RT Journal Article
SR Electronic
T1 Non-Parametric Genetic Prediction of Complex Traits with Latent Dirichlet Process Regression Models
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 149609
DO 10.1101/149609
A1 Ping Zeng
A1 Xiang Zhou
YR 2017
UL http://biorxiv.org/content/early/2017/06/13/149609.abstract
AB 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 (DPR). DPR 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 DPR with several commonly used prediction methods with simulations. We further apply DPR to predict gene expressions, to conduct PrediXcan based gene set test, to perform genomic selection of four traits in two species, and to predict eight complex traits in a human cohort.