Abstract
The objectives of this study were to 1) compare four models for breeding value prediction using genomic or pedigree information and 2) evaluate the impact of fixed effects that account for family structure. Comparisons were made in a Nellore-Angus population comprising F2, F3 and half-siblings to embryo transfer F2 calves with records for overall temperament at weaning (TEMP; n = 769) and Warner-Bratzler shear force (WBSF; n = 387). After quality control, there were 34,913 whole genome SNP markers remaining. Bayesian methods employed were BayesB (π̃ = 0.995 or 0.997 for WBSF or TEMP, respectively) and BayesC (π = 0 and π̃), where π̃ is the ideal proportion of markers not included. Direct genomic values (DGV) from single trait Bayesian analyses were compared to conventional pedigree-based animal model breeding values. Numerically, BayesC procedures (using π̃) had the highest accuracy of all models for WBSF and TEMP (ρ̂gĝ = 0.843 and 0.923, respectively), but BayesB had the least bias (regression of performance on prediction closest to 1, β̂y,x = 2.886 and 1.755, respectively). Accounting for family structure decreased accuracy and increased bias in prediction of DGV indicating a detrimental impact when used in these prediction methods that simultaneously fit many markers.
Highlights
Expected progeny differences (EPD) are widely used for selection in the US beef cattle industry
estimated breeding values (EBV) from animal models for Warner-Bratzler shear force (WBSF) and temperament at weaning (TEMP) were compared to direct genomic values (DGV) from three Bayesian methods with and without inclusion of fixed effects to account for significant family structure (Tables 2-4)
Inclusion of fixed effects to account for family structure resulted in lower Spearman Rank correlation coefficients for DGV from any Bayesian model compared with EBV from a traditional animal model excluding such fixed effects
Summary
Expected progeny differences (EPD) are widely used for selection in the US beef cattle industry. The objectives of this study were to 1) evaluate and compare four models (three Bayesian using genomic information and a pedigree-based animal model) in predicting genetic merit using all animals in the training population; and 2) evaluate the effects on genetic merit prediction of including or excluding fixed effects to account for family structure. These objectives were investigated using single trait analyses of two traits measured in a Nellore-Angus crossbred population
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