PT  - JOURNAL ARTICLE
AU  - Hao Cheng
AU  - Kadir Kizilkaya
AU  - Jian Zeng
AU  - Dorian Garrick
AU  - Rohan Fernando
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.