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Activity Number: 140 - Frontiers of Statistical Genetics: Genomics, Transcriptomics, and PheWAS
Type: Invited
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: WNAR
Abstract #300222
Title: Weighted Hypothesis Testing Accounting for Correlated Predictors
Author(s): Li Hsu*
Companies: Fred Hutchinson Cancer Research Center, USA
Keywords: multiple comparisons; GWAS; weighted hypothesis testing

Genome-wide association studies (GWAS) have genotyped millions of SNPs on tens of thousands of individuals, providing a comprehensive investigation of genetic association with various traits including disease phenotypes, gene expression, and methylation. However, the power for genome-wide discovery is hindered by the need to account for multiple comparisons of testing millions of variants. In this talk, I will present the use of screening statistics to prioritize variants for testing, and cast this into an independent weighted hypothesis testing framework. The idea is to allocate the type I error to each hypothesis differently according to the screening statistic such that the power is maximized. A unique and ubiquitous feature of GWAS data is that genetic variants are correlated locally throughout the genome. Treating them as independent predictors yields conservative type I error control, thereby losing power. We extend the framework to account for these correlations, and show by simulation and a real data example that the proposed approach improves power and yield more findings than existing approaches.

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

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