Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models.

ABSTRACT: Using genotype data to perform accurate genetic prediction of complex traits can facilitate genomic selection in animal and plant breeding programs, and can aid in the development of personalized medicine in humans. Because most complex traits have a polygenic architecture, accurate genetic prediction often requires modeling all genetic variants together via polygenic methods. Here, we develop such a polygenic method, which we refer to as the latent Dirichlet process regression model. Dirichlet process regression is non-parametric in nature, relies on the Dirichlet process to flexibly and adaptively model the effect size distribution, and thus enjoys robust prediction performance across a broad spectrum of genetic architectures. We compare Dirichlet process regression with several commonly

SUBMITTER: Zeng P 

PROVIDER: S-EPMC5587666 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature