The effects of genotype-by-environment interactions on body condition score across three winter supplemental feed environments in a composite beef cattle breed in Montana.

Cattle operations in the Northern Great Plains region of the United States face extreme cold weather conditions and require nutritional supplementation over the winter season in order for animals to maintain body condition. In cow-calf operations, body condition scores (BCS) measured at calving and breeding have been shown to be associated with several economically important health and fertility traits, so maintenance of BCS is both an animal welfare and economic concern. A low-to-medium heritability has been found for BCS when measured across various production stages, indicating a large environmental influence but sufficient genetic basis for selection. The present study evaluated BCS measured prior to calving (late winter) and breeding (early summer) under three winter supplementation environments in a multitrait linear mixed model. Traits were discretized by winter supplementation and genetic correlations between environments were considered a reflection of evidence for genotype-by-environment interactions between BCS and diet. Winter supplementation treatments were fed October through April and varied by range access and protein content: 1) feedlot environment with approximately 15% crude protein (CP) corn/silage diet, 2) native rangeland access with 1.8 kg of an 18% CP pellet supplement, and 3) native rangeland access with a self-fed 50% CP and mineral supplement. A total of 2,988 and 2,353 records were collected across multiple parities on 1,010 and 800 individuals for prebreeding and precalving BCS, respectively. Heifers and cows came from a composite beef cattle breed developed and maintained by the USDA Fort Keogh Livestock and Range Research Laboratory near Miles City, Montana. Genetic correlations between treatments 1 and 2, 1 and 3, and 2 and 3 were 0.98, 0.78, and 0.65 and 1.00, 0.98, and 0.99 for precalving and prebreeding BCS, respectively. This provides moderate evidence of genotype-by-environment interactions for precalving BCS under treatment 3 relative to treatments 1 and 2, but no evidence for genotype-by-environment interactions for prebreeding BCS. Treatment 3 differed substantially in CP content relative to treatments 1 and 2, indicating that some animals differ in their ability to maintain BCS up to spring calving across a protein gradient. These results indicate the potential for selection of animals with increased resilience under cold weather conditions and high protein, restricted energy diets to maintain BCS.

Alternative SNP weighting for multi-step and single-step genomic BLUP in the presence of causative variants.

The accuracy of genetic selection in dairy can be increased by the adoption of new technologies, such as the inclusion of sequence data. In simulation studies, assigning different weights to causative single-nucleotide polymorphism (SNP) markers led to better predictions depending on the genomic prediction method used. However, it is still not clear how the weights should be calculated. Our objective was to evaluate the accuracy of a multi-step method (GBLUP) and single-step GBLUP with simulated data using regular SNP, causatives variants (QTN) and the combination of both. Additionally, we compared the accuracies of all previous scenarios using alternatives for SNP weighting. The data were simulated assuming a single trait with a heritability of 0.3. The effective population size (Ne) was approximately 200. The pedigree contained 440,000 animals, and approximately 16,800 individuals were genotyped. A total of 49,974 SNP markers were evenly placed throughout the genome, and 100, 1000 and 2000 causative QTN were simulated. Both GBLUP and ssGBLUP were used in this study. We evaluated quadratic and nonlinear SNP weights in addition to the unweighted G. The inclusion of QTN to panels led to significant accuracy gains. Nonlinear A was demonstrated to be superior to quadratic weighting and unweighted approaches; however, results from Nonlinear A were dependent on the equation parameters. The unweighted approach was more suitable for less polygenic scenarios. Finally, SNP weighting might help elucidate trait architecture features based on changes in the accuracy of genomic prediction.

Genomic Analysis of Heterosis in an Angus × Hereford Cattle Population.

Crossbreeding is widely used in the beef cattle industry to exploit its several benefits. This study evaluated the effects of heterozygosity on growth traits in an Angus × Hereford cross-population. Moreover, a genome wide association study was conducted to detect regions in the genome with significant dominance effects on growth traits contributing to heterosis. A total of 1530 animals comprising of pure Line 1 Hereford, Angus and Angus × Line 1 Hereford cross. Growth phenotypes included birth weight, weaning weight and yearling weight. All animals were genotyped with GeneSeek GGP LD 50k. Significant effects of genomic heterozygosity on growth traits were detected. These effects were 0.03 kg (0.006), 5.13 kg (0.04), 6.02 kg (0.08) on birth weight, weaning weight and yearling weight, respectively. Genome wide association study revealed several SNP markers with significant heterotic effects associated with birth weight, weaning weight and yearling weight. These SNP markers were located on chromosomes 1, 2, 6, 21, 14, 19, 13 and 12. Genes in these regions were reported to be involved in growth and other important physiological mechanisms. Our study revealed several regions associated with dominance effects and contributing to heterosis. These results could be beneficial in optimizing crossbreeding.

Fuzzy Logic as a Strategy for Combining Marker Statistics to Optimize Preselection of High-Density and Sequence Genotype Data.

The high dimensionality of genotype data available for genomic evaluations has presented a motivation for developing strategies to identify subsets of markers capable of increasing the accuracy of predictions compared to the current commercial single nucleotide polymorphism (SNP) chips. In this simulation study, an algorithm for combining statistics used in the preselection and prioritization of SNP markers from a high-density panel (1.3 million SNPs) into a composite "fuzzy" ranking score based on a Sugeno-type fuzzy inference system (FIS) was developed and evaluated for performance in preselection for genomic predictions. FST scores, and p-values were evaluated as inputs for the FIS. The accuracy of genomic predictions for fuzzy-score-preselected panel sizes of 1-50 k SNPs ranged from -0.4-11.7 and -0.3-3.8% higher than FST and p-value preselection, respectively. Though gains in prediction accuracies using only two inputs to the FIS were modest, preselection based on fuzzy scores yielded more accurate predictions than both FST scores and p-values for the majority of evaluated panel sizes under all genetic architectures. FIS have the potential to aggregate information from multiple criteria that reflect SNP-trait associations and biological relevance in a flexible and efficient way to yield higher quality genomic predictions.

Genetic architecture of a composite beef cattle population.

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.

Inbreeding Calculated with Runs of Homozygosity Suggests Chromosome-Specific Inbreeding Depression Regions in Line 1 Hereford.

The goal of this study was to evaluate inbreeding in a closed beef cattle population and assess phenotype prediction accuracy using inbreeding information. Effects of inbreeding on average daily gain phenotype in the Line 1 Hereford cattle population were assessed in this study. Genomic data were used to calculate inbreeding based on runs of homozygosity (ROH), and pedigree information was used to calculate the probability of an allele being identical by descent. Prediction ability of phenotypes using inbreeding coefficients calculated based on pedigree information and runs of homozygosity over the whole genome was close to 0, even in the case of significant inbreeding coefficient effects. On the other hand, inbreeding calculated per individual chromosomes' ROH yielded higher accuracies of prediction. Additionally, including only ROH from chromosomes with higher predicting ability further increased prediction accuracy. Phenotype prediction accuracy, inbreeding depression, and the effects of chromosome-specific ROHs varied widely across the genome. The results of this study suggest that inbreeding should be evaluated per individual regions of the genome. Moreover, mating schemes to avoid inbreeding depression should focus more on specific ROH with negative effects. Finally, using ROH as added information may increase prediction of the genetic merit of animals in a genomic selection program.

Dissection of the impact of prioritized QTL-linked and -unlinked SNP markers on the accuracy of genomic selection1.

BACKGROUND: Use of genomic information has resulted in an undeniable improvement in prediction accuracies and an increase in genetic gain in animal and plant genetic selection programs in spite of oversimplified assumptions about the true biological processes. Even for complex traits, a large portion of markers do not segregate with or effectively track genomic regions contributing to trait variation; yet it is not clear how genomic prediction accuracies are impacted by such potentially nonrelevant markers. In this study, a simulation was carried out to evaluate genomic predictions in the presence of markers unlinked with trait-relevant QTL. Further, we compared the ability of the population statistic FST and absolute estimated marker effect as preselection statistics to discriminate between linked and unlinked markers and the corresponding impact on accuracy. RESULTS: We found that the accuracy of genomic predictions decreased as the proportion of unlinked markers used to calculate the genomic relationships increased. Using all, only linked, and only unlinked marker sets yielded prediction accuracies of 0.62, 0.89, and 0.22, respectively. Furthermore, it was found that prediction accuracies are severely impacted by unlinked markers with large spurious associations. FST-preselected marker sets of 10 k and larger yielded accuracies 8.97 to 17.91% higher than those achieved using preselection by absolute estimated marker effects, despite selecting 5.1 to 37.7% more unlinked markers and explaining 2.4 to 5.0% less of the genetic variance. This was attributed to false positives selected by absolute estimated marker effects having a larger spurious association with the trait of interest and more negative impact on predictions. The Pearson correlation between FST scores and absolute estimated marker effects was 0.77 and 0.27 among only linked and only unlinked markers, respectively. The sensitivity of FST scores to detect truly linked markers is comparable to absolute estimated marker effects but the consistency between the two statistics regarding false positives is weak. CONCLUSION: Identification and exclusion of markers that have little to no relevance to the trait of interest may significantly increase genomic prediction accuracies. The population statistic FST presents an efficient and effective tool for preselection of trait-relevant markers.

Grid search approach to discriminate between old and recent inbreeding using phenotypic, pedigree and genomic information.

BACKGROUND: Although inbreeding caused by the mating of animals related through a recent common ancestor is expected to have more harmful effects on phenotypes than ancient inbreeding (old inbreeding), estimating these effects requires a clear definition of recent (new) and ancient (old) inbreeding. Several methods have been proposed to classify inbreeding using pedigree and genomic data. Unfortunately, these methods are largely based on heuristic criteria such as the number of generations from a common ancestor or length of runs of homozygosity (ROH) segments. To mitigate these deficiencies, this study aimed to develop a method to classify pedigree and genomic inbreeding into recent and ancient classes based on a grid search algorithm driven by the assumption that new inbreeding tends to have a more pronounced detrimental effect on traits. The proposed method was tested using a cattle population characterized by a deep pedigree. RESULTS: Effects of recent and ancient inbreeding were assessed on four growth traits (birth, weaning and yearling weights and average daily gain). Thresholds to classify inbreeding into recent and ancient classes were trait-specific and varied across traits and sources of information. Using pedigree information, inbreeding generated in the last 10 to 11 generations was considered as recent. When genomic information (ROH) was used, thresholds ranged between four to seven generations, indicating, in part, the ability of ROH segments to characterize the harmful effects of inbreeding in shorter periods of time. Nevertheless, using the proposed classification method, the discrimination between new and old inbreeding was less robust when ROH segments were used compared to pedigree. Using several model comparison criteria, the proposed approach was generally better than existing methods. Recent inbreeding appeared to be more harmful across the growth traits analyzed. However, both new and old inbreeding were found to be associated with decreased yearling weight and average daily gain. CONCLUSIONS: The proposed method provided a more objective quantitative approach for the classification of inbreeding. The proposed method detected a clear divergence in the effects of old and recent inbreeding using pedigree data and it was superior to existing methods for all analyzed traits. Using ROH data, the discrimination between old and recent inbreeding was less clear and the proposed method was superior to existing approaches for two out of the four analyzed traits. Deleterious effects of recent inbreeding were detected sooner (fewer generations) using genomic information than pedigree. Difference in the results using genomic and pedigree information could be due to the dissimilarity in the number of generations to a common ancestor. Additionally, the uncertainty associated with the identification of ROH segments and associated inbreeding could have an effect on the results. Potential biases in the estimation of inbreeding effects may occur when new and old inbreeding are discriminated based on arbitrary thresholds. To minimize the impact of inbreeding, mating designs should take the different inbreeding origins into consideration.

Effects of management decisions on genetic evaluation of simulated calving records using random regression.

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.

Runs of homozygosity and analysis of inbreeding depression.

Pedigree information was traditionally used to assess inbreeding. The availability of high-density marker panels provides an alternative to assess inbreeding, particularly in the presence of incomplete and error-prone pedigrees. Assessment of autozygosity across chromosomal segments using runs of homozygosity (ROH) has emerged as a valuable tool to estimate inbreeding due to its general flexibility and ability to quantify the chromosomal contribution to genome-wide inbreeding. Unfortunately, the identification of ROH segments is sensitive to the parameters used during the search process. These parameters are heuristically set, leading to significant variation in the results. The minimum length required to identify an ROH segment has major effects on the estimation of inbreeding and inbreeding depression, yet it is arbitrarily set. To overcome this limitation, a search algorithm to approximate mutation enrichment was developed to determine the minimum length of ROH segments. It consists of finding genome segments with significant effect differences in trait means between animals with high and low burdens of autozygous intervals with a specific length. The minimum length could be determined heuristically as the smallest interval at which a significant signal is detected. The proposed method was tested in an inbred Hereford cattle population genotyped for 30,220 SNPs. Phenotypes recorded for six traits were used for the approximation of mutation loads. The estimated minimum length was around 1 Mb for yearling weight (YW) and average daily gain (ADG) and 4 Mb for birth weight and weaning weight. These trait-specific thresholds estimated using the proposed method could be attributed to a trait-dependent effect of homozygosity. The detection of significant inbreeding effects was well aligned with the estimated thresholds, especially for YW and ADG. Although highly deleterious alleles are expected to be more frequent in recent inbreeding (long ROH), short ROH segments (<5 Mb) could contain a large number of less deleterious mutations with substantial joint effects on some traits (YW and ADG). Our results highlight the importance of accurate estimation of the ROH-based inbreeding and the necessity to consider a trait-specific minimum length threshold for the identification of ROH segments in inbreeding depression analyses. These thresholds could be determined using the proposed method provided the availability of phenotypic information.

Genomic Breed Composition of Selection Signatures in Brangus Beef Cattle.

Cattle breeding routinely uses crossbreeding between subspecies (Bos taurus taurus and Bos taurus indicus) to form composite breeds, such as Brangus. These composite breeds provide an opportunity to identify recent selection signatures formed in the new population and evaluate the genomic composition of these regions of the genome. Using high-density genotyping, we first identified runs of homozygosity (ROH) and calculated genomic inbreeding. Then, we evaluated the genomic composition of the regions identified as selected (selective sweeps) using a chromosome painting approach. The genomic inbreeding increased at approximately 1% per generation after composite breed formation, showing the need of inbreeding control even in composite breeds. Three selected regions in Brangus were also identified as Angus selection signatures. Two regions (chromosomes 14 and 21) were identified as signatures of selection in Brangus and both founder breeds. Five of the 10 homozygous regions in Brangus were predominantly Angus in origin (probability >80%), and the other five regions had a mixed origin but always with Brahman contributing less than 50%. Therefore, genetic events, such as drift, selection, and complementarity, are likely shaping the genetic composition of founder breeds in specific genomic regions. Such findings highlight a variety of opportunities to better control the selection process and explore heterosis and complementarity at the genomic level in composite breeds.

A Weighted Genomic Relationship Matrix Based on Fixation Index (FST) Prioritized SNPs for Genomic Selection.

A dramatic increase in the density of marker panels has been expected to increase the accuracy of genomic selection (GS), unfortunately, little to no improvement has been observed. By including all variants in the association model, the dimensionality of the problem should be dramatically increased, and it could undoubtedly reduce the statistical power. Using all Single nucleotide polymorphisms (SNPs) to compute the genomic relationship matrix (G) does not necessarily increase accuracy as the additive relationships can be accurately estimated using a much smaller number of markers. Due to these limitations, variant prioritization has become a necessity to improve accuracy. The fixation index (FST) as a measure of population differentiation has been used to identify genome segments and variants under selection pressure. Using prioritized variants has increased the accuracy of GS. Additionally, FST can be used to weight the relative contribution of prioritized SNPs in computing G. In this study, relative weights based on FST scores were developed and incorporated into the calculation of G and their impact on the estimation of variance components and accuracy was assessed. The results showed that prioritizing SNPs based on their FST scores resulted in an increase in the genetic similarity between training and validation animals and improved the accuracy of GS by more than 5%.

Multigenerational Effects.

Environmental influences resulting in epigenetic mediation of gene expression can affect multiple generations via direct effect (first generation); direct or maternally mediated effects on the fetus (second generation), or gonadal cell lines of the fetus (third generation) when pregnant animals are exposed to the stimuli; and through generational inheritance. The cumulative effects are rapid changes in phenotypic characteristics of the population when compared with rate of phenotypic change from genetic selection. With extensive data collection, significant potential exists to propagate desired characteristics in the livestock industry through epigenetic pathways.

New world goat populations are a genetically diverse reservoir for future use.

Western hemisphere goats have European, African and Central Asian origins, and some local or rare breeds are reported to be adapted to their environments and economically important. By-in-large these genetic resources have not been quantified. Using 50 K SNP genotypes of 244 animals from 12 goat populations in United States, Costa Rica, Brazil and Argentina, we evaluated the genetic diversity, population structure and selective sweeps documenting goat migration to the "New World". Our findings suggest the concept of breed, particularly among "locally adapted" breeds, is not a meaningful way to characterize goat populations. The USA Spanish goats were found to be an important genetic reservoir, sharing genomic composition with the wild ancestor and with specialized breeds (e.g. Angora, Lamancha and Saanen). Results suggest goats in the Americas have substantial genetic diversity to use in selection and promote environmental adaptation or product driven specialization. These findings highlight the importance of maintaining goat conservation programs and suggest an awaiting reservoir of genetic diversity for breeding and research while simultaneously discarding concerns about breed designations.

Inbreeding depression in line 1 Hereford cattle population using pedigree and genomic information.

This study aimed at assessing inbreeding and its effect on growth and fertility traits using the longtime closed line 1 Hereford cattle population. Inbreeding was estimated based on pedigree (FPED) and genomic information. For the latter, three estimates were derived based on the diagonal elements of the genomic relationship matrix using estimated (FGRM) or fixed (FGRM0.5) minor allele frequencies or runs of homozygosity (ROH) (FROH). A pedigree containing 10,186 animals was used to calculate FPED. Genomic inbreeding was evaluated using 785 animals genotyped for 30,810 SNP. Traits analyzed were birth weight (BWT), weaning weight (WWT), yearling weight (YWT), ADG, and age at first calving (AFC). The number of ROH per animal ranged between 6 and 119 segments with an average of 83. The shortest and longest segments were 1.36 and 64.86 Mb long, respectively, reflecting both ancient and recent inbreeding occurring in the last 30 to 40 generations. The average inbreeding was 29.2%, 16.1%, 30.2%, and 22.9% for FPED, FGRM, FGRM0.5, and FROH, respectively. FROH provided the highest correlations with FPED (r = 0.66). Across paternal half-sib families, with minimal variation in FPED, there were substantial variations in their genomic inbreeding. Inbreeding depression analyses showed that a 1% increase in an animal's FPED resulted in a decrease of 1.20 kg, 2.03 kg, and 0.004 kg/d in WWT, YWT, and ADG, respectively. Maternal inbreeding showed significantly negative effects on progeny growth performance. AFC increased by 1.4 and 0.8 d for each 1% increase in FPED of the cow and her dam, respectively. Using genomic inbreeding, similar impact on growth traits was observed although the magnitude of the effect varied between methods. Across all genomic measures, WWT, YWT, and ADG decreased by 0.21 to 0.53 kg, 0.46 to 1.13 kg, and 0.002 to 0.006 kg/d for each 1% increase in genomic inbreeding, respectively. Four chromosomes (9, 12, 17, and 27) were identified to have a significant association between their homozygosity (FROH-CHR) and growth traits. Variability in genomic inbreeding could be useful when deciding between full and half-sib selection candidates. Despite the high level of inbreeding in this study, its negative impact on growth performance was not as severe as expected, which may be attributed to the purging of the deleterious alleles due to natural or artificial selection over time.

A Bayesian approach for analysis of ordered categorical responses subject to misclassification.

Ordinal categorical responses are frequently collected in survey studies, human medicine, and animal and plant improvement programs, just to mention a few. Errors in this type of data are neither rare nor easy to detect. These errors tend to bias the inference, reduce the statistical power and ultimately the efficiency of the decision-making process. Contrarily to the binary situation where misclassification occurs between two response classes, noise in ordinal categorical data is more complex due to the increased number of categories, diversity and asymmetry of errors. Although several approaches have been presented for dealing with misclassification in binary data, only limited practical methods have been proposed to analyze noisy categorical responses. A latent variable model implemented within a Bayesian framework was proposed to analyze ordinal categorical data subject to misclassification using simulated and real datasets. The simulated scenario consisted of a discrete response with three categories and a symmetric error rate of 5% between any two classes. The real data consisted of calving ease records of beef cows. Using real and simulated data, ignoring misclassification resulted in substantial bias in the estimation of genetic parameters and reduction of the accuracy of predicted breeding values. Using our proposed approach, a significant reduction in bias and increase in accuracy ranging from 11% to 17% was observed. Furthermore, most of the misclassified observations (in the simulated data) were identified with a substantially higher probability. Similar results were observed for a scenario with asymmetric misclassification. While the extension to traits with more categories between adjacent classes is straightforward, it could be computationally costly. For traits with high heritability, the performance of the methodology would be expected to improve.

Genomic predictions combining SNP markers and copy number variations in Nellore cattle.

BACKGROUND: Due to the advancement in high throughput technology, single nucleotide polymorphism (SNP) is routinely being incorporated along with phenotypic information into genetic evaluation. However, this approach often cannot achieve high accuracy for some complex traits. It is possible that SNP markers are not sufficient to predict these traits due to the missing heritability caused by other genetic variations such as microsatellite and copy number variation (CNV), which have been shown to affect disease and complex traits in humans and other species. RESULTS: In this study, CNVs were included in a SNP based genomic selection framework. A Nellore cattle dataset consisting of 2230 animals genotyped on BovineHD SNP array was used, and 9 weight and carcass traits were analyzed. A total of six models were implemented and compared based on their prediction accuracy. For comparison, three models including only SNPs were implemented: 1) BayesA model, 2) Bayesian mixture model (BayesB), and 3) a GBLUP model without polygenic effects. The other three models incorporating both SNP and CNV included 4) a Bayesian model similar to BayesA (BayesA+CNV), 5) a Bayesian mixture model (BayesB+CNV), and 6) GBLUP with CNVs modeled as a covariable (GBLUP+CNV). Prediction accuracies were assessed based on Pearson's correlation between de-regressed EBVs (dEBVs) and direct genomic values (DGVs) in the validation dataset. For BayesA, BayesB and GBLUP, accuracy ranged from 0.12 to 0.62 across the nine traits. A minimal increase in prediction accuracy for some traits was noticed when including CNVs in the model (BayesA+CNV, BayesB+CNV, GBLUP+CNV). CONCLUSIONS: This study presents the first genomic prediction study integrating CNVs and SNPs in livestock. Combining CNV and SNP marker information proved to be beneficial for genomic prediction of some traits in Nellore cattle.

Genotype × prenatal and post-weaning nutritional environment interaction in a composite beef cattle breed using reaction norms and a multi-trait model.

Environmental effects have been shown to influence several economically important traits in beef cattle. In this study, genotype × nutritional environment interaction has been evaluated in a composite beef cattle breed (50% Red Angus, 25% Charolais, 25% Tarentaise). Four nutritional environments (marginal-restricted [MARG-RES], marginal-control [MARG-CTRL], adequate-restricted [ADEQ-RES], and adequate-control [ADEQ-CTRL]) were created based on two levels of winter supplement provided to dams grazing winter range during gestation (MARG and ADEQ) and two levels of input to offspring during post-weaning development (RES and CTRL). Genetic parameters of average daily gain (ADG) during the 140-d post-wean trial, yearling weight (YW), and ultrasound measurement of fat depth (FAT) at the 12th rib and intramuscular fat percentage (IMF) of 3,020 individuals in the four environments were estimated. The heritabilities estimated using a single trait mixed linear model were: ADG: 0.21, 0.23, 0.19 and 0.21; YW: 0.27, 0.33, 0.20 and 0.26; FAT: 0.30, 0.29, 0.29, 0.55; IMF: 0.45, 0.51, 0.33, 0.53 for MARG-RES, ADEQ-RES, MARG-CTRL and ADEQ-CTRL, respectively. The extent of genotype × environment interaction was modeled using two separate methods: reaction norms and multi-trait models. The genetic correlations were estimated using a multi-trait model for ADG, YW, FAT and IMF. Growth traits (ADG, YW) and FAT showed correlations less than 0.80 across the four different environments indicating genotype by environment interaction. For example, genetic correlation for ADG between MARG-CTRL and MARG-RES was 0.65 and 0.73 between ADEQ-RES and MARG-RES. In this example, the former genetic correlation corresponds to differences in post-weaning nutritional environment, and the later represents a nutritional difference imposed on dams (i.e., prenatal environment), potentially mediated via fetal programming. The reaction norm model results were in concordance with the multi-trait model, genotype by environment interaction had a higher effect on traits with a lower heritability.

Fast genomic prediction of breeding values using parallel Markov chain Monte Carlo with convergence diagnosis.

BACKGROUND: Running multiple-chain Markov Chain Monte Carlo (MCMC) provides an efficient parallel computing method for complex Bayesian models, although the efficiency of the approach critically depends on the length of the non-parallelizable burn-in period, for which all simulated data are discarded. In practice, this burn-in period is set arbitrarily and often leads to the performance of far more iterations than required. In addition, the accuracy of genomic predictions does not improve after the MCMC reaches equilibrium. RESULTS: Automatic tuning of the burn-in length for running multiple-chain MCMC was proposed in the context of genomic predictions using BayesA and BayesCπ models. The performance of parallel computing versus sequential computing and tunable burn-in MCMC versus fixed burn-in MCMC was assessed using simulation data sets as well by applying these methods to genomic predictions of a Chinese Simmental beef cattle population. The results showed that tunable burn-in parallel MCMC had greater speedups than fixed burn-in parallel MCMC, and both had greater speedups relative to sequential (single-chain) MCMC. Nevertheless, genomic estimated breeding values (GEBVs) and genomic prediction accuracies were highly comparable between the various computing approaches. When applied to the genomic predictions of four quantitative traits in a Chinese Simmental population of 1217 beef cattle genotyped by an Illumina Bovine 770 K SNP BeadChip, tunable burn-in multiple-chain BayesCπ (TBM-BayesCπ) outperformed tunable burn-in multiple-chain BayesCπ (TBM-BayesA) and Genomic Best Linear Unbiased Prediction (GBLUP) in terms of the prediction accuracy, although the differences were not necessarily caused by computational factors and could have been intrinsic to the statistical models per se. CONCLUSIONS: Automatically tunable burn-in multiple-chain MCMC provides an accurate and cost-effective tool for high-performance computing of Bayesian genomic prediction models, and this algorithm is generally applicable to high-performance computing of any complex Bayesian statistical model.