Multiple-Trait Genomic Selection Methods Increase Genetic Value Prediction Accuracy

TLDR

Genetic correlations among quantitative traits mean that measuring one trait provides information on others, but current single‑trait genomic selection ignores this; multivariate genomic selection could exploit it but has been little explored in practice. The authors compared GBLUP, BayesA, and BayesCπ multivariate linear models to univariate models on simulated and real traits, extended BayesA to a hierarchical model estimating hyperparameters and used BayesCπ to impute missing phenotypes, and investigated additional factors influencing performance. Optimal marker‑effect variance priors depend on genetic architecture, and multivariate genomic selection markedly improves prediction accuracy for low‑heritability traits when correlated high‑heritability traits are available, and also outperforms single‑trait models when phenotypes are missing across individuals and traits.

Abstract

Abstract Genetic correlations between quantitative traits measured in many breeding programs are pervasive. These correlations indicate that measurements of one trait carry information on other traits. Current single-trait (univariate) genomic selection does not take advantage of this information. Multivariate genomic selection on multiple traits could accomplish this but has been little explored and tested in practical breeding programs. In this study, three multivariate linear models (i.e., GBLUP, BayesA, and BayesCπ) were presented and compared to univariate models using simulated and real quantitative traits controlled by different genetic architectures. We also extended BayesA with fixed hyperparameters to a full hierarchical model that estimated hyperparameters and BayesCπ to impute missing phenotypes. We found that optimal marker-effect variance priors depended on the genetic architecture of the trait so that estimating them was beneficial. We showed that the prediction accuracy for a low-heritability trait could be significantly increased by multivariate genomic selection when a correlated high-heritability trait was available. Further, multiple-trait genomic selection had higher prediction accuracy than single-trait genomic selection when phenotypes are not available on all individuals and traits. Additional factors affecting the performance of multiple-trait genomic selection were explored.