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Across the majority livestock species, routinely collected genomic and pedigree information has been incorporated into evaluations using single-step methods. As a result, strategies that reduce genotyping costs without reducing the response to selection are important as they could have substantial economic impacts on breeding programs. Therefore, the objective of the current study was to investigate the impact of selectively genotyping selection candidates on the selection response using simulation. Populations were simulated to mimic the genome and population structure of a swine and cattle population undergoing selection on an index comprised of the estimated breeding values (EBV) for 2 genetically correlated quantitative traits. Ten generations were generated and genotyping began generation 7. Two phenotyping scenarios were simulated that assumed the first trait was recorded early in life on all individuals and the second trait was recorded on all versus a random subset of the individuals. The EBV were generated from a bivariate animal model. Multiple genotyping scenarios were generated that ranged from not genotyping any selection candidates, a proportion of the selection candidates based on either their index value or chosen at random, and genotyping all selection candidates. An interim index value was utilized to decide who to genotype for the selective genotype strategy. The interim value assumed only the first trait was observed and the only genotypic information available was on animals in previous generations. Within each genotyping scenario 25 replicates were generated. Within each genotyping scenario the mean response per generation and the degree to which EBV were inflated/deflated was calculated. Across both species and phenotyping strategies, the plateau of diminishing returns was observed when 60% of the selection candidates with the largest index values were genotyped. When randomly genotyping selection candidates, either 80 or 100% of the selection

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.

Ultrasonography was used to capture a coronal–sagittal image of the veins of the pampiniform plexus (PP) and the testicular artery of 327 maternal‐line boars at approximately 6 months of age at the University of Nebraska—Lincoln. Varicocoele was diagnosed by two methods. Method 1 diagnosed varicocoele when the average vessel area on one side of the scrotum was 1.5 times larger than the average vessel area on the other side of the scrotum. Method 2 diagnosed varicocoele when the average vessel area on one side of the scrotum of a boar was 1.5 times larger than the average vessel on the same side of the scrotum of the boar’s cohorts (same population and year). Varicocoele was diagnosed in 23.17% and 15.1% of boars measured using method 1 and method 2, respectively. Ultrasonography showed to be an effective means to measure PP vessel size in boars and may even allow for earlier detection of varicocoele than by using palpation. Animal models were employed to estimate the heritability for: average area of right PP vessels (0.52), average area of the left PP vessels (0.46), varicocoele presence using method 1 (0.26) and varicocoele presence using method 2 (0.25). These heritability estimates suggest that vessel size and varicocoele could be selected against in breeding programmes to potentially improve boar semen quality.

Simulated and swine industry data sets were utilized to assess the impact of removing older data on the predictive ability of selection candidate estimated breeding values (EBV) when using single-step genomic best linear unbiased prediction (ssGBLUP). Simulated data included thirty replicates designed to mimic the structure of swine data sets. For the simulated data, varying amounts of data were truncated based on the number of ancestral generations back from the selection candidates. The swine data sets consisted of phenotypic and genotypic records for three traits across two breeds on animals born from 2003 to 2017. Phenotypes and genotypes were iteratively removed 1 year at a time based on the year an animal was born. For the swine data sets, correlations between corrected phenotypes (Cp) and EBV were used to evaluate the predictive ability on young animals born in 2016–2017. In the simulated data set, keeping data two generations back or greater resulted in no statistical difference (p-value > 0.05) in the reduction in the true breeding value at generation 15 compared to utilizing all available data. Across swine data sets, removing phenotypes from animals born prior to 2011 resulted in a negligible or a slight numerical increase in the correlation between Cp and EBV. Truncating data is a method to alleviate computational issues without negatively impacting the predictive ability of selection candidate EBV. Includes Supplemental Table S1.

Feed costs comprise the majority of variable expenses in beef cattle systems making feed efficiency an important economic consideration within the beef industry. Due to the expense of recording individual feed-intake phenotypes, a genomic-enabled approach could be advantageous toward improving this economically relevant trait complex. A genome-wide association study (GWAS) was performed using 748 crossbred steers and heifers representing seven sire breeds with phenotypes for ADG and ADFI. Animals were genotyped with the BovineSNP50v2 BeadChip containing approximately 54,000 SNP. Both traits were analyzed using univariate SNPbased (BayesC) and haplotype-based (BayesIM) models and jointly using BayesIM to perform a bivariate GWAS. For BayesIM, a hidden Markov model (HMM) of haplotype segments of variable length was built where haplotypes were mapped to clusters based on local similarity. The estimated HMM was then used to assign haplotype cluster genotypes, instead of SNP genotypes, as latent covariates in a Bayesian mixture model. The number of haplotype clusters at each location was assumed to be either 8 (BayesIM8) or 16 (BayesIM16). A total of three univariate analyses for each trait and two bivariate analyses were performed. Posterior SD (PSD) for ADG were 0.28 (0.08), 0.37 (0.11), 0.37 (0.11), 0.35 (0.11), and 0.35 (0.12) for BayesC, BayesIM8, BayesIM16, BayesIM8 bivariate, and BayesIM16 bivariate, respectively. ADFI PSD were 0.30 (0.07), 0.44 (0.13), 0.42 (0.12), 0.38 (0.10), and 0.38 (0.10) for the same models. The top 1% of 1-Mb windows that explained the largest fraction of genetic variation in common between univariate SNP and haplotype models ranged from 24% to 40% and from 20% to 32% for ADG and ADFI, respectively. Spearmen rank correlations between molecular breeding values from SNP and haplotype-based models in the training data were similar for both traits (>0.96) suggesting that either model would lead to similar rankings of animals, although resoluti

Across the majority livestock species, routinely collected genomic and pedigree information has been incorporated into evaluations using single-step methods. As a result, strategies that reduce genotyping costs without reducing the response to selection are important as they could have substantial economic impacts on breeding programs. Therefore, the objective of the current study was to investigate the impact of selectively genotyping selection candidates on the selection response using simulation. Populations were simulated to mimic the genome and population structure of a swine and cattle population undergoing selection on an index comprised of the estimated breeding values (EBV) for 2 genetically correlated quantitative traits. Ten generations were generated and genotyping began generation 7. Two phenotyping scenarios were simulated that assumed the first trait was recorded early in life on all individuals and the second trait was recorded on all versus a random subset of the individuals. The EBV were generated from a bivariate animal model. Multiple genotyping scenarios were generated that ranged from not genotyping any selection candidates, a proportion of the selection candidates based on either their index value or chosen at random, and genotyping all selection candidates. An interim index value was utilized to decide who to genotype for the selective genotype strategy. The interim value assumed only the first trait was observed and the only genotypic information available was on animals in previous generations. Within each genotyping scenario 25 replicates were generated. Within each genotyping scenario the mean response per generation and the degree to which EBV were inflated/deflated was calculated. Across both species and phenotyping strategies, the plateau of diminishing returns was observed when 60% of the selection candidates with the largest index values were genotyped. When randomly genotyping selection candidates, either 80 or 100% of the selection

SNP chips are commonly used for genotyping animals in genomic selection but strategies for selecting low-density (LD) SNPs for imputation-mediated genomic selection have not been addressed adequately. The main purpose of the present study was to compare the performance of eight LD (6K) SNP panels, each selected by a different strategy exploiting a combination of three major factors: evenly-spaced SNPs, increased minor allele frequencies, and SNP-trait associations either for single traits independently or for all the three traits jointly. The imputation accuracies from 6K to 80K SNP genotypes were between 96.2 and 98.2%. Genomic prediction accuracies obtained using imputed 80K genotypes were between 0.817 and 0.821 for daughter pregnancy rate, between 0.838 and 0.844 for fat yield, and between 0.850 and 0.863 for milk yield. The two SNP panels optimized on the three major factors had the highest genomic prediction accuracy (0.821–0.863), and these accuracies were very close to those obtained using observed 80K genotypes (0.825–0.868). Further exploration of the underlying relationships showed that genomic prediction accuracies did not respond linearly to imputation accuracies, but were significantly affected by genotype (imputation) errors of SNPs in association with the traits to be predicted. SNPs optimal for map coverage and MAF were favorable for obtaining accurate imputation of genotypes whereas trait-associated SNPs improved genomic prediction accuracies. Thus, optimal LD SNP panels were the ones that combined both strengths. The present results have practical implications on the design of LD SNP chips for imputation-enabled genomic prediction.

Genomic selection (GS) has become an integral part of genetic evaluation methodology and has been applied to all major livestock species, including beef and dairy cattle, pigs, and chickens. Significant contributions in increased accuracy of selection decisions have been clearly illustrated in dairy cattle after practical application of GS. In the majority of U.S. beef cattle breeds, similar efforts have also been made to increase the accuracy of genetic merit estimates through the inclusion of genomic information into routine genetic evaluations using a variety of methods. However, prediction accuracies can vary relative to panel density, the number of folds used for folds cross-validation, and the choice of dependent variables (e.g., EBV, deregressed EBV, adjusted phenotypes). The aim of this study was to evaluate the accuracy of genomic predictors for Red Angus beef cattle with different strategies used in training and evaluation. The reference population consisted of 9,776 Red Angus animals whose genotypes were imputed to 2 medium-density panels consisting of over 50,000 (50K) and approximately 80,000 (80K) SNP. Using the imputed panels, we determined the influence of marker density, exclusion (deregressed EPD adjusting for parental information [DEPD-PA]) or inclusion (deregressed EPD without adjusting for parental information [DEPD]) of parental information in the deregressed EPD used as the dependent variable, and the number of clusters used to partition training animals (3, 5, or 10). A BayesC model with π set to 0.99 was used to predict molecular breeding values (MBV) for 13 traits for which EPD existed. The prediction accuracies were measured as genetic correlations between MBV and weighted deregressed EPD. The average accuracies across all traits were 0.540 and 0.552 when using the 50K and 80K SNP panels, respectively, and 0.538, 0.541, and 0.561 when using 3, 5, and 10 folds, respectively, for cross-validation. Using DEPD-PA as the response variable resul

Approximately 50% of sows are culled annually with more than one-third due to poor fertility. Our research demonstrated that age at puberty is an early pre-breeding indicator of reproductive longevity. Age at puberty can be measured early in life, has a moderate heritability, and is negatively correlated with lifetime number of parities. Detection of age at puberty is tedious and time consuming and is therefore not collected by the industry, which limits genetic progress. Genomic prediction is a viable approach to preselect gilts that will express puberty early and have superior reproductive longevity. The hypothesis that genetic variants explaining differences in age at puberty also explain differences in sow reproductive longevity was tested. Phenotypes, genotypes, and tissues from the UNL resource population (n > 1700) were used in genome-wide association analyses, genome, and RNA sequencing to uncover functional polymorphisms that could explain variation in puberty and reproductive longevity. A BeadArray including 56,424 SNP explained 25.2% of the phenotypic variation in age at puberty in a training set (n = 820). Evaluation of major windows and SNPs of subsequent batches of similar genetics (n = 412) showed that if all SNPs located in the major 1-Mb windows were tested, they explained a substantial amount of phenotypic variation (12.3 to 36.8%). Due to differences in linkage disequilibrium status, the most informative SNP from these windows explained a lower proportion of the variation (6.5 to 23.7%). To improve genomic predictive ability, the limited capability of BeadArray was enhanced by potential functional variants uncovered by genome sequencing of selected sires (n = 20; >20X). There were 11.2 mil. SNPs and 2.9 mil. indels discovered across sires and reference genomes. The role of gene expression differences in explaining phenotypic variation in age at puberty was investigated by RNA sequencing of the hypothalamic arcuate nucleus (ARC) in gilts (n = 37) w

Genetic variants associated with traits such as age at puberty and litter size could provide insight into the underlying genetic sources of variation impacting sow reproductive longevity and productivity. Genomewide characterization and gene expression profiling were used using gilts from the University of Nebraska–Lincoln swine resource population (n = 1,644) to identify genetic variants associated with age at puberty and litter size traits. From all reproductive traits studied, the largest fraction of phenotypic variation explained by the Porcine SNP60 BeadArray was for age at puberty (27.3%). In an evaluation data set, the predictive ability of all SNP from highranked 1-Mb windows (1 to 50%), based on genetic variance explained in training, was greater (12.3 to 36.8%) compared with the most informative SNP from these windows (6.5 to 23.7%). In the integrated data set (n = 1,644), the top 1% of the 1-Mb windows explained 6.7% of the genetic variation of age at puberty. One of the high-ranked windows detected (SSC2, 12–12.9 Mb) showed pleiotropic features, affecting both age at puberty and litter size traits. The RNA sequencing of the hypothalami arcuate nucleus uncovered 17 differentially expressed genes (adjusted P < 0.05) between gilts that became pubertal early (180 d of age). Twelve of the differentially expressed genes are upregulated in the late pubertal gilts. One of these genes is involved in energy homeostasis (FFAR2), a function in which the arcuate nucleus plays an important contribution, linking nutrition with reproductive development. Energy restriction during the gilt development period delayed age at puberty by 7 d but increased the probability of a sow to produce up to 3 parities (P < 0.05). Identification of pleotropic functional polymorphisms may improve accuracy of genomic prediction while facilitating a reduction in sow replacement rates and addressing welfare concerns.

The western corn rootworm, Diabrotica virgifera virgifera, is an insect pest of corn and population suppression with chemical insecticides is an important management tool. Traits conferring organophosphate insecticide resistance have increased in frequency amongst D. v. virgifera populations, resulting in the reduced efficacy in many corn-growing regions of the USA. We used comparative functional genomic and quantitative trait locus (QTL) mapping approaches to investigate the genetic basis of D. v. virgifera resistance to the organophosphate methyl-parathion. RNA from adult methyl-parathion resistant and susceptible adults was hybridized to 8331 microarray probes. The results predicted that 11 transcripts were significantly up-regulated in resistant phenotypes, with the most significant (fold increases >/= 2.43) being an a-esterase-like transcript. Differential expression was validated only for the aesterase (ST020027A20C03), with 11- to 13-fold greater expression in methyl-parathion resistant adults (P

Eighteen breeds were utilized to estimate genetic parameters for birth weight and calving difficulty on first-parity females. Birth weight and calving difficulty were moderately heritable allowing for genetic selection to decrease calving difficulty. Genetic correlation estimates were positive between direct effects for birth weight and calving difficulty. This work will serve as the foundation for estimating across-breed EPD for calving difficulty in the U.S.

Heterosis, assumed proportional to expected breed heterozygosity, was calculated for 6834 individuals with birth, weaning and yearling weight records from Cycle VII and advanced generations of the U.S. Meat Animal Research Center (USMARC) Germplasm Evaluation (GPE) project. Breeds represented in these data included: Angus, Hereford, Red Angus, Charolais, Gelbvieh, Simmental, Limousin and Composite MARC III. Heterosis was further estimated by proportions of British × British (B × B), British × Continental (B × C) and Continental × Continental (C × C) crosses and by breed-specific combinations. Model 1 fitted fixed covariates for heterosis within biological types while Model 2 fitted random breed-specific combinations nested within the fixed biological type covariates. Direct heritability estimates (SE) for birth, weaning ,and yearling weight for Model 1 were 0.42 (0.04), 0.22 (0.03), and 0.39 (0.05), respectively. The direct heritability estimates (SE) of birth, weaning, and yearling weight for Model 2 were the same as Model 1, except yearling weight heritability was 0.38 (0.05). The B × B, B × C, and C × C heterosis estimates for birth weight were 0.47 (0.37), 0.75 (0.32), and 0.73 (0.54) kg, respectively. The B × B, B × C, and C × C heterosis estimates for weaning weight were 6.43 (1.80), 8.65 (1.54), and 5.86 (2.57) kg, respectively. Yearling weight estimates for B × B, B × C, and C × C heterosis were 17.59(3.06), 13.88 (2.63), and 9.12 (4.34) kg, respectively. Differences did exist among estimates of breed-specific heterosis for weaning and yearling weight, although the variance component associated with breed-specific heterosis was not significant. These results illustrate that there are differences in breed-specific heterosis and exploiting these differences can lead to varying levels of heterosis among mating plans.

Genomic information could be used efficiently to improve traits that are expensive to measure, sex limited or expressed late in life. This study analyzed the phenotypic variation explained by major SNPs and windows for age at puberty in gilts, an indicator of reproductive longevity. A genome-wide association study using 56,424 SNPs explained 25.2% of the phenotypic variation in age at puberty in a training set (n = 820). All SNPs from the top 10% of 1-Mb windows explained 33.5% of the phenotypic variance compared to 47.1% explained by the most informative markers (n = 261). In an evaluation population, consisting of subsequent batches (n = 412), the predictive ability of all SNPs from the major 1-Mb windows was higher compared to the variance captured by the most informative SNP from each of these windows. The phenotypic variance explained in the evaluation population varied from 12.3% to 36.8% when all SNPs from major windows were used compared to 6.5–23.7% explained by most informative SNPs. The correlation between phenotype and genomic prediction values based on SNP effects estimated in the training population was marginal compared to their effects retrained in the evaluation population for all (0.46–0.81) or most informative SNPs (0.30–0.65) from major windows. An increase in genetic gain of 20.5% could be obtained if genomic selection included both sexes compared to females alone. The pleiotropic role of major genes such as AVPR1A could be exploited in selection of both age at puberty and reproductive longevity. Two supplemental files are attached (below).

Background: Porcine circovirus 2 is the primary agent responsible for inducing a group of associated diseases known as Porcine Circovirus Associated Diseases (PCVAD), which can have detrimental effects on production efficiency as well as causing significant mortality. The objective of this study was to evaluate variation in viral replication, immune response and growth across pigs (n = 974) from different crossbred lines. The approach used in this study was experimental infection with a PCV2b strain of pigs at an average of 43 days of age. Results: The sequence of the PCV2b isolate used in the challenge was similar with a cluster of PCV2b isolates known to induce PCVAD and increased mortality rates. The swine leukocyte antigen class II (SLAII) profile of the population was diverse, with nine DQB1 haplotypes being present. Individual viremia and antibody profiles during challenge demonstrate variation in magnitude and time of viral surge and immune response. The correlations between PCV2 specific antibodies and average daily gain (ADG) were relatively low and varied between - 0.14 to 0.08 for IgM and −0.02 and 0.11 for IgG. In contrast, PCV2 viremia was an important driver of ADG decline following infection; a moderate negative correlation was observed between viral load and overall ADG (r = − 0.35, P < 0.001). The pigs with the lowest 10% level of viral load maintained a steady increase in weekly ADG (P < 0.0001) compared to the pigs that had the 10% greatest viral load (P < 0.55). In addition, the highly viremic group expressed higher IgM and IgG starting with d 14 and d 21 respectively, and higher tumor necrosis factor – alpha (TNF-α) at d 21 (P < 0.005), compared to low viremic group. Conclusions: Molecular sources of the observed differences in viremia and immune response could provide a better understanding of the host factors that influence the development of PCVAD and lead to improved knowledge of swine immunity.

Seven research trials conducted over five years at the University of Nebraska–Lincoln were summarized to determine how animal performance changes through the finishing period on a live and carcass weight basis. Live weight, carcass weight, carcass ADG, and carcass feed efficiency all changed quadratically throughout the feeding period; live ADG and live feed efficiency declined linearly. During times of negative profit margins, optimal profitability for steers marketed on a live-basis occurred by selling early, whereas optimal profitability was achieved by feeding steers marketed on a carcass-basis longer.

Purebred Red Angus genotypes, via the Ilumina BovineSNP50 assay, and expected progeny differences (EPD) were used to evaluate the accuracy of genomic predictors for traits that are currently reportedthrough the American Red AngusAssociations’ National Cattle Evaluation. Two genomic predictors were evaluated, one derived using prediction equations from the National Beef Cattle Evaluation Consortium and the other from Zoetis.

Background: The identification of genetic markers associated with complex traits that are expensive to record such as feed intake or feed efficiency would allow these traits to be included in selection programs. To identify large-effect QTL, we performed a series of genome-wide association studies and functional analyses using 50 K and 770 K SNP genotypes scored in 5,133 animals from 4 independent beef cattle populations (Cycle VII, Angus, Hereford and Simmental × Angus) with phenotypes for average daily gain, dry matter intake, metabolic mid-test body weight and residual feed intake. Results: A total of 5, 6, 11 and 10 significant QTL (defined as 1-Mb genome windows with Bonferroni-corrected P-value Conclusions: This GWAS study, which is the largest performed for feed efficiency and its component traits in beef cattle to date, identified several large-effect QTL that cumulatively explained a significant percentage of additive genetic variance within each population. Differences in the QTL identified among the different populations may be due to differences in power to detect QTL, environmental variation, or differences in the genetic architecture of trait variation among breeds. These results enhance our understanding of the biology of growth, feed intake and utilisation in beef cattle.