35 Young Scholar Award Talk: Computing Strategies for National Beef Cattle Evaluations

Abstract

Abstract The constantly growing size of the national beef cattle evaluations datasets creates computational challenges to estimate breeding values and their accuracies under the single-step methodology timely and in a robust way. This study aims to present strategies for making weekly national single-step beef cattle evaluations computationally feasible. The process for estimating breeding values in single-step can be divided into two parts. In the first part, hereafter called genomic setup, the genotype file is read, and the inverse of the genomic relationship matrix is calculated using the Algorithm for Proven and Young (APY). The genotype file is read in a stream-unformatted format, and the markers are stored bit-wise. A residual polygenic effect can be fitted with an algorithm whose computing cost is constant with the number of genotyped animals. The calculation of is done using parallel computing and optimized matrix algebra procedures. The second part estimates breeding values by iteration on data with a block diagonal preconditioner. Finally, the accuracy of the estimated breeding values is calculated by combining the pedigree and genomic information in terms of effective daughter contributions. For an Angus dataset with 10 million animals in the pedigree and 500,000 genotypes, the genomic setup took 4 hours. With the same dataset, solutions for a three-trait model with maternal effects including birth weight, weaning weight, and post-weaning gain were obtained after 12 hours. The approximation of the accuracies of breeding values took less than one hour. Our results show that weekly national beef cattle evaluations are computationally feasible.