Comparison between estimation of breeding values and fixed effects using Bayesian and empirical BLUP estimation under selection on parents and missing pedigree information

Bayesian (via Gibbs sampling) and empirical BLUP (EBLUP) estimation of fixed effects and breeding values were compared by simulation. Combinations of two simulation models (with or without effect of contemporary group (CG)), three selection schemes (random, phenotypic and BLUP selection), two levels of heritability (0.20 and 0.50) and two levels of pedigree information (0% and 15% randomly missing) were considered. Populations consisted of 450 animals spread over six discrete generations. An infinitesimal additive genetic animal model was assumed while simulating data. EBLUP and Bayesian estimates of CG effects and breeding values were, in all situations, essentially the same with respect to Spearman's rank correlation between true and estimated values. Bias and mean square error (MSE) of EBLUP and Bayesian estimates of CG effects and breeding values showed the same pattern over the range of simulated scenarios. Methods were not biased by phenotypic and BLUP selection when pedigree information was complete, albeit MSE of estimated breeding values increased for situations where CG effects were present. Estimation of breeding values by Bayesian and EBLUP was similarly affected by joint effect of phenotypic or BLUP selection and randomly missing pedigree information. For both methods, bias and MSE of estimated breeding values and CG effects substantially increased across generations

Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection

The aim of this study was to compare iterative and direct solvers for estimation of marker effects in genomic selection. One iterative and two direct methods were used: Gauss-Seidel with Residual Update, Cholesky Decomposition and Gentleman-Givens rotations. For resembling different scenarios with respect to number of markers and of genotyped animals, a simulated data set divided into 25 subsets was used. Number of markers ranged from 1,200 to 5,925 and number of animals ranged from 1,200 to 5,865. Methods were also applied to real data comprising 3081 individuals genotyped for 45181 SNPs. Results from simulated data showed that the iterative solver was substantially faster than direct methods for larger numbers of markers. Use of a direct solver may allow for computing (co)variances of SNP effects. When applied to real data, performance of the iterative method varied substantially, depending on the level of ill-conditioning of the coefficient matrix. From results with real data, Gentleman-Givens rotations would be the method of choice in this particular application as it provided an exact solution within a fairly reasonable time frame (less than two hours). It would indeed be the preferred method whenever computer resources allow its use

Novel methods for genotype imputation to whole-genome sequence and a simple linear model to predict imputation accuracy

Abstract Background Accurate imputation plays a major role in genomic studies of livestock industries, where the number of genotyped or sequenced animals is limited by costs. This study explored methods to create an ideal reference population for imputation to Next Generation Sequencing data in cattle. Methods Methods for clustering of animals for imputation were explored, using 1000 Bull Genomes Project sequence data on 1146 animals from a variety of beef and dairy breeds. Imputation from 50 K to 777 K was first carried out to choose an ideal clustering method, using ADMIXTURE or PLINK clustering algorithms with either genotypes or reconstructed haplotypes. Results Due to efficiency, accuracy and ease of use, clustering with PLINK using haplotypes as quasi-genotypes was chosen as the most advantageous grouping method. It was found that using a clustered population slightly decreased computing time, while maintaining accuracy across the population. Although overall accuracy remained the same, a slight increase in accuracy was observed for groups of animals in some breeds (primarily purebred beef cattle from breeds with fewer sequenced animals) and for other groups, primarily crossbreed animals, a slight decrease in accuracy was observed. However, it was noted that some animals in each breed were poorly imputed across all methods. When imputed sequences were included in the reference population to aid imputation of poorly imputed animals, a small increase in overall accuracy was observed for nearly every individual in the population. Two models were created to predict imputation accuracy, a complete model using all information available including Euclidean distances from genotypes and haplotypes, pedigree information, and clustering groups and a simple model using only breed and an Euclidean distance matrix as predictors. Both models were successful in predicting imputation accuracy, with correlations between predicted and true imputation accuracy as measured by concordance rate of 0.87 and 0.83, respectively. Conclusions A clustering methodology can be very useful to subgroup cattle for efficient genotype imputation. In addition, accuracy of genotype imputation from medium to high-density Single Nucleotide Polymorphisms (SNP) chip panels to whole-genome sequence can be predicted well using a simple linear model defined in this study

Marginal ancestral contributions to atrial fibrillation in the Standardbred racehorse: Comparison of cases and controls

<div><p>Admissions of Standardbred racehorses (<b>Std</b>) to the Ontario Veterinary College Teaching Hospital (<b>OVCTH</b>) for treatment of atrial fibrillation (<b>AF</b>) began to increase in the early 1990s. The arrhythmia has been shown to have a modest heritability (h² ≃ 0.15), with some stallions appearing as sires or sires of mares used in breeding (broodmares) of affected horses more frequently than others. The objective of this study was to determine the marginal genetic contributions of ancestors to cohorts of Std affected with AF and their contemporary control groups, and whether these ancestors contribute significantly more to the affected cohorts than to controls. All Std admitted to OVCTH for treatment of AF that were born between 1993 and 2007 comprised the affected case group (n = 168). Five randomly selected racing contemporaries for each Std admitted, assumed to not suffer from the arrhythmia, comprised the control group (n = 840). Three-year overlapping cohorts were created for case and control horses, determined according to year of birth, for a total of 26 cohorts. Marginal genetic contributions of ancestors to each cohort were determined and differences analyzed for statistical significance using a two-tailed paired t-test, with P ≤ 0.05 considered significant. The marginal contributions of 26 ancestors were significant, with 11 contributing significantly more to affected cohorts than the corresponding controls, and 15 contributing significantly more to controls than the corresponding affected cohorts. One stallion and one broodmare were very highly significant to affected cohorts at P ≤ 0.001, and nine stallions and three broodmares were very highly significant to control cohorts at P ≤ 0.001. Therefore, a number of stallions have statistically significant contributions to the genetics of Std affected with AF, while many others have statistically significant contributions to healthy Std. The arrhythmia appears to be particularly prevalent in the descendants of one sire family.</p></div

Strategies for within-litter selection of piglets using ultra-low density SNP panels

Genotyping costs and the large number of selection candidates are major factors that inhibit the application of genomic selection in the swine industry and other small-sized livestock species. In order to reduce genotyping costs and increase the uptake of genomic selection, a possible strategy is to genotype animals with an affordable low-density (LD) SNP panel and, then accurately impute the LD panel to a high-density (HD) SNP panel. For within-litter piglet selection, genotyping all piglets from all farrows using the commercially available SNP chips is still cost prohibitive. Consequently, genomic evaluation is limited in this stage and genotypic and phenotypic data from all piglets in a litter are rarely available. This study investigates the feasibility of implementing genomic selection for within-litter piglet selection, using a total of nine simulated LD panels: from the “ultra” low (300–3000 SNP markers) to moderately low (6000–10, 000 SNP markers). For each LD panel, the performance of the genomic predictions according to the accuracy of genotype imputation, the accuracy of the genomic estimated breeding values (GEBV) based on the imputed data, and distribution of the correctly selected animals within litter was evaluated and compared to using the simulated HD panel (60,000 SNP) and True Breeding Values (TBVs). In this simulation study, we considered three economically important traits: back fat thickness (BF), growth rate of age to 100 Kg (GR), and litter size (LS). For the LD panel sizes ranging from 300 to 10,000, the accuracy of imputation (measured as concordance rate) ranged from 73.20 to 99.81%; and the mean proportion of the correctly selected top rank animals within litter ranged from 55 to 98%. Based on the trade-off between panel size and genomic selection accuracy, the use of a LD panel containing 1500 SNPs might be recommended, as this panel yielded more than 85% correctly selected animals within-litter based on all three traits

Percentage of affected horses which have each of 11 significant ancestors present at least once in their five-generation pedigree.

<p>Percentage of affected horses which have each of 11 significant ancestors present at least once in their five-generation pedigree.</p