Genomic Prediction from Multiple-Trait Bayesian Regression Methods Using Mixture Priors.

ABSTRACT: Bayesian multiple-regression methods incorporating different mixture priors for marker effects are used widely in genomic prediction. Improvement in prediction accuracies from using those methods, such as BayesB, BayesC, and BayesCπ, have been shown in single-trait analyses with both simulated and real data. These methods have been extended to multi-trait analyses, but only under the restrictive assumption that a locus simultaneously affects all the traits or none of them. This assumption is not biologically meaningful, especially in multi-trait analyses involving many traits. In this paper, we develop and implement a more general multi-trait BayesC[Formula: see text] and BayesB methods allowing a broader range of mixture priors. Our methods allow a locus to affect any combination o

SUBMITTER: Cheng H 

PROVIDER: S-EPMC5937171 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature