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Activity Number: 317 - Integration Approaches and Methods for Deciphering Genotype-Phenotype Mapping Toward Precision Medicine
Type: Invited
Date/Time: Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #321973 View Presentation
Title: Robust Genetic Prediction of Complex Traits with the Latent Dirichlet Process Regression Models
Author(s): Xiang Zhou* and Ping Zeng
Companies: University of Michigan and University of Michigan
Keywords: prediction ; gene set test ; Dirichlet process ; genomic selection ; polygenic models ; complex traits

Accurate genetic prediction of complex traits requires the development of polygenic methods to model all SNPs jointly. Previous polygenic methods make parametric assumptions on the SNP effect size distribution. However, depending on how well the assumed effect size distribution matches the unknown truth, different polygenic methods can perform well for different traits. To enable robust phenotype prediction across a range of traits, we develop a novel polygenic model with a flexible assumption on the effect size distribution. We refer to our model as the latent Dirichlet Process Regression (DPR). DPR relies on the Dirichlet process to assign a prior on the effect size distribution itself, is non-parametric in nature, and is capable of inferring the effect size distribution from the data at hand. Because of the flexible modeling assumption, DPR is able to adapt to a broad spectrum of genetic architectures and achieves robust predictive performance for a variety of complex traits. We illustrate the benefits of DPR by applying it to predict gene expressions using cis-SNPs, to conduct PrediXcan based gene set test, and to perform genomic selection of five traits across three species.

Authors who are presenting talks have a * after their name.

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