RT Journal Article
SR Electronic
T1 Multple-trait Bayesian Regression Methods with Mixture Priors for Genomic Prediction
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 102962
DO 10.1101/102962
A1 Hao Cheng
A1 Kadir Kizilkaya
A1 Jian Zeng
A1 Dorian Garrick
A1 Rohan Fernando
YR 2017
UL http://biorxiv.org/content/early/2017/01/25/102962.abstract
AB Bayesian multiple-regression methods incorporating different mixture priors for marker effects are widely used 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 data and real data. These methods have been extended to multi-trait analyses, but only under a specific limited circumstance that assumes a locus affects all the traits or none of them. In this paper, we develop and implement the most general multi-trait BayesCΠ and BayesB methods allowing a broader range of mixture priors. Further, we compare them to single-trait methods and the “restricted” multi-trait formulation using real data. In those data analyses, significant higher prediction accuracies were sometimes observed from these new broad-based multi-trait Bayesian multiple-regression methods. The software tool JWAS offers routines to perform the analyses.