Abstract

BackgroundGenomic selection is a recently developed technology that is beginning to revolutionize animal breeding. The objective of this study was to estimate marker effects to derive prediction equations for direct genomic values for 16 routinely recorded traits of American Angus beef cattle and quantify corresponding accuracies of prediction.MethodsDeregressed estimated breeding values were used as observations in a weighted analysis to derive direct genomic values for 3570 sires genotyped using the Illumina BovineSNP50 BeadChip. These bulls were clustered into five groups using K-means clustering on pedigree estimates of additive genetic relationships between animals, with the aim of increasing within-group and decreasing between-group relationships. All five combinations of four groups were used for model training, with cross-validation performed in the group not used in training. Bivariate animal models were used for each trait to estimate the genetic correlation between deregressed estimated breeding values and direct genomic values.ResultsAccuracies of direct genomic values ranged from 0.22 to 0.69 for the studied traits, with an average of 0.44. Predictions were more accurate when animals within the validation group were more closely related to animals in the training set. When training and validation sets were formed by random allocation, the accuracies of direct genomic values ranged from 0.38 to 0.85, with an average of 0.65, reflecting the greater relationship between animals in training and validation. The accuracies of direct genomic values obtained from training on older animals and validating in younger animals were intermediate to the accuracies obtained from K-means clustering and random clustering for most traits. The genetic correlation between deregressed estimated breeding values and direct genomic values ranged from 0.15 to 0.80 for the traits studied.ConclusionsThese results suggest that genomic estimates of genetic merit can be produced in beef cattle at a young age but the recurrent inclusion of genotyped sires in retraining analyses will be necessary to routinely produce for the industry the direct genomic values with the highest accuracy.

Highlights

  • Genomic selection is a recently developed technology that is beginning to revolutionize animal breeding

  • Habier et al [11] indicated that genomic selection uses genetic relationships among individuals and linkage disequilibrium (LD) between markers and quantitative trait loci (QTL) to improve the accuracy of direct genomic values (DGV)

  • The increase in accuracy of evaluation from using a genomic relationship matrix in traditional animal models comes from replacing an expected relationship matrix, which is conditional on the pedigree, with a realized matrix that is not influenced by missing pedigree information or violation of the assumption that the Mendelian sampling of parental gametes is drawn from a distribution with zero mean

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Summary

Introduction

Genomic selection is a recently developed technology that is beginning to revolutionize animal breeding. For a given selection intensity, response to at least 50 000 single nucleotide polymorphisms (SNP) using a variety of assays, such as the BovineSNP50 [2], BovineHD (Illumina, San Diego, CA) or Axiom BOS 1 (Affymetrix, Santa Clara, CA) assays. These SNP panels can be used to produce direct genomic values (DGV), as proposed by Meuwissen et al [1], via the estimation of marker effects from the analysis of a population with SNP genotypes and trait phenotypes (training set). Saatchi et al [13] and Habier et al [14] have shown that the number of generations separating training and validation datasets influences accuracy, with lower accuracies occurring when this relationship is more distant

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