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Activity Number: 644
Type: Topic Contributed
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #319681 View Presentation
Title: Meta-Analysis for Discovering Rare-Variant Associations
Author(s): Zheng-Zheng Tang*
Companies: Vanderbilt University
Keywords: gene-level test ; meta-analysis ; sequencing studies ; rare-variant associations

There is heightened interest in using next-generation sequencing technologies to identify rare variants that influence complex human diseases and traits. Meta-analysis is essential to this endeavor because large sample sizes are required to detect associations with rare variants. In this work, we provide a comprehensive overview of statistical methods for meta-analysis of sequencing studies to discover rare-variant associations. Specifically, we discuss the calculation of relevant summary statistics from participating studies, the construction of gene-level association tests, the choice of transformation for quantitative traits, the use of fixed-effects versus random-effects models, and the removal of shadow association signals through conditional analysis. We also show that meta-analysis based on properly calculated summary statistics is as powerful as joint analysis of individual-participant data. In addition, we demonstrate the performance of different meta-analysis methods using both simulated and empirical data.

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

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