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Activity Number: 245
Type: Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #320763 View Presentation
Title: A Powerful Statistical Procedure for Pathway-Based Meta-Analysis Using Summary Statistics
Author(s): Han Zhang* and William Wheeler and Paula L. Hyland and Yifan Yang and Jianxin Shi and Nilanjan Chatterjee and Kai Yu
Companies: National Cancer Institute and Information Management Services and National Cancer Institute and University of Kentucky and National Cancer Institute and The Johns Hopkins University and National Cancer Institute
Keywords: Summary statistics ; Pathway association analysis ; GWAS ; Meta-analysis ; ARTP

Meta-analysis of multiple genome-wide association studies (GWAS) has become an effective approach for detecting single nucleotide polymorphism (SNP) associations with complex traits. However, it is difficult to integrate the readily accessible SNP-level summary statistics from a meta-analysis into more powerful multi-marker testing procedures, which generally require individual-level genetic data. We developed a general procedure called Summary based Adaptive Rank Truncated Product (sARTP) for conducting gene and pathway meta-analysis that uses only SNP-level summary statistics in combination with genotype correlation estimated from a panel of individual-level genetic data. We demonstrated the validity and power advantage of sARTP over existing methods through comprehensive simulated studies. We carried out a pathway analysis of 4713 candidates on their association with type II diabetes using summary data from two largest studies by far with European ancestry. Our analysis identified 43 globally significant pathways after Bonferroni correction. We also trans-ethnically validated some of our finding on additional summary data generated from eastern Asian populations.

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

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