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Heredity (Edinb). 2015 Jul;115(1):29-36. doi: 10.1038/hdy.2015.9. Epub 2015 Apr 15.

Joint prediction of multiple quantitative traits using a Bayesian multivariate antedependence model.

Author information

1
Department of Animal Genetics, Breeding and Reproduction, China Agricultural University, Beijing, China.
2
Department of Animal and Avian Sciences, University of Maryland, College Park, MD, USA.
3
Institute of Animal Science, Chinese Academy of Agricultural Science, Beijing, China.
4
Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada.

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.

PMID:
25873147
PMCID:
PMC4815501
DOI:
10.1038/hdy.2015.9
[Indexed for MEDLINE]
Free PMC Article

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