Online Program Home
My Program

Abstract Details

Activity Number: 40 - Recent Advances in Statistical Methods for Genome-Wide Association Studies
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #327097
Title: A Model-Based Clustering to Identify Disease-Associated SNPs
Author(s): Yan Xu* and Xuekui Zhang and Weiliang Qiu
Companies: University of Victoria and University of Victoria and Brigham and Women's Hosptial/Harvard Medical School
Keywords: Bayesian hierarchical models; Clustering; GWAS
Abstract:

Genome-wide association studies (GWASs) aim to detect genetic risk factors for complex human diseases by identifying disease-associated single-nucleotide polymorphisms (SNPs). The most commonly-used GWAS method is the SNP-wise-test approach, in which an association test is performed for each SNP, and then the p-values are adjusted for multiple testing. However, this approach is often lack of power after multiple testing adjustments due to a huge number (> 1 million) of tests in GWAS. To address this problem, we propose a model-based clustering via a mixture of Bayesian hierarchical models, which could borrow information across SNPs to group SNPs to different clusters having different mean genotype levels between cases and controls. Simulation studies and real data studies showed that the proposed model-based clustering outperformed SNP-wised-test approach.


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

Back to the full JSM 2018 program