In this study, two Duroc populations were genotyped using SNP chips, and genome-wide association analysis was used to identify SNPs and candidate genes related to BFT. A number of genetic markers and candidate genes including MC4R, RYR1, HMGA1, NUDT3, SIRT2, NKAIN2, AMH, SORCS1, and SORCS3 were identified to be significantly related to BFT. Our data suggest that many of the candidate genes can be directly used for genetic improvement through molecular markers.

Understanding the changes in the swine female reproductive system is important for solving issues related to reproductive failure and litter size. Elucidating the regulatory mechanisms of the natural estrous cycle in the oviduct under non-fertilisation conditions can improve our understanding of its role in the reproductive system. Herein, whole transcriptome RNA sequencing of oviduct tissue samples was performed. The differentially expressed genes (DEGs) were identified for each time point relative to day 0 and classified into three clusters based on their expression patterns. Clusters 1 and 2 included genes involved in the physiological changes through the estrous cycle. Cluster 1 genes were mainly involved in PI3K-Akt signaling and steroid hormone biosynthesis pathways. Cluster 2 genes were involved in extracellular matrix-receptor interactions and protein digestion pathways. In Cluster 3, the DEGs were downregulated in the luteal phase; they were strongly associated with cell cycle, calcium signaling, and oocyte meiosis. The gene expression in the oviduct during the estrous cycle influenced oocyte transport and fertilization. Our findings provide a basis for successfully breeding pigs and elucidating the mechanisms underlying the changes in the pig oviduct during the estrous cycle.Understanding the swine female reproductive system is important for solving issues related to reproductive failure and litter size. The oviduct is the site of fertilization. After fertilization, the fertilized egg moves to the uterus for implantation. Elucidating the regulatory mechanisms of the estrous cycle in the oviduct can improve our understanding of their roles. In this study, whole transcriptome RNA sequencing of oviduct tissue samples was performed throughout the estrous cycle to screen for differentially expressed genes (DEGs). The DEGs were classified into three clusters based on their expression patterns. Clusters 1 and 2 included genes involved in the physiological changes observed through the estrous cycle. The expression levels of Cluster 3 genes were downregulated specifically in the luteal phase; this was associated with calcium signalling and oocyte meiosis. In this study, we identified that the expression of genes in the oviduct influences oocyte transport and fertilization, which are the key functions of the oviduct. This study provides a basis for successful breeding in the pig industry and elucidating the mechanisms underlying the changes in the pig oviduct during the estrous cycle.

Fecal egg count (FEC) is an indicative measurement for parasite infection in sheep. Different FEC methods may show inconsistent results. Not accounting for inconsistencies can be problematic when integrating measurements from different FEC methods for genetic evaluation. The objectives of this study were to evaluate the difference in means and variances between two fecal egg counting methods used in sheep—the Modified McMaster (LMMR) and the Triple Chamber McMaster (LTCM); to estimate variance components for the two FEC methods, treating them as two different traits; and to integrate FEC data from the two different methods and estimate genetic parameters for FEC and other gastrointestinal parasite resistance traits. Fecal samples were collected from a commercial Rideau-Arcott sheep farm in Ontario. Fecal egg counting was performed using both LMMR and the LTCM methods. Other parasite resistance trait records were collected from the same farm including eye score (FAMACHA), body condition score (BCS), and body weight (WT). The two FEC methods were highly genetically (0.94) and phenotypically (0.88) correlated. However, the mean and variance between the two FEC methods were significantly different (P < 0.0001). Therefore, re-scaling is required prior to integrating data from the different methods. For the multiple trait analysis, data from the two fecal egg counting methods were integrated (LFEC) by using records for the LMMR when available and replacing missing records with re-standardized LTCM records converted to the same mean and variance of LMMR. Heritability estimates were 0.12 ± 0.04, 0.07 ± 0.05, 0.17 ± 0.06, and 0.24 ± 0.07 for LFEC egg count, FAMACHA, BCS, and WT, respectively. The estimated genetic correlations between FEC and the other parasite resistance traits were low and not significant (P > 0.05) for FAMACHA (r = 0.24 ± 0.32) and WT (r = 0.22 ± 0.19), and essentially zero for BCS (r = −0.03 ± 0.25), suggesting little to no benefit of using such traits as indicators for LFEC.

Ovine progressive pneumonia virus (OPPV) is a small ruminant lentivirus that is widespread throughout U.S. sheep flocks. Infections with OPPV are lifelong and effects are multi-systemic with significant implications for animal well-being and productivity. A protein isoform with lysine at position 35 (K35, haplotype “1”) encoded by the ovine transmembrane protein 154 (TMEM154) gene has been associated with reduced susceptibility to infection when two copies are present (i.e., diplotype “1,1”). Conversely, the ancestral protein isoform with glutamate at position 35 (E35, haplotype “3”) is associated with high susceptibility to infection when at least one copy is present. The beneficial effect of TMEM154 K35 alleles on ewe productivity has not been previously measured in controlled challenge experiments and was a major objective of this study. Ewes with TMEM154 diplotypes “1,1”; “1,3”; and “3,3” (n = 31, 47, and 30, respectively) were born and reared by OPPV-infected dams and managed under continual natural exposure to OPPV. Ewes were tested for serological status at 4-mo intervals for up to 5.5 yr. The incidence of infection in ewes with diplotype “1,1” was 6.5% to 9.7% and significantly lower (P < 0.001) than ewes with diplotype “1,3” (60.5 to 97.3%) or “3,3” (64.0 to 91.4%). Furthermore, the incidence among ewes with diplotype “1,1” did not increase from 10 to 67 mo of age (P > 0.99), whereas the incidence among diplotype “1,3” and “3,3” ewes increased steadily until reaching an asymptote at approximately 52 mo of age. Total number and weight of lamb weaned per ewe exposed through 5.5 yr from ewes with diplotype “1,1” far exceeded (P ≤ 0.05) those with diplotypes “1,3” and “3,3” by, on average, 2.1 lambs and 40 kg, respectively. The present study confirmed that TMEM154 diplotype “1,1” animals have reduced incidence of OPPV infection and, correspondingly, improved productivity. In flocks with a high frequency of TMEM154 haplotype “3,” selection for haplotype “1” appears to be a cost-effective approach to mitigate the impact of this economically important disease.

Selective genotyping of crossbred (CB) animals to include in traditionally purebred (PB) dominated genetic evaluations has been shown to provide an increase in the response to selection for CB performance. However, the inclusion of phenotypes from selectively genotyped CB animals, without the phenotypes of their non-genotyped cohorts, could cause bias in estimated variance components (VC) and subsequent estimated breeding values (EBV). The objective of the study was to determine the impact of selective CB genotyping on VC estimates and subsequent bias in EBV when non-genotyped CB animals are not included in genetic evaluations. A swine crossbreeding scheme producing 3-way CB animals was simulated to create selectively genotyped datasets. The breeding scheme consisted of three PB breeds each with 25 males and 450 females, F1 crosses with 1200 females and 12,000 CB progeny. Eighteen chromosomes each with 100 QTL and 4k SNP markers were simulated. Both PB and CB performance were considered to be moderately heritable (h2=0.4). Factors evaluated were, 1) CB phenotype and genotype inclusion of 15% (n=1800) or 35% (n=4200), 2) genetic correlation between PB and CB performance (rpc=0.1, 0.5 or 0.7) and 3) selective genotyping strategy. Genotyping strategies included: a) Random: random CB selection, b) Top: highest CB phenotype and c) Extreme: half highest and half lowest CB phenotypes. Top and Extreme selective genotyping strategies were considered by selecting animals in full-sib (FS) families or among the CB population (T). In each generation, 4320 PB selection candidates contributed phenotypic and genotypic records. Each scenario was replicated 15 times. VC were estimated for PB and CB performance utilizing bivariate models using pedigree relationships with dams of CB animals considered to be unknown. Estimated values of VC for PB performance were not statistically different from true values. Top selective genotyping strategies produced deflated estimates of phenotypic VC for CB performance compared to true values. When using estimated VC, Top_T and Extreme_T produced the most biased EBV, yet EBV of PB selection candidates for CB performance were most accurate when using Extreme_T. Results suggest that randomly selecting CB animals to genotype or selectively genotyping Top or Extreme CB animals within full-sib families can lead to accurate estimates of additive genetic VC for CB performance and unbiased EBV.

Inbreeding depression reduces the mean phenotypic value of important traits in livestock populations. The goal of this work was to estimate the level of inbreeding and inbreeding depression for growth and reproductive traits in Argentinean Brangus cattle, in order to obtain a diagnosis and monitor breed management. Data comprised 359,257 (from which 1,990 were genotyped for 40,678 single nucleotide polymorphisms [SNPs]) animals with phenotypic records for at least one of three growth traits: birth weight (BW), weaning weight (WW), and finishing weight (FW). For scrotal circumference (SC), 52,399 phenotypic records (of which 256 had genotype) were available. There were 530,938 animals in pedigree. Three methods to estimate inbreeding coefficients were used. Pedigree-based inbreeding coefficients were estimated accounting for missing parents. Inbreeding coefficients combining genotyped and nongenotyped animal information were also computed from matrix H of the single-step approach. Genomic inbreeding coefficients were estimated using homozygous segments obtained from a Hidden Markov model (HMM) approach. Inbreeding depression was estimated from the regression of the phenotype on inbreeding coefficients in a multiple-trait mixed model framework, either for the whole dataset or for the dataset of genotyped animals. All traits were unfavorably affected by inbreeding depression. A 10% increase in pedigree-based or combined inbreeding would result in a reduction of 0.34 to 0.39 kg in BW, 2.77 to 3.28 kg in WW, and 0.23 cm in SC. For FW, a 10% increase in pedigree-based, genomic, or combined inbreeding would result in a decrease of 8.05 to 11.57 kg. Genomic inbreeding based on the HMM was able to capture inbreeding depression, even in such a compressed genotyped dataset.

The objectives of the present study were to estimate genetic parameters for several feeding behavior traits in growing cattle, as well as the genetic associations among and between feeding behavior and both performance and feed efficiency traits. An additional objective was to investigate the use of feeding behavior traits as predictors of genetic merit for feed intake. Feed intake and live-weight data on 6,088 growing cattle were used of which 4,672 had ultrasound data and 1,548 had feeding behavior data. Feeding behavior traits were defined based on individual feed events or meal events (where individual feed events were grouped into meals). Univariate and bivariate animal linear mixed models were used to estimate (co)variance components. Heritability estimates (± SE) for the feeding behavior traits ranged from 0.19 ± 0.08 for meals per day to 0.61 ± 0.10 for feeding time per day. The coefficient of genetic variation per trait varied from 5% for meals per day to 22% for the duration of each feed event. Genetically heavier cattle, those with a higher daily energy intake (MEI), or those that grew faster had a faster feeding rate, as well as a greater energy intake per feed event and per meal. Better daily feed efficiency (i.e., lower residual energy intake) was genetically associated with both a shorter feeding time per day and shorter meal time per day. In a validation population of 321 steers and heifers, the ability of estimated breeding values (EBV) for MEI to predict (adjusted) phenotypic MEI was demonstrated; EBVs for MEI were estimated using multi-trait models with different sets of predictor traits such as liveweight and/or feeding behaviors. The correlation (± SE) between phenotypic MEI and EBV for MEI marginally improved (P < 0.001) from 0.64 ± 0.03 to 0.68 ± 0.03 when feeding behavior phenotypes from the validation population were included in a genetic evaluation that already included phenotypic mid-test metabolic live-weight from the validation population. This is one of the largest studies demonstrating that significant exploitable genetic variation exists in the feeding behavior of young crossbred growing cattle; such feeding behavior traits are also genetically correlated with several performance and feed efficiency metrics. Nonetheless, there was only a marginal benefit to the inclusion of time-related feeding behavior phenotypes in a genetic evaluation for MEI to improve the precision of the EBVs for this trait.

Fertility is a key factor in the economic success of horse farms. However, it has received little attention due to the difficulty of measuring fertility objectively. Since its studbook creation (1912), the Pura Raza Española (PRE) breed has been a closed population and become high in-bred resulting in inbreeding depression (poor phenotypic values). Nevertheless, heterogeneous effects of inbreeding depression have been detected among founders and nonfounders. The aims of this study were (1) to analyze the genetic parameters for reproductive traits in mares of the PRE horse breed and (2) to estimate, for the first time, the inbreeding depression load associated with common ancestors of the breed. A total of 22, 799 mares were analyzed. Heritability estimates ranged from 0.05 (interval between first and second foaling) to 0.16 (age at first foaling), whereas inbreeding depression load ratios ranged from 0.06 (parturition efficiency at 6th foaling) to 0.17 (age at first foaling), for a partial inbreeding coefficient of 10%. Although heritability is related to the variability expressed in the population, inbreeding depression load ratios measure the potential variability, whether expressed in the population or not. Most correlations between additive and inbreeding depression load genetic values were significant (P < 0.001) and of low to moderate magnitude. Our results confirm that individual inbreeding depression loads allow us to select horses that have a genetic value resistant to the deleterious effects of inbreeding. © The Author(s) 2021. Published by Oxford University Press on behalf of the American Society of Animal Science.

For swine breeding programs, testing and selection programs are usually within purebred (PB) populations located in nucleus units that are generally managed differently and tend to have a higher health level than the commercial herds in which the crossbred (CB) descendants of these nucleus animals are expected to perform. This approach assumes that PB animals selected in the nucleus herd will have CB progeny that have superior performance at the commercial level. There is clear evidence that this may not be the case for all traits of economic importance and, thus, including data collected at the commercial herd level may increase the accuracy of selection for commercial CB performance at the nucleus level. The goal for this study was to estimate genetic parameters for five maternal reproductive traits between two PB maternal nucleus populations (Landrace and Yorkshire) and their CB offspring: Total Number Born (TNB), Number Born Alive (NBA), Number Born Alive > 1 kg (NBA > 1 kg), Total Number Weaned (TNW), and Litter Weight at Weaning (LWW). Estimates were based on single-step GBLUP by analyzing any two combinations of a PB and the CB population, and by analyzing all three populations jointly. The genomic relationship matrix between the three populations was generated by using within-population allele frequencies for relationships within a population, and across-population allele frequencies for relationships of the CB with the PB animals. Utilization of metafounders for the two PB populations had no effect on parameter estimates, so the two PB populations were assumed to be genetically unrelated. Joint analysis of two (one PB plus CB) vs. three (both PB and CB) populations did not impact estimates of heritability, additive genetic variance, and genetic correlations. Heritabilities were generally similar between the PB and CB populations, except for LWW and TNW, for which PB populations had about four times larger estimates than CB. Purebred-crossbred genetic correlations (rpc) were larger for Landrace than for Yorkshire, except for NBA > 1 kg. These estimates of rpc indicate that there is potential to improve selection of PB animals for CB performance by including CB information for all traits in the Yorkshire population, but that noticeable additional gains may only occur for NBA > 1 kg and TNW in the Landrace population.

Pigs with complete resistance to porcine reproductive and respiratory syndrome (PRRS) virus (PRRSV) have been produced by genetically knocking out the CD163 gene that encodes a receptor of the PRRSV for entry into macrophages. The objectives of this study were to evaluate associations of naturally occurring single nucleotide polymorphisms (SNPs) in the CD163 gene and in three other candidate genes (CD169, RGS16, and TRAF1) with host response to PRRSV-only infection and to PRRS vaccination and PRRSV/porcine circovirus 2b (PCV2b) coinfection. SNPs in the CD163 gene were not included on SNP genotyping panels that were used for previous genome-wide association analyses of these data. An additional objective was to identify the potential genetic interaction of variants at these four candidate genes with a mutation in the GBP5 gene that was previously identified to be associated with host response to PRRSV infection. Finally, the association of SNPs with expression level of the nearby gene was tested. Several SNPs in the CD163, CD169, and RGS16 genes were significantly associated with host response under PRRSV-only and/or PRRSV/PCV2b coinfection. The effects of all SNPs that were significant in the PRRSV-only infection trials depend on genetic background. The effects of some SNPs in the CD163, CD169, and RGS16 genes depend on genotype at the putative causative mutation in the GBP5 gene, which indicates a potential biological interaction of these genes with GBP5. In addition, genome-wide association results for the PRRSV-only infection trials revealed that SNPs located in the CDK5RAP2 or MEGF9 genes, near the TRAF1 gene, had suggestive effects on PRRS viral load, which indicates that these SNPs might contribute to PRRSV neuropathogenesis. In conclusion, natural genetic variants in the CD163, CD169, and RGS16 genes are associated with resistance to PRRSV and/or PCV2b infection and appear to interact with the resistance quantitative trait locus in the GBP5 gene. The identified SNPs can be used to select for increased natural resistance to PRRSV and/or PRRSV-PCV2b coinfection.

Recent years have seen a revolution in single-cell technologies, particularly single-cell RNA-sequencing (scRNA-seq). As the number, size and complexity of scRNA-seq datasets continue to increase, so does the number of computational methods and software tools for extracting meaning from them. Since 2016 the scRNA-tools database has catalogued software tools for analysing scRNA-seq data. With the number of tools in the database passing 1000, we take this opportunity to provide an update on the state of the project and the field. Analysis of five years of analysis tool tracking data clearly shows the evolution of the field, and that the focus of developers has moved from ordering cells on continuous trajectories to integrating multiple samples and making use of reference datasets. We also find evidence that open science practices reward developers with increased recognition and help accelerate the field.

Accuracy of genomic predictions is an important component of the selection response. The objectives of this research were: 1) to investigate trends for prediction accuracies over time in a broiler population of accumulated phenotypes, genotypes, and pedigrees and 2) to test if data from distant generations are useful to maintain prediction accuracies in selection candidates. The data contained 820K phenotypes for a growth trait (GT), 200K for two feed efficiency traits (FE1 and FE2), and 42K for a carcass yield trait (CY). The pedigree included 1,252,619 birds hatched over 7 years, of which 154,318 from the last 4 years were genotyped. Training populations were constructed adding 1 year of data sequentially, persistency of accuracy over time was evaluated using predictions from birds hatched in the three generations following or in the years after the training populations. In the first generation, before genotypes became available for the training populations (first 3 years of data), accuracies remained almost stable with successive additions of phenotypes and pedigree to the accumulated dataset. The inclusion of 1 year of genotypes in addition to 4 years of phenotypes and pedigree in the training population led to increases in accuracy of 54% for GT, 76% for FE1, 110% for CY, and 38% for FE2; on average, 74% of the increase was due to genomics. Prediction accuracies declined faster without than with genomic information in the training populations. When genotypes were unavailable, the average decline in prediction accuracy across traits was 41% from the first to the second generation of validation, and 51% from the second to the third generation of validation. When genotypes were available, the average decline across traits was 14% from the first to the second generation of validation, and 3% from the second to the third generation of validation. Prediction accuracies in the last three generations were the same when the training population included 5 or 2 years of data, and a decrease of ~7% was observed when the training population included only 1 year of data. Training sets including genomic information provided an increase in accuracy and persistence of genomic predictions compared with training sets without genomic data. The two most recent years of pedigree, phenotypic, and genomic data were sufficient to maintain prediction accuracies in selection candidates. Similar conclusions were obtained using validation populations per year.

The Berlin Fat Mouse Inbred (BFMI) line is a model for juvenile obesity. Previous studies on crosses between BFMI and C57Bl/6N (B6N) have identified a recessive defect causing juvenile obesity on chromosome 3 (jObes1). Bbs7 was identified as the most likely candidate gene for the observed effect. Comparative sequence analysis showed a 1578 bp deletion in intron 8 of Bbs7 in BFMI mice. A CTCF-element is located inside this deletion. To investigate the functional effect of this deletion, it was introduced into B6N mice using CRISPR/Cas9. Two mice containing the target deletion were obtained (B6N Bbs7emI8∆1 and Bbs7emI8∆2) and were subsequently mated to BFMI and B6N to generate two families suitable for complementation. Inherited alleles were determined and body composition was measured by quantitative magnetic resonance. Evidence for a partial complementation (13.1–15.1%) of the jObes1 allele by the CRISPR/Cas9 modified B6N Bbs7emI8∆1 and Bbs7emI8∆2 alleles was found. Mice carrying the complementation alleles had a 23–27% higher fat-to-lean ratio compared to animals which have a B6N allele (P(Bbs7emI8∆1) = 4.25 × 10–7; P(Bbs7emI8∆2) = 3.17 × 10–5). Consistent with previous findings, the recessive effect of the BFMI allele was also seen for the B6N Bbs7emI8∆1 and Bbs7emI8∆2 alleles. However, the effect size of the B6N Bbs7emI8∆1 and Bbs7emI8∆2 alleles was smaller than the BFMI allele, and thus showed only a partial complementation. Findings suggest additional variants near Bbs7 in addition to or interacting with the deletion in intron 8.

It is of interest to evaluate crossbred pigs for hot carcass weight (HCW) and birth weight (BW); however, obtaining a HCW record is dependent on livability (LIV) and retained tag (RT). The purpose of this study is to analyze how HCW evaluations are affected when herd removal and missing identification are included in the model and examine if accounting for the reasons for missing traits improves the accuracy of predicting breeding values. Pedigree information was available for 1,965,077 purebred and crossbred animals. Records for 503,716 commercial three-way crossbred terminal animals from 2014 to 2019 were provided by Smithfield Premium Genetics. Two pedigree-based models were compared; model 1 (M1) was a threshold-linear model with all four traits (BW, HCW, RT, and LIV), and model 2 (M2) was a linear model including only BW and HCW. The fixed effects used in the model were contemporary group, sex, age at harvest (for HCW only), and dam parity. The random effects included direct additive genetic and random litter effects. Accuracy, dispersion, bias, and Pearson correlations were estimated using the linear regression method. The heritabilities were 0.11, 0.07, 0.02, and 0.04 for BW, HCW, RT, and LIV, respectively, with standard errors less than 0.01. No difference was observed in heritabilities or accuracies for BW and HCW between M1 and M2. Accuracies were 0.33, 0.37, 0.19, and 0.23 for BW, HCW, RT, and LIV, respectively. The genetic correlation between BW and RT was 0.34 ± 0.03, and between BW and LIV was 0.56 ± 0.03. Similarly, the genetic correlation between HCW and RT was 0.26 ± 0.04, and between HCW and LIV was 0.09 ± 0.05, respectively. The positive and moderate genetic correlations between BW and other traits imply a heavier BW resulted in a higher probability of surviving to harvest. Genetic correlations between HCW and other traits were lower due to the large quantity of missing records. Despite the heritable and correlated aspects of RT and LIV, results imply no major differences between M1 and M2; hence, it is unnecessary to include these traits in classical models for BW and HCW.

Genomic selection has been adopted nationally and internationally in different livestock and plant species. However, understanding whether genomic selection has been effective or not is an essential question for both industry and academia. Once genomic evaluation started being used, estimation of breeding values with pedigree best linear unbiased prediction (BLUP) became biased because this method does not consider selection using genomic information. Hence, the effective starting point of genomic selection can be detected in two possible ways including the divergence of genetic trends and Realized Mendelian sampling (RMS) trends obtained with BLUP and single-step genomic BLUP (ssGBLUP). This study aimed to find the start date of genomic selection for a set of economically important traits in three livestock species by comparing trends obtained using BLUP and ssGBLUP. Three datasets were used for this purpose: 1) a pig dataset with 117k genotypes and 1.3M animals in pedigree, 2) an Angus cattle dataset consisted of ~842k genotypes and 11.5M animals in pedigree, and 3) a purebred broiler chicken dataset included ~154k genotypes and 1.3M birds in pedigree were used. The genetic trends for pigs diverged for the genotyped animals born in 2014 for average daily gain (ADG) and backfat (BF). In beef cattle, the trends started diverging in 2009 for weaning weight (WW) and in 2016 for postweaning gain (PWG), with little divergence for birth weight (BTW). In broiler chickens, the genetic trends estimated by ssGBLUP and BLUP diverged at breeding cycle 6 for two out of the three production traits. The RMS trends for the genotyped pigs diverged for animals born in 2014, more for ADG than for BF. In beef cattle, the RMS trends started diverging in 2009 for WW and in 2016 for PWG, with a trivial trend for BTW. In broiler chickens, the RMS trends from ssGBLUP and BLUP diverged strongly for two production traits at breeding cycle 6, with a slight divergence for another trait. Divergence of the genetic trends from ssGBLUP and BLUP indicates the onset of the genomic selection. The presence of trends for RMS indicates selective genotyping, with or without the genomic selection. The onset of genomic selection and genotyping strategies agrees with industry practices across the three species. In summary, the effective start of genomic selection can be detected by the divergence between genetic and RMS trends from BLUP and ssGBLUP.

Aleutian disease (AD), caused by the Aleutian mink disease virus (AMDV), is a major health concern that results in global economic losses to the mink industry. The unsatisfactory outcome of the culling strategy, immunoprophylaxis, and medical treatment in controlling AD have urged mink farmers to select AD resilient mink based on several detection tests, including enzyme-linked immunosorbent assay (ELISA), counterimmunoelectrophoresis (CIEP), and iodine agglutination test (IAT). However, the genetic analysis of these AD tests and their correlations with pelt quality, reproductive performance, packed-cell volume (PCV), and harvest length (HL) have not been investigated. In this study, data on 5,824 mink were used to estimate the genetic and phenotypic parameters of four AD tests, including two systems of ELISA, CIEP, and IAT, and their genetic and phenotypic correlations with two pelt quality, five female reproductive performance, PCV, and HL traits. Significances (P < 0.05) of fixed effects (sex, year, dam age, and color type), covariates (age at harvest and blood sampling), and random effects (additive genetic, permanent environmental, and maternal effects) were determined under univariate models using ASReml 4.1 software. The genetic and phenotypic parameters for all traits were estimated under bivariate models using ASReml 4.1 software. Estimated heritabilities (±SE) were 0.39 ± 0.06, 0.61 ± 0.07, 0.11 ± 0.07, and 0.26 ± 0.05 for AMDV antigen-based ELISA (ELISA-G), AMDV capsid protein-based ELISA, CIEP, and IAT, respectively. The ELISA-G also showed a moderate repeatability (0.58 ± 0.04) and had significant negative genetic correlations (±SE) with reproductive performance traits (from −0.41 ± 0.16 to −0.49 ± 0.12), PCV (−0.53 ± 0.09), and HL (−0.45 ± 0.16). These results indicated that ELISA-G had the potential to be applied as an indicator trait for genetic selection of AD resilient mink in AD endemic ranches and therefore help mink farmers to reduce the adverse effects caused by AD.

Pig survival is an economically important trait with relevant social welfare implications, thus standing out as an important selection criterion for the current pig farming system. We aimed to estimate (co)variance components for survival in different production phases in a crossbred pig population as well as to investigate the benefit of including genomic information through single-step genomic best linear unbiased prediction (ssGBLUP) on the prediction accuracy of survival traits compared with results from traditional BLUP. Individual survival records on, at most, 64,894 crossbred piglets were evaluated under two multi-trait threshold models. The first model included farrowing, lactation, and combined postweaning survival, whereas the second model included nursery and finishing survival. Direct and maternal breeding values were estimated using BLUP and ssGBLUP methods. Furthermore, prediction accuracy, bias, and dispersion were accessed using the linear regression validation method. Direct heritability estimates for survival in all studied phases were low (from 0.02 to 0.08). Survival in preweaning phases (farrowing and lactation) was controlled by the dam and piglet additive genetic effects, although the maternal side was more important. Postweaning phases (nursery, finishing, and the combination of both) showed the same or higher direct heritabilities compared with preweaning phases. The genetic correlations between survival traits within preweaning and postweaning phases were favorable and strong, but correlations between preweaning and postweaning phases were moderate. The prediction accuracy of survival traits was low, although it increased by including genomic information through ssGBLUP compared with the prediction accuracy from BLUP. Direct and maternal breeding values were similarly accurate with BLUP, but direct breeding values benefited more from genomic information. Overall, a slight increase in bias was observed when genomic information was included, whereas dispersion of breeding values was greatly reduced. Combined postweaning survival presented higher direct heritability than in the preweaning phases and the highest prediction accuracy among all evaluated production phases, therefore standing out as a candidate trait for improving survival. Survival is a complex trait with low heritability; however, important genetic gains can still be obtained, especially under a genomic prediction framework.

To investigate single nucleotide polymorphism (SNP) loci associated with yearling wool traits of fine-wool sheep for optimizing marker-assisted selection and dissection of the genetic architecture of wool traits, we conducted a genome-wide association study (GWAS) based on the fixed and random model circulating probability unification (FarmCPU) for yearling staple length (YSL), yearling mean fiber diameter (YFD), yearling greasy fleece weight (YGFW), and yearling clean fleece rate (YCFR) by using the whole-genome re-sequenced data (totaling 577 sheep) from the following four fine-wool sheep breeds in China: Alpine Merino sheep (AMS), Chinese Merino sheep (CMS), Qinghai fine-wool sheep (QHS), and Aohan fine-wool sheep (AHS). A total of 16 SNPs were detected above the genome-wise significant threshold (P = 5.45E-09), and 79 SNPs were located above the suggestive significance threshold (P = 5.00E-07) from the GWAS results. For YFD and YGFW traits, 7 and 9 SNPs reached the genome-wise significance thresholds, whereas 10 and 12 SNPs reached the suggestive significance threshold, respectively. For YSL and YCFR traits, none of the SNPs reached the genome-wise significance thresholds, whereas 57 SNPs exceeded the suggestive significance threshold. We recorded 14 genes located at the region of ±50-kb near the genome-wise significant SNPs and 59 genes located at the region of ±50-kb near the suggestive significant SNPs. Meanwhile, we used the Average Information Restricted Maximum likelihood algorithm (AI-REML) in the “HIBLUP” package to estimate the heritability and variance components of the four desired yearling wool traits. The estimated heritability values (h2) of YSL, YFD, YGFW, and YCFR were 0.6208, 0.7460, 0.6758, and 0.5559, respectively. We noted that the genetic parameters in this study can be used for fine-wool sheep breeding. The newly detected significant SNPs and the newly identified candidate genes in this study would enhance our understanding of yearling wool formation, and significant SNPs can be applied to genome selection in fine-wool sheep breeding.

The commercial beef cattle industry relies heavily on the use of natural service sires. When artificial insemination is deemed difficult to implement, multisire breeding pastures are used to increase reproductive rates in large breeding herds or to safe-guard against bull injury during the breeding season. Although each bull might be given an equal opportunity to produce offspring, evidence suggest that there is substantial variation in the number of calves sired by each bull in a breeding pasture. With the use of DNA-based paternity testing, correctly assigning calves to their respective sires in multisire pastures is possible and presents an opportunity to investigate the degree to which this trait complex is under genetic control. Field data from a large commercial ranch was used to estimate genetic parameters for calf count (CC; 574 records from 443 sires) and yearling scrotal circumference (SC; n = 1961) using univariate and bivariate animal models. Calf counts averaged 12.2 ± 10.7 and SC averaged 35.4 ± 2.30 cm. Bulls had an average of 1.30 records and there were 23.9 ± 11.1 bulls per contemporary group. The model for CC included fixed effects of age during the breeding season (in years) and contemporary group (concatenation of breeding pasture and year). Random effects included additive genetic and permanent environmental effects, and a residual. The model for SC included fixed effects of age (in days) and contemporary group (concatenation of month and year of measurement). Random effects included an additive genetic effect and a residual. Univariate model heritability estimates for CC and SC were 0.178 ± 0.142 and 0.455 ± 0.072, respectively. Similarly, the bivariate model resulted in heritability estimates for CC and SC of 0.184 ± 0.142 and 0.457 ± 0.072, respectively. Repeatability estimates for CC from univariate and bivariate models were 0.315 ± 0.080 and 0.317 ± 0.080, respectively. The estimate of genetic correlation between CC and SC was 0.268 ± 0.274. Heritability estimates suggest that both CC and SC would respond favorably to selection. Moreover, CC is lowly repeatable and although favorably correlated, SC appears to be weakly associated with CC.

Bitter taste perception in sheep can lead to avoidance of specific types of forage, such as sagebrush, which is present on many rangeland grazing systems in the Intermountain West. In humans, bitter taste perception is influenced by variation in several TAS2R genes, including more extensively studied TAS2R38 and TAS2R16. We hypothesize that variation in taste receptor genes in sheep is associated with bitter taste. Therefore, the objective of this study was to examine variation in TAS2R genes in relation to consumption of a bitter tasting compound phenylthiocarbamide (PTC) which determines bitter “taster” and “non-taster” status in humans. Rambouillet and Targhee rams (n = 26) were offered various concentrations of PTC solution (0.2–12.29 mM) and water in a side-by-side presentation during two experiments. Blood was collected for DNA isolation and sequencing. Nineteen TAS2R genes were amplified and sequenced with long read Oxford Nanopore MinION technology. A total of 1,049 single nucleotide polymorphisms (SNPs) and 26 haplotypes were identified in these genes. Of these, 24 SNPs and 11 haplotypes were significantly (P < 0.05) associated with PTC consumption in TAS2R3, TAS2R5, TAS2R8, TAS2R9, TAS2R16, TAS2R31-like, TAS2R38, TAS2R39, and TAS2R42-like. Over 50% of the SNPs resulted in a change in amino acid sequence and several resided in potential regulatory regions, which could have downstream functional consequences and influence bitter taste perception in sheep. Further research is needed to validate these associations and elucidate the mechanisms that link variation in TAS2R genes to bitter taste perception in sheep. This may enable producers to select sheep more likely to consume bitter forage such as sagebrush as a flock and rangeland management strategy.

Selection indices are used in genetic improvement programs, with the purpose of selectins simultaneous for several economically important traits. The objective of this study was to construct equations for selection indices in the Blanco-Orejinegro (BON) breed and to determine the index that would generate the greatest genetic progress. The information used included birth weight (BW), body weights adjusted to 120, 240, 480, and 720 days old (W120, W240, W240, 480 and W720, respectively), age at first calving (AFC) and interval between first and second calving (IBC) estimated breeding values. Two Smith and Hazel indices were calculated using variances (I1) and literature (I2), with a part two indices designed using information from experts and breeders (I3 and I4). All the indices favored the reduction of weight at birth. The I1 focused mainly on W120 and I2, I3 and I4 focused on 720. In general, the estimated indices obtained similar reliability and expected genetic differences I1 generated a decrease in direct BW. I2 generated the largest increases in BW and AFC. I3 and I4 generated positive changes in growth and reproductive traits, with I3 generating the greatest genetic gains in the population, especially for W240.

Genomic selection is routinely used worldwide in agricultural breeding. However, in Russia, it is still not used to its full potential partially due to high genotyping costs. The use of genotypes imputed from the low-density chips (LD-chip) provides a valuable opportunity for reducing the genotyping costs. Pork production in Russia is based on the conventional 3-tier pyramid involving 3 breeds; therefore, the best option would be the development of a single LD-chip that could be used for all of them. Here, we for the first time have analyzed genomic variability in 3 breeds of Russian pigs, namely, Landrace, Duroc, and Large White and generated the LD-chip that can be used in pig breeding with the negligible loss in genotyping quality. We have demonstrated that out of the 3 methods commonly used for LD-chip construction, the block method shows the best results. The imputation quality depends strongly on the presence of close ancestors in the reference population. We have demonstrated that for the animals with both parents genotyped using high-density panels high-quality genotypes (allelic discordance rate < 0.05) could be obtained using a 300 single nucleotide polymorphism (SNP) chip, while in the absence of genotyped ancestors at least 2,000 SNP markers are required. We have shown that imputation quality varies between chromosomes, and it is lower near the chromosome ends and drops with the increase in minor allele frequency. Imputation quality of the individual SNPs correlated well across breeds. Using the same LD-chip, we were able to obtain comparable imputation quality in all 3 breeds, so it may be suggested that a single chip could be used for all of them. Our findings also suggest that the presence of markers with extremely low imputation quality is likely to be explained by wrong mapping of the markers to the chromosomal positions.

This study examined the influence of a diet enriched with free methionine (dl-Met) or methionine dipeptide (dl-MMet) on the intestinal health of Eimeria-challenged (EC) and unchallenged (UC) broilers. A non-supplemented, methionine-deficient diet (NS) was used as control. Treatments were arranged in a 2 × 3 factorial completely randomized design with eight replications. Broilers in the EC group were infected with sporulated oocysts of Eimeria spp. (E. acervulina, E. maxima, E. praecox, and E. mitis) at 14 d of age. Performance analysis, light and electron microscopy of the jejunum, analysis of genes related to apoptosis and cell proliferation in the jejunum, and blood tests were performed at 6 days post-inoculation (dpi). EC broilers had poorer performance than UC broilers, regardless of diet (P < 0.001). Broilers fed the dl-Met diet had greater weight gain (P = 0.004) and lower feed conversion ratio (P = 0.019) than broilers fed other diets. Jejunal sections from EC broilers fed the NS diet showed short (P = 0.001) and wide villi (P < 0.001) with increased crypt depth (P < 0.001) and reduced villus / crypt ratio (P = 0.001), jejunal absorptive surface area (P < 0.001), number of neutral goblet cells (Eimeria challenge: P = 0.048; diet P = 0.016), and mucin 2 (MUC2) gene expression (P = 0.018). EC birds fed the dl-MMet diet had higher enterocyte height (P < 0.001). Birds fed the dl-MMet diet had low lamina propria width (P = 0.009). UC broilers fed the dl-Met diet had the highest number of acidic goblet cells (P = 0.005), whereas EC broilers assigned the dl-MMet diet showed the highest number of intraepithelial lymphocytes (P = 0.033). Reduced expression of caspase-3 (CASP3) (P = 0.005), B-cell lymphoma 2 (BCL2) (P < 0.001), mechanistic target of rapamycin (MTOR) (P < 0.001), and ribosomal protein S6 kinase B1 (RPS6KB1) (P < 0.001) genes was observed in EC animals. MTOR expression levels were highest in birds fed the dl-MMet diet (P = 0.004). Plasma activities of aspartate aminotransferase (AST) was influenced by both diet (P = 0.002) and Eimeria challenge (P = 0.005), with EC broilers assigned the NS diet showing the highest levels. EC broilers fed the NS diet had higher creatine kinase (CK) activity (P = 0.049). EC broilers had lower plasma uric acid (P = 0.004) and higher serum mucoproteins level (P < 0.001). These results indicate that methionine dipeptide supplementation is able to mitigate the harmful intestinal effects of Eimeria spp. in broilers.

Combining breeds in a multibreed evaluation can have a negative impact on prediction accuracy, especially if single nucleotide polymorphism (SNP) effects differ among breeds. The aim of this study was to evaluate the use of a multibreed genomic relationship matrix (G), where SNP effects are considered to be unique to each breed, that is, nonshared. This multibreed G was created by treating SNP of different breeds as if they were on nonoverlapping positions on the chromosome, although, in reality, they were not. This simple setup may avoid spurious Identity by state (IBS) relationships between breeds and automatically considers breed-specific allele frequencies. This scenario was contrasted to a regular multibreed evaluation where all SNPs were shared, that is, the same position, and to single-breed evaluations. Different SNP densities (9k and 45k) and different effective population sizes (Ne) were tested. Five breeds mimicking recent beef cattle populations that diverged from the same historical population were simulated using different selection criteria. It was assumed that quantitative trait locus (QTL) effects were the same over all breeds. For the recent population, generations 1–9 had approximately half of the animals genotyped, whereas all animals in generation 10 were genotyped. Generation 10 animals were set for validation; therefore, each breed had a validation group. Analyses were performed using single-step genomic best linear unbiased prediction. Prediction accuracy was calculated as the correlation between true (T) and genomic estimated breeding values (GEBV). Accuracies of GEBV were lower for the larger Ne and low SNP density. All three evaluation scenarios using 45k resulted in similar accuracies, suggesting that the marker density is high enough to account for relationships and linkage disequilibrium with QTL. A shared multibreed evaluation using 9k resulted in a decrease of accuracy of 0.08 for a smaller Ne and 0.12 for a larger Ne. This loss was mostly avoided when markers were treated as nonshared within the same G matrix. A G matrix with nonshared SNP enables multibreed evaluations without considerably changing accuracy, especially with limited information per breed.

Understanding early predictors of sow fertility has the potential to improve genomic predictions. A custom SNP array (SowPro90 produced by Affymetrix) was developed to include genetic variants overlapping quantitative trait loci for age at puberty, one of the earliest indicators of sow fertility, as well as variants related to innate and adaptive immunity. The polymorphisms included in the custom genotyping array were identified using multiple genomic approaches including deep genomic and transcriptomic sequencing and genome-wide associations. Animals from research and commercial populations (n = 2,586) were genotyped for 103,476 SNPs included in SowPro90. To assess the quality of data generated, genotype concordance was evaluated between the SowPro90 and Porcine SNP60 BeadArray using a subset of common SNP (n = 44,708) and animals (n = 277). The mean genotype concordance rate per SNP was 98.4%. Differences in distribution of data quality were observed between the platforms indicating the need for platform specific thresholds for quality parameters. The optimal thresholds for SowPro90 (≥97% SNP and ≥93% sample call rate) were obtained by analyzing the data quality distribution and genotype concordance per SNP across platforms. At ≥97% SNP call rate, there were 42,151 SNPs (94.3%) retained with a mean genotype concordance of 98.6% across platforms. Similarly, ≥94% SNPs and ≥85% sample call rates were established as thresholds for Porcine SNP60 BeadArray. At ≥94% SNPs call rate, there were 41,043 SNPs (91.8%) retained with a mean genotype concordance of 98.6% across platforms. Final evaluation of SowPro90 array content (n = 103,476) at ≥97% SNPs and ≥93% sample call rates allowed retention of 89,040 SNPs (86%) for downstream analysis. The findings and strategy for quality control could be helpful in identifying consistent, high-quality genotypes for genomic evaluations, especially when integrating genotype data from different platforms.

International audience; Improving feed efficiency is of interest to French beef producers so as to increase their profitability. To enable this improvement through selection, genetic correlations with production traits need to be quantified. The objective of this study was to estimate the genetic parameters for growth, feed efficiency (FE), and slaughter performance of young beef bulls of the French Charolais breed. Three feed efficiency criteria were calculated: residual feed intake (RFI), residual gain (RG), and ratio of FE. Data on feed intake, growth, and FE were available for 4,675 Charolais bulls tested in performance test stations and fed with pelleted diet. Between 1985 and 1989, 60 among 510 of these bulls were selected to procreate one generation of 1,477 progeny bulls which received the same pelleted diet at the experimental farm in Bourges. In addition to feed intake, growth, and FE traits, these terminal bulls also had slaughter traits of carcass yield, carcass composition, and weight of visceral organs collected. Genetic parameters were estimated using linear mixed animal models. Between performance test bulls and terminal bulls, the genetic correlation of RFI was 0.80 +/- 0.18; it was 0.70 +/- 0.21 for RG and 0.46 +/- 0.20 for FE. For carcass traits, RFI was negatively correlated with carcass yield (-0.18 +/- 0.14) and muscle content (-0.47 +/- 0.14) and positively with fat content (0.48 +/- 0.13). Conversely, RG and FE were positively correlated with carcass yield and muscle content and negatively with fat content. For the three FE criteria, efficient animals had leaner carcass. For visceral organs (as a proportion of empty body weight), RFI was genetically correlated with the proportions of the 5th quarter (0.51 +/- 0.17), internal fat (0.36 +/- 0.14), abomasum (0.46 +/- 0.20), intestines (0.38 +/- 0.17), liver (0.36 +/- 0.16), and kidneys (0.73 +/- 0.11). Conversely, RG and FE were negatively associated with these traits. The high-energy expenditure associated with the high-protein turnover in visceral organs may explain this opposite relationship between FE and the proportion of visceral organs. Selection for final weight and RFI increased growth and FE in progeny, and also improved carcass yield and muscle content in the carcass. To conclude, determinations of growth and feed intake in performance test stations are effective to select bulls to improve their growth, FE, and muscle content in carcass.

Cow mature weight (MWT) has increased in the past 30 yr. Larger cows cost more to maintain, but their efficiency—and thus profitability—depends on the production environment. Incorporating MWT effectively into selection and mating decisions requires understanding of growth to maturity. The objective of this study was to describe growth to maturity in crossbred beef cattle using Brody, spline, and quadratic functions. Parameter estimates utilized data on crossbred cows from cycle VII and continuous sampling phases of the Germplasm Evaluation Program at the U.S. Meat Animal Research Center. The MWT were estimated at 6 yr from the fitted parameters obtained from the Brody (BMWT), spline (SMWT), and quadratic (QMWT) functions. These were defined as BMWT, SMWT, and QMWT for the Brody, spline, and quadratic functions, respectively. Key parameters from the Brody function were BMWT and maturing constant. The spline was fitted as piecewise linear where the two linear functions joined at a knot. Key parameters were knot position and SMWT. For the quadratic model, the main parameter considered was QMWT. Data were scaled for fitting such that 180 d was the y-intercept with the average weight at 180 d (214.3 kg) subtracted from all weights. Weights were re-expressed by adding 214.3 kg after analysis. Once data were edited, with outliers removed, there were parameter estimates for 5,156, 5,041, and 4,905 cows for the Brody, spline, and quadratic functions, respectively. The average maturing constant (SD) was 0.0023 d(−1) (0.0008 d(−1)). The mean MWT estimates (SD) from the Brody, spline, and quadratic functions were 650.0 kg (64.0 kg), 707.3 kg (79.8 kg), and 597.8 kg (116.7 kg), respectively. The spline function had the highest average R(2) value when fit to individual cows’ data. However, the Brody function produced more consistent MWT estimates regardless of the timeframe of data available and produced the fewest extreme MWT. For the spline and quadratic functions, weights through 4 and 5 yr of age, respectively, were needed before consistent estimates of MWT were obtained. Of the three functions fitted, the Brody was best suited for estimating MWT at a later age in crossbred beef cattle.

This study evaluated the use of molecular breeding values (MBVs) for carcass traits to sort steers into quality grid and lean meat yield (LMY) groups. A discovery set of 2,609 animals with genotypes and carcass phenotypes was used to predict MBVs for LMY and marbling score (MBS) for 299 Angus, 181 Charolais, and 638 Kinsella Composite steers using genomic best linear unbiased prediction. Steers were sorted in silico into four MBV groups namely Quality (with MBVs greater than the mean for LMY and MBS), Lean (with MBVs greater than the mean for LMY but less than or equal to the mean for MBS), Marbling (with MBVs greater than the mean for MBS but less than or equal to the mean for LMY), and Other (with MBVs lower than the mean for LMY and MBS). Carcass phenotypes on the steers were then collected at slaughter and evaluated for consistency with the assigned MBV groups using descriptive statistics and least square analysis. Accuracy of MBV predictions was assessed by Pearson’s correlation between predicted MBV and adjusted phenotype divided by the square root of trait heritability. Genomic breed compositions were predicted for all steers to correct for possible population stratification and breed effects in the test model. The number of steers that met the expected carcass outcome was counted to produce actual percentages for each MBV group. Results showed that on average, Quality and Marbling groups had greater back-fat and more marbling across the three populations while Lean group had leaner carcasses. Carcass weights were similar across MBV groups. Within MBV groups, decreases in variability were observed for most traits suggesting improvement in carcass uniformity. Greater than 70% of the steers in Quality, Lean, and Marbling groups met the Quality Grid and Y1-LMY target for Angus and Charolais but not for Kinsella composite. The accuracy of MBV prediction ranged from 0.43 to 0.59 indicating that up to 35% of the observed carcass trait variability can be predicted, which suggests utility of MBV as a marker-assisted management tool to sort feeder cattle into uniform carcass endpoint groups under similar environmental and management conditions. Further investigation is warranted to evaluate the performance of feeder cattle sorted based on MBV and managed for different carcass endpoints as well as the cost–benefit implications for feedlot operations.

The study objective was to quantify the ability of genetic merit for a generated carcass index to differentiate animals on primal carcass cut weights using data from 1,446 herds on 9,414 heifers and 22,413 steers with weights for 14 different primal carcass cuts (plus 3 generated groups of cuts). The carcass genetic merit index was compromised of carcass weight (positive weight), conformation (positive weight), and fat score (negative weight), each equally weighted within the index. The association analyses were undertaken using linear mixed models; models were run with or without carcass weight as a covariate. In a further series of analyses, carcass weight and carcass fat score were both included as covariates in the models. Whether the association between primal cut yield and carcass weight differed by genetic merit stratum was also investigated. Genetic merit was associated (P < 0.001) with the weight of all cuts evaluated even when adjusted to a common carcass weight (P < 0.01); when simultaneously adjusted to a common carcass weight and fat score, genetic merit was not associated with the weight of the cuberoll or the group cuts termed minced-meat. The weight of the different primal cuts increased almost linearly within increasing genetic merit, with the exception of the rump and bavette. The difference in mean primal cut weight between the very low and very high genetic merit strata, as a proportion of the overall mean weight of that cut in the entire data set, varied from 0.05 (bavette) to 0.28 (eye of round); the average was 0.17. Following adjustment for differences in carcass weight, there was no difference in cut weight between the very low and very high strata for the rump, chuck tender, and mince cut group; the remaining cuts were heavier in the higher index animals with the exception of the cuberoll and bavette, which were lighter in the very high index animals. The association between carcass weight and the weight of each of the evaluated primal cuts differed (P < 0.05) by genetic merit stratum for all cuts evaluated with the exception of the rump, striploin, and brisket as well as the group cuts of frying and mincing. With the exception of these 5 primal (group) cuts, the regression coefficients of primal cut weight on carcass weight increased consistently for all traits with increasing genetic merit stratum, other than for the fillet, cuberoll, bavette, chuck and neck, and heel and shank.

Through domestication and human selection, horses have acquired various coat colors, including seven phenotypes: black, brown, dark bay, bay, chestnut, white, and gray. Here we determined the genotypes for melanocortin-1 receptor (MC1R) and agouti signaling protein (ASIP) in 709 horses from 15 breeds. We found that the E(E)E(E) genotype frequency at MC1R decreased from dark to light colors (black = 64.5%, brown = 67.5%, dark bay = 47.0%, bay = 16.5%, and chestnut = 0.0%), whereas the A(A)A(A) genotype frequency at ASIP increased as coat color lightened (black = 0.0%, brown = 22.9%, dark bay = 69.2%, and bay = 83.0%). When combined genotypes at MC1R and ASIP were examined, different advantage genotype combinations were found for each color: black E(E)E(E)–A(a)A(a) (64.5%), brown E(E)E(E)–A(A)A(a) (47.0%), dark bay E(E)E(E)–A(A)A(A), and E(E)E(e)–A(A)A(A) (36.2% and 33.0%, totally 69.2%), bay E(E)E(e)–A(A)A(A) (69.6%), and chestnut E(e)E(e)–A(A)A(A) (62.6%). The χ(2) test showed that the phenotypes of horse coat colors were significantly related with the genotypes of MC1R and ASIP (p < 0.001). Furthermore, in contrast to a previous study where A(a)A(a) was only found in black, chestnut, and gray horses, we also found this allele in brown, dark bay, bay, and white horses. These results indicated that MC1R and ASIP may synergistically affect the levels of melanin in equine coat colors and that additional genes are likely involved in regulating coat colors, especially for white and gray colors. Our research provides new data for further studies on the synergetic actions of MC1R and ASIP in coat color of horses.

Displacement of abomasum (DA) is one of the most common and important disorders in dairy cattle. The objective of the present study was to detect the quantitative trait loci (QTL) for DA in Chinese Holstein using single-step genomic BLUP methodology. A total of 60,556 producer-recorded DA event records from 32,190 cows, together with 2,336 genotyped animals with 40,054 SNP markers, were used for the analysis. Genomic data were incorporated into a threshold model for variance component estimation, and the estimated heritability of DA was 0.108 (SE = 0.086). Results of genome-wide association studies were reported as the proportion of genetic variance explained 20-SNP windows. Eight QTLs covering 129 genes on Bos taurus autosomes 2, 4, 7, 10, 14, 17, 20 showed associations with DA. Ten genes, namely BMP4, SOCS4, GCH1, DDHD1, ATG14, ACBP/DBI, SMO, AHCYL2, CYP7A1, and CACNA1A, involved in insulin metabolism and lipid metabolism pathways may be considered as candidate genes of DA in dairy. The identified QTLs, biological pathways, and associated genes underlying DA identified from the present study will contribute to the understanding of the genetic architecture of this complex disease.

The inverses of the pedigree and genomic relationship matrices (A, G) are required for single-step GBLUP (ssGBLUP). While, inverting A is possible for millions of animals at a linear cost, inverting G has a cubic cost and feasible for at most 150,000 animals, using the current conventional algorithms. The algorithm for proven and young (APY) provides approximations of the regular ssGBLUP by splitting genotyped animals into core and noncore groups, with computational costs being cubic for core and linear for noncore animals. The data consisted of 9,406,096 animals in the pedigree, 6,243,753 weaning weight phenotypes, and 46,949 genotyped animals from 5 breeds, composites, and animals with missing breed information from New Zealand. Aiming to find a core sample for a multibreed sheep population that can provide evaluations similar to those from the regular ssGBLUP, different core types, and core sizes were studied. Core types random, composite, oldest, youngest, the most inbred animals in G (GINB), and in A (AINB) were studied in 5K, 10K, and 20K core sizes (K = 1,000). Romney core was studied in 5K and 10K, and Coopworth-Perendale core was studied in 5K. Correlation and regression coefficient (slope) between GEBV from the non-APY and the APY analyses, as indicators for consistency with non-APY and bias from non-APY, showed a large impact of APY on noncore and a small impact on nongenotyped animals. Breed-based 5K cores resulted in large bias from non-APY even for nongenotyped animals. Random and GINB at 20K core size resulted in the highest consistency with non-APY and the lowest bias from non-APY. However, GINB did not perform as well as Random at lower core sizes. The number of animals from a breed in the core sample was very important for the evaluation of that breed. We observed that cores without Texel or Highlander animals resulted in poor evaluations for those breeds. Solving the mixed model equations, within core type, the smallest core size, and within core size, Random core converged in the least number of iterations. However, APY per se did not necessarily reduce the solving time. Random cores performed the best, as they could give a good coverage on the generations and breeds, representative for the genotyped population. Core size 20K performed better than 5K and 10K, and the optimum core size was found to be 18.8K, according to the eigenvalue decomposition of G.

The study was designed to investigate the association of Insulin-like growth factor 1 (IGF1) gene polymorphism with the reproductive performance of FUNAAB-Alpha, Sasso, and Kuroiler dual-purpose chicken breeds. To achieve this, a total of 250 healthy hens were selected at 12 wk of age and were intensively managed in cages for 52 wk. Blood sample was taken from each chicken at the 34th week and genomic DNA was extracted using Qiagentm DNA extraction kit, PCR was used to amplify the DNA fragments, and the PCR products were electrophoresed. Amplicons obtained were digested with restriction enzyme hinf1, and were further electrophoresed on 1.5% agarose gel. Data obtained were analyzed using the General linear model of SAS (2002) version 9.0 to determine the effect of IGF1 gene polymorphism and the distribution of alleles within the breeds. Results show polymorphism of the IGF1 gene and the restriction analysis indicated two alleles; A 58% and C 42% with the identification of genotypes AA, AC, and CC, and genotypic frequency of 22%, 43%, and 35%, respectively. Significant associations were observed between the polymorphism of the IGF1 gene, age of the bird at first lay, and weight of the hen at first lay. Chickens with haplotype CC came earlier into lay compared to those with the other two haplotypes (AA and AC). Therefore, the study suggests that haplotype CC could be used as a genetic marker to select for an improved laying performance in chickens.

Understanding the relationships between feed efficiency traits measured in different stages of production is necessary to improve feed efficiency across the beef value chain. The objective of this study was to evaluate relationships among feed efficiency traits measured as growing heifers and breeding females and in their progeny in three full production cycles, and relationships of dam residual feed intake (RFI) with lifetime and lifecycle cow efficiency traits. Data were collected on 160 mixed-breed heifers from 240 d of age to weaning of their third progeny, and postweaning performance of progeny until harvest in experiments initiated in 1953, 1954, 1959, 1964, 1969, and 1974. Individual feed offered was recorded daily, and feed refusals measured every 28 d. Milk yield was measured at 14-d intervals throughout lactation by machine or hand milking. Females and progeny were weighed at 28-d intervals and progeny were harvested at a constant endpoint of live grade or age depending upon the experiment. Feed efficiency traits of RFI and residual BW gain (RG) were computed as the residual from linear regression for developing heifers, dams (RFI and residual energy-corrected milk [RECM]), and postweaning progeny. Feed:gain ratio (FCR) was computed for developing heifers and postweaning progeny, and feed:milk energy ratio (FME) was computed for dams. Various measures of cow efficiency were calculated on either a life cycle or lifetime basis using ratios of progeny and dam weight outputs to progeny and dam feed inputs. Pearson correlations were computed among traits adjusted for a random year-breed-diet group effect. Heifer RFI (0.74) and RG (−0.32) were correlated (P ≤ 0.05) with dam RFI in parity 1 only, but were not correlated (P > 0.05) with dam RECM in any parity. Heifer RFI was correlated (P ≤ 0.05) with progeny RFI (0.17) in parity 3 only. Heifer FCR was not correlated with dam FME or progeny FCR in any parity. Dam RFI was weakly correlated (r = 0.25 to 0.36; P ≤ 0.05) among parities, whereas dam FME and RECM were strongly correlated (r = 0.49 to 0.72; P ≤ 0.05) among parities. Dam RFI in parity 1 and 2 was weakly correlated (r = −0.20 to −0.33; P ≤ 0.05) with cow efficiency ratios that included dam weight as an output, whereas dam RFI in parity 3 was not correlated with any cow efficiency ratio. In conclusion, feed efficiency traits were poorly correlated across production segments, but moderately repeatable across production cycles.

Semen traits are crucial in commercial pig production since semen from boars is widely used in artificial insemination for both purebred and crossbred pig production. Revealing the genetic architecture of semen traits potentially promotes the efficiencies of improving semen traits through artificial selection. This study is aimed to identify candidate genes related to the semen traits in Duroc boars. First, we identified the genes that were significantly associated with three semen traits, including sperm motility (MO), sperm concentration (CON), and semen volume (VOL) in a Duroc boar population through a genome-wide association study (GWAS). Second, we performed a weighted gene co-expression network analysis (WGCNA). A total of 2, 3, and 20 single-nucleotide polymorphisms were found to be significantly associated with MO, CON, and VOL, respectively. Based on the haplotype block analysis, we identified one genetic region associated with MO, which explained 6.15% of the genetic trait variance. ENSSSCG00000018823 located within this region was considered as the candidate gene for regulating MO. Another genetic region explaining 1.95% of CON genetic variance was identified, and, in this region, B9D2, PAFAH1B3, TMEM145, and CIC were detected as the CON-related candidate genes. Two genetic regions that accounted for 2.23% and 2.48% of VOL genetic variance were identified, and, in these two regions, WWC2, CDKN2AIP, ING2, TRAPPC11, STOX2, and PELO were identified as VOL-related candidate genes. WGCNA analysis showed that, among these candidate genes, B9D2, TMEM145, WWC2, CDKN2AIP, TRAPPC11, and PELO were located within the most significant module eigengenes, confirming these candidate genes’ role in regulating semen traits in Duroc boars. The identification of these candidate genes can help to better understand the genetic architecture of semen traits in boars. Our findings can be applied for semen traits improvement in Duroc boars.

Validation of beef total merit breeding indexes for improving performance and profitability has previously been undertaken at the individual animal level; however, no herd-level validation of beef genetic merit and profit has been previously investigated. The objective of the present study was to quantify the relationship between herd profitability and both herd-average terminal and maternal genetic merit across 1,311 commercial Irish beef herds. Herd-level physical and financial performance data were available from a financial benchmarking tool used by Irish farmers and their extension advisors. Animal genetic merit data originated from the Irish Cattle Breeding Federation who undertake the national beef and dairy genetic evaluations. Herd-average genetic merit variables included the terminal index of young animals, the maternal index of dams, and the terminal index of service sires. The herds represented three production systems: 1) cow-calf to beef, 2) cow-calf to weanling/yearling, and 3) weanling/yearling to beef. Associations between herd financial performance metrics and herd average genetic merit variables were quantified using a series of linear mixed models with year, production system, herd size, stocking rate, concentrate input, and the two-way interactions between production system and herd size, stocking rate, and concentrate input included as nuisance factors. Herd nested within the county of Ireland (n = 26) was included as a repeated effect. Herds with young cattle excelling in terminal index enjoyed greater gross and net profit per hectare (ha), per livestock unit (LU), and per kg net live-weight output. The change in gross profit per LU per unit change in the terminal index of young animals was €1.41 (SE = 0.23), while the respective regression coefficient for net profit per LU was €1.37 (SE = 0.30); the standard deviation of the terminal index is €37. Herd-average dam maternal index and sire terminal index were both independently positively associated with gross profit per ha and gross profit per LU. Each one unit increase in dam maternal index (standard deviation of €38) was associated with a €1.40 (SE = 0.48) and €0.76 (SE = 0.29) greater gross profit per ha and per LU, respectively. Results from the present study at the herd-level concur with previous validation studies at the individual animal level thus instilling further confidence among stakeholders as to the expected improvement in herd profitability with improving genetic merit.

Feed efficiency (FE) is the amount of body weight gain for a given feed intake. Improving FE through selective breeding is key for sustainable finfish aquaculture but its evaluation at individual level is technically challenging. We therefore investigated whether individual routine metabolic rate (RMR) was a predictor of individual FE in the European sea bass Dicentrarchus labrax, a major species in European mariculture. The European sea bass has three genetically distinct populations across its geographical range, namely Atlantic (AT), West Mediterranean (WM), and East Mediterranean (EM). We compared FE and RMR of fish from these three populations at 18 or 24 °C. We held 200 fish (62 AT, 66 WM, and 72 EM) in individual aquaria and fed them from ad libitum down to fasting. FI was assessed for an ad libitum feeding rate and for a fixed restricted ration (1% of metabolic body weight·day−1, with metabolic body weight = body weight0.8). After being refed 12 wk in a common tank, individual RMR was measured over 36 h by intermittent flow respirometry. There was a significant effect of temperature whereby fish at 18 °C had greater mean FE (P < 0.05) and lower RMR (P < 0.001). There was also a significant effect of population, where AT fish had lower FE (P < 0.05) and greater RMR (P < 0.001) than WM and EM, at both temperatures. Despite these differences in temperature and population means, individual FE and RMR were not significantly correlated (P > 0.05). Therefore, although the results provide evidence of an association between metabolic rate and FE, RMR was not a predictor of individual FE, for reasons that require further investigation.

Polymorphisms of milk protein genes have been proposed as candidate markers for dairy production traits in cattle. In the present study, a polymorphism was detected in the 5′-flanking (promoter) region of the bovine alpha-lactalbumin (LALBA) gene, a T/C transition located at nucleotide −1,001 relative to the transcription start site g.-1001T > C (NC_037332.1:g.31183170T > C), which is recognizable with PstI restriction endonuclease. In silico analyses showed that this mutation created novel retinoid X receptor alpha and vitamin D receptor transcription factor binding sites. Real-time PCR found that cows with different genetic variants of the promoter demonstrated different levels of expression of LALBA mRNA in milk somatic cells (MSCs). The TT genotype cows demonstrated low expression, whereas those with CT demonstrated much higher expression (P < 0.05). ELISA analysis found milk LALBA protein levels also differed between the TT and CT cows (P < 0.05) and that these levels were not correlated with the mRNA abundance in MSC. Association analysis found that the g.-1001T > C polymorphism in the promoter region of the LALBA gene influenced milk production traits in Polish Holstein-Friesian cows. High daily milk yield and dry matter yield, and high lactose yield and concentration were associated with the TT genotype. The TT genotype cows also had a lower number of somatic cells in the milk, considered as an indicator of udder health status. Therefore, the TT genotype could be more desirable from the breeder’s perspective.

Awareness and interest in calf health and wellbeing is intensifying, prompting change in the management and breeding decisions of producers and associated policy-makers. The objectives of the present study were to 1) quantify the risk factors associated with subjectively measured scores of vigor and birth size as well as diagnoses of scour and pneumonia in a large national dataset of beef calves, and 2) to estimate the contribution of genetic variance to such phenotypic measures. After edits, the data consisted of health and birth size data subjectively scored by producers on 88,207 calves born in 6,126 Irish beef herds. Vigor was recorded on a scale of 1 (very poor) to 5 (very good). Birth size was also scored on a scale of 1 (very small) to 5 (very large). Scour and pneumonia were both scored independently based on the suspected number of occurrence of each (0 = no occurrence, 1 = one occurrence, or 2 = more than one occurrence). On average, 14.7% of calves were recorded as having had at least one occurrence of scour within the first 5 mo of life, whereas 6.4% of calves were recorded as having had at least one occurrence of pneumonia within the first 5 mo of life. Relative to female calves, male calves had a worse vigor score and a suspected greater incidence of both scour and pneumonia. Relative to singletons, twins were, on average, smaller at birth, they had a worse vigor score, and they were more prone to scour. Calves born in the later periods of the calving season (i.e., late and very late) had a greater incidence of scour relative to calves in the herd born earlier in the calving season. Heritability estimates for vigor, birth size, and pneumonia were 0.12 (0.02), 0.33 (0.03), and 0.08 (0.02), respectively; no genetic variance was detected for scour. Breeding for vigorous calves that are less susceptible to pneumonia could provide producers with an additional strategy to ensure consumer concerns regarding food quality, safety, and calf wellbeing are being met.

Disease resilience refers to the productivity of an animal under disease. Given the high biosecurity of pig nucleus herds, traits that can be measured on healthy pigs and that are genetically correlated with disease resilience, that is, genetic indicator traits, offer a strategy to select for disease resilience. Our objective was to evaluate mitogen stimulation assays (MSAs) on peripheral blood mononuclear cells (PBMCs) from young healthy pigs as genetic indicators for disease resilience. Data were from a natural disease challenge in which batches of 60 or 75 naïve Yorkshire × Landrace piglets were introduced every 3 wk into a continuous flow barn that was seeded with multiple diseases. In this environment, disease resilience traits, including growth, treatment, and mortality rates, were recorded on 3,136 pigs that were genotyped with a high-density marker panel. PBMCs from 882 of these pigs from 19 batches were isolated from whole blood collected prior to the disease challenge and stimulated with five mitogens: concanavalin A (ConA), phytohemagglutinin (PHA), pokeweed mitogen (PWM), lipopolysaccharide (LPS), and phorbol myristate acetate (PMA). The proliferation of cells was evaluated at 48, 72, and 96 h and compared with unstimulated samples (rest count). Heritabilities of cell proliferation were estimated using a model with batch as a fixed effect and covariates of entry age; rest count; complete blood count proportions of lymphocytes, monocytes, eosinophils, and basophils; and pen, litter, and animal genetics as random effects. Heritability estimates were highest for response to ConA (0.30 ± 0.09, 0.28 ± 0.10, 0.17 ± 0.10, and 0.25 ±0.10 at 48, 72, and 96 h after stimulation and for area under the curve across the three time points, respectively). Estimates were in a similar range for response to PHA and PMA but low for PWM and LPS. Responses to ConA, PHA, and PMA were moderately genetically correlated with several disease resilience traits and in the expected direction, but individual estimates were not significantly different from zero due to large SEs. In conclusion, although validation is needed, MSAss, in particular based on ConA, show promise as genetic indicator traits for disease resilience.

India is considered as the home tract of some of the best buffalo breeds. However, the genetic structure of the Indian river buffalo is poorly understood. Hence, there is a need to characterize the populations and understand the genetic structure of various buffalo breeds for selection and to design breeding strategies. In this study, we have analyzed genetic variability and population structure of seven buffalo breeds from their respective geographical regions using Axiom Buffalo Genotyping Array. Diversity, as measured by expected heterozygosity, ranged from 0.364 in Surti to 0.384 in Murrah breed, and pair-wise FST values revealed the lowest genetic distance between Murrah and Nili-Ravi (0.0022), while the highest between Surti and Pandharpuri (0.030). Principal component analysis and structure analysis unveiled the differentiation of Surti, Pandharpuri, and Jaffarabadi in first two principal components and at K = 4, respectively, while remaining breeds were grouped together as a separate single cluster and admixed. Murrah and Mehsana showed early linkage disequilibrium (LD) decay, while Surti breed showed late decay. In LD blocks to quantitative trait locis (QTLs) concordance analysis, 4.65% of concordance was observed with 873 LD blocks overlapped with 2,330 QTLs. Overall, total 4,090 markers were identified from all LD blocks for six types of traits. Results of this study indicated that these single-nucleotide polymorphism (SNP) markers could differentiate phenotypically distinct breeds like Surti, Pandharpuri, and Jaffarabadi but not others. So, there is a need to develop SNP chip based on SNP markers identified by sequence information of local breeds.

A major obstacle in applying genomic selection (GS) to uniquely adapted local breeds in less-developed countries has been the cost of genotyping at high densities of single-nucleotide polymorphisms (SNP). Cost reduction can be achieved by imputing genotypes from lower to higher densities. Locally adapted breeds tend to be admixed and exhibit a high degree of genomic heterogeneity thus necessitating the optimization of SNP selection for downstream imputation. The aim of this study was to quantify the achievable imputation accuracy for a sample of 1,135 South African (SA) Drakensberger cattle using several custom-derived lower-density panels varying in both SNP density and how the SNP were selected. From a pool of 120,608 genotyped SNP, subsets of SNP were chosen (1) at random, (2) with even genomic dispersion, (3) by maximizing the mean minor allele frequency (MAF), (4) using a combined score of MAF and linkage disequilibrium (LD), (5) using a partitioning-around-medoids (PAM) algorithm, and finally (6) using a hierarchical LD-based clustering algorithm. Imputation accuracy to higher density improved as SNP density increased; animal-wise imputation accuracy defined as the within-animal correlation between the imputed and actual alleles ranged from 0.625 to 0.990 when 2,500 randomly selected SNP were chosen vs. a range of 0.918 to 0.999 when 50,000 randomly selected SNP were used. At a panel density of 10,000 SNP, the mean (standard deviation) animal-wise allele concordance rate was 0.976 (0.018) vs. 0.982 (0.014) when the worst (i.e., random) as opposed to the best (i.e., combination of MAF and LD) SNP selection strategy was employed. A difference of 0.071 units was observed between the mean correlation-based accuracy of imputed SNP categorized as low (0.01 < MAF ≤ 0.1) vs. high MAF (0.4 < MAF ≤ 0.5). Greater mean imputation accuracy was achieved for SNP located on autosomal extremes when these regions were populated with more SNP. The presented results suggested that genotype imputation can be a practical cost-saving strategy for indigenous breeds such as the SA Drakensberger. Based on the results, a genotyping panel consisting of ~10,000 SNP selected based on a combination of MAF and LD would suffice in achieving a

The objective of this study was to evaluate the effects of various data structures on the genetic evaluation for the binary phenotype of reproductive success. The data were simulated based on an existing pedigree and an underlying fertility phenotype with a heritability of 0.10. A data set of complete observations was generated for all cows. This data set was then modified mimicking the culling of cows when they first failed to reproduce, cows having a missing observation at either their second or fifth opportunity to reproduce as if they had been selected as donors for embryo transfer, and censoring records following the sixth opportunity to reproduce as in a cull-for-age strategy. The data were analyzed using a third-order polynomial random regression model. The EBV of interest for each animal was the sum of the age-specific EBV over the first 10 observations (reproductive success at ages 2–11). Thus, the EBV might be interpreted as the genetic expectation of number of calves produced when a female is given 10 opportunities to calve. Culling open cows resulted in the EBV for 3-yr-old cows being reduced from 8.27 ± 0.03 when open cows were retained to 7.60 ± 0.02 when they were culled. The magnitude of this effect decreased as cows grew older when they first failed to reproduce and were subsequently culled. Cows that did not fail over the 11 yr of simulated data had an EBV of 9.43 ± 0.01 and 9.35 ± 0.01 based on analyses of the complete data and the data in which cows that failed to reproduce were culled, respectively. Cows that had a missing observation for their second record had a significantly reduced EBV, but the corresponding effect at the fifth record was negligible. The current study illustrates that culling and management decisions, and particularly those that affect the beginning of the trajectory of sustained reproductive success, can influence both the magnitude and accuracy of resulting EBV.

Genomic selection (GS) is now practiced successfully across many species. However, many questions remain, such as long-term effects, estimations of genomic parameters, robustness of genome-wide association study (GWAS) with small and large datasets, and stability of genomic predictions. This study summarizes presentations from the authors at the 2020 American Society of Animal Science (ASAS) symposium. The focus of many studies until now is on linkage disequilibrium between two loci. Ignoring higher-level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, the selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make the computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWASs using small genomic datasets frequently find many marker-trait associations, whereas studies using much bigger datasets find only a few. Most of the current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit the computation of P-values from genomic best linear unbiased prediction (GBLUP), where models can be arbitrarily complex but restricted to genotyped animals only, and single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top-ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as 1 SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. Although many issues in GS have been solved, many new issues that require additional research continue to surface.

Rates of gain and feed efficiency are important traits in most breeding programs for growing farm animals. The rate of gain (GAIN) is usually expressed over a certain age period and feed efficiency is often expressed as residual feed intake (RFI), defined as observed feed intake (FI) minus expected feed intake based on live weight (WGT) and GAIN. However, the basic traits recorded are always WGT and FI and other traits are derived from these basic records. The aim of this study was to develop a procedure for simultaneous analysis of the basic records and then derive linear traits related to feed efficiency without retorting to any approximation. A bivariate longitudinal random regression model was employed on 13,791 individual longitudinal records of WGT and FI from 2,827 bulls of six different beef breeds tested for their own performance in the period from 7 to 13 mo of age. Genetic and permanent environmental covariance functions for curves of WGT and FI were estimated using Gibbs sampling. Genetic and permanent covariance functions for curves of GAIN were estimated from the first derivative of the function for WGT and finally the covariance functions were extended to curves for RFI, based on the conditional distribution of FI given WGT and GAIN. Furthermore, the covariance functions were extended to include GAIN and RFI defined over different periods of the performance test. These periods included the whole test period as normally used when predicting breeding values for GAIN and RFI for beef bulls. Based on the presented method, breeding values and genetic parameters for derived traits such as GAIN and RFI defined longitudinally or integrated over (parts of) of the test period can be obtained from a joint analysis of the basic records. The resulting covariance functions for WGT, FI, GAIN, and RFI are usually singular but the method presented here does not suffer from the estimation problems associated with defining these traits individually before the genetic analysis. All the results are thus estimated simultaneously, and the set of parameters is consistent.