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

Genetics: A Periodical Record of Investigations Bearing on Heredity and Variation

Kadir Kizilkaya^2¹  Rohan Fernando^5²  Hao Cheng^1³  Jian Zeng^3⁴  Dorian Garrick^4⁵ 

[1] Department of Animal Science, Adnan Menderes University, 9100 Aydin, Turkey^2;Department of Animal Science, Iowa State University, Ames, Iowa 50011-1050^5;Department of Animal Science, University of California Davis, California 95616^1;Program in Complex Trait Genomics, Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD 4072, Australia^3;School of Agriculture, Massey University, Palmerston North 4442 New Zealand^4

关键词: multi-trait;    mixture priors;    genomic prediction;    Bayesian regression;    pleiotropy;    GenPred;    Shared data resources;    Genomic Selection;   

DOI  :  10.1534/genetics.118.300650

学科分类：医学（综合）

来源: Genetics Society of America

【 摘 要 】

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 BayesC π , 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 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.

【 授权许可】

CC BY