Genomic prediction from multiple-trait bayesian regression methods using mixture priors

Cheng, Hao, Kizilkaya, Kadir, Zeng, Jian, Garrick, Dorian and Fernando, Rohan (2018) Genomic prediction from multiple-trait bayesian regression methods using mixture priors. Genetics, 209 1: 89-103. doi:10.1534/genetics.118.300650

Author Cheng, Hao
Kizilkaya, Kadir
Zeng, Jian
Garrick, Dorian
Fernando, Rohan
Title Genomic prediction from multiple-trait bayesian regression methods using mixture priors
Journal name Genetics   Check publisher's open access policy
ISSN 1943-2631
Publication date 2018-03-07
Year available 2018
Sub-type Article (original research)
DOI 10.1534/genetics.118.300650
Open Access Status PMC
Volume 209
Issue 1
Start page 89
End page 103
Total pages 15
Place of publication Bethesda, MD., United States
Publisher Genetics Society of America
Language eng
Abstract 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 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 BayesCπ and BayesB methods allowing a broader range of mixture priors. Our methods allow a locus to affect any combination of traits, e.g., in a 5-trait analysis, the "restrictive" model only allows two situations, whereas ours allow all 32 situations. Further, we compare our methods to single-trait methods and the "restrictive" multi-trait formulation using real and simulated data. In the real data analysis, higher prediction accuracies were observed from both our new broad-based multi-trait methods and the "restrictive" formulation. The broad-based and restrictive multi-trait methods showed similar prediction accuracies. In the simulated data analysis, higher prediction accuracies to the "restrictive" method were observed from our general multi-trait methods for intermediate training population size. The software tool JWAS offers open-source routines to perform these analyses.
Keyword Bayesian regression
Genomic prediction
Mixture priors
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Institute for Molecular Bioscience - Publications
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Created: Wed, 14 Mar 2018, 10:05:46 EST