A Model-Based Clustering to Identify Disease-Associated SNPs

• Activity Number: 87 - Invited ePoster Session: a Statistical Smörgåsbord
• Type: Invited
• Date/Time: Sunday, July 29, 2018 : 8:30 PM to 10:30 PM
• Sponsor: WNAR
• Author(s): Li Xing* and Xuekui Zhang and Yan Xu and Weiliang Qiu
• Companies: University of Victoria and University of Victoria and University of Victoria and Brigham and Women's Hosptial/Harvard Medical School
• Keywords: GWASs; multiple testing; model-based clustering; Bayesian hierarchical models; mixture model; EM algorithm

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.