Comparison of breeding value prediction for two traits in a Nellore-Angus crossbred population using different Bayesian modeling methodologies

Genetics and Molecular Biology

Lauren L. Hulsman Hanna²  Dorian J. Garrick¹  Clare A. Gill²  Andy D. Herring²  James O. Sanders²  David G. Riley² 

[1] ,Texas A & M University Department of Animal Science College Station Texas ,USA

关键词: Bayesian inference;    crossbred cattle;    genomic prediction;    model comparison;   

DOI  :  10.1590/S1415-47572014005000021

来源: SciELO

【 摘 要 】

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 ( π = 0.995 or 0.997 for WBSF or TEMP, respectively) and BayesC (π = 0 and π), where π 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 π) had the highest accuracy of all models for WBSF and TEMP ( ρgg = 0.843 and 0.923, respectively), but BayesB had the least bias (regression of performance on prediction closest to 1, βy,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.

【 授权许可】

CC BY   
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