Multi-Trait Bayesian Models Enhance the Accuracy of Genomic Prediction in Multi-Breed Reference Populations

Weining Li, Meilin Zhang, Heng Du, Jianliang Wu, Lei Zhou and Jianfeng Liu ()
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Weining Li: State Key Laboratory of Animal Biotech Breeding, College of Animal Science and Technology, China Agricultural University, Haidian, Beijing 100193, China
Meilin Zhang: State Key Laboratory of Animal Biotech Breeding, College of Animal Science and Technology, China Agricultural University, Haidian, Beijing 100193, China
Heng Du: State Key Laboratory of Animal Biotech Breeding, College of Animal Science and Technology, China Agricultural University, Haidian, Beijing 100193, China
Jianliang Wu: Beijing Zhongyu Pig Breeding Co., Ltd., Beijing 100194, China
Lei Zhou: State Key Laboratory of Animal Biotech Breeding, College of Animal Science and Technology, China Agricultural University, Haidian, Beijing 100193, China
Jianfeng Liu: State Key Laboratory of Animal Biotech Breeding, College of Animal Science and Technology, China Agricultural University, Haidian, Beijing 100193, China

Agriculture, 2024, vol. 14, issue 4, 1-19

Abstract: Performing joint genomic predictions for multiple breeds (MBGP) to expand the reference size is a promising strategy for improving the prediction for limited population sizes or phenotypic records for a single breed. This study proposes an MBGP model—mbBayesAB, which treats the same traits of different breeds as potentially genetically related but different, and divides chromosomes into independent blocks to fit heterogeneous genetic (co)variances. Best practices of random effect (co)variance matrix priors in mbBayesAB were analyzed, and the prediction accuracies of mbBayesAB were compared with within-breed (WBGP) and other commonly used MBGP models. The results showed that assigning an inverse Wishart prior to the random effect and obtaining information on the scale of the inverse Wishart prior from the phenotype enabled mbBayesAB to achieve the highest accuracy. When combining two cattle breeds (Limousin and Angus) in reference, mbBayesAB achieved higher accuracy than the WBGP model for two weight traits. For the marbling score trait in pigs, MBGP of the Yorkshire and Landrace breeds led to a 6.27% increase in accuracy for Yorkshire validation using mbBayesAB compared to that using the WBGP model. Therefore, considering heterogeneous genetic (co)variance in MBGP is advantageous. However, determining appropriate priors for (co)variance and hyperparameters is crucial for MBGP.

Keywords: genomic prediction; multi-breed; Bayes; heterogeneous genetic (co)variances; matrix prior; hierarchical inverse Wishart prior (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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