PT - JOURNAL ARTICLE AU - Cheng, Hao AU - Kizilkaya, Kadir AU - Zeng, Jian AU - Garrick, Dorian AU - Fernando, Rohan TI - Multple-trait Bayesian Regression Methods with Mixture Priors for Genomic Prediction AID - 10.1101/102962 DP - 2017 Jan 01 TA - bioRxiv PG - 102962 4099 - http://biorxiv.org/content/early/2017/01/25/102962.short 4100 - http://biorxiv.org/content/early/2017/01/25/102962.full 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.