title: Joint prediction of multiple quantitative traits using a Bayesian multivariate antedependence model.
abstract: Predicting organismal phenotypes from genotype data is important for preventive and personalized medicine as well as plant and animal breeding. Although genome-wide association studies (GWAS) for complex traits have discovered a large number of trait- and disease-associated variants, phenotype prediction based on associated variants is usually in low accuracy even for a high-heritability trait because these variants can typically account for a limited fraction of total genetic variance. In comparison with GWAS, the whole-genome prediction (WGP) methods can increase prediction accuracy by making use of a huge number of variants simultaneously. Among various statistical methods for WGP, multiple-trait model and antedependence model show their respective advantages. To take advantage of both strategies within a unified framework, we proposed a novel multivariate antedependence-based method for joint prediction of multiple quantitative traits using a Bayesian algorithm via modeling a linear relationship of effect vector between each pair of adjacent markers. Through both simulation and real-data analyses, our studies demonstrated that the proposed antedependence-based multiple-trait WGP method is more accurate and robust than corresponding traditional counterparts (Bayes A and multi-trait Bayes A) under various scenarios. Our method can be readily extended to deal with missing phenotypes and resequence data with rare variants, offering a feasible way to jointly predict phenotypes for multiple complex traits in human genetic epidemiology as well as plant and livestock breeding.
authors: Department of Animal Genetics, Breeding and Reproduction, China Agricultural University, Beijing, China.
authors: J
authors: J
authors: Jiang
authors: Department of Animal Genetics, Breeding and Reproduction, China Agricultural University, Beijing, China.
authors: Q
authors: Q
authors: Zhang
authors: Department of Animal and Avian Sciences, University of Maryland, College Park, MD, USA.
authors: L
authors: L
authors: Ma
authors: Institute of Animal Science, Chinese Academy of Agricultural Science, Beijing, China.
authors: J
authors: J
authors: Li
authors: Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada.
authors: Z
authors: Z
authors: Wang
authors: Department of Animal Genetics, Breeding and Reproduction, China Agricultural University, Beijing, China.
authors: J-F
authors: JF
authors: Liu
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D003198:Computer Simulation; D005260:Female; D016014:Linear Models; D008297:Male; D051379:Mice; D008957:Models, Genetic; D010641:Phenotype; D040641:Quantitative Trait Loci