Department of Biostatistics Seminar/Workshop Series

A General Framework for Meta-Analysis of Sequencing Studies

Zhengzheng Tang

Research Assistant, Department of Biostatistics, University of North Carolina at Chapel Hill

Recent advances in sequencing technologies have made it possible to explore the influence of rare variants on complex diseases and traits. Meta-analysis is essential to this exploration because large sample sizes are required to detect rare variants. Several methods are available to conduct meta-analysis for rare variants under fixed-effects models, which assume that the genetic effects are the same across all studies. In practice, genetic associations are likely to be heterogeneous among studies because of differences in population composition, environmental factors, phenotype and genotype measurements, or analysis method. We propose a general framework for meta-analysis of sequencing studies that allows the genetic effects to vary among studies. We produce the fixed-effects and random-effects versions of all commonly used gene-level association tests, including burden, variable threshold and variance-component tests. Our methods take score statistics, rather than individual participant data, as input and thus can accommodate any study designs and any phenotypes. We demonstrate through extensive simulation studies that our tests are more powerful than the existing ones in a wide range of practical situations. The usefulness of the proposed methods is further illustrated with data from the National Heart, Lung, and Blood Institute Exome Sequencing Project.
Topic revision: r1 - 11 Apr 2014, NanaKwarteng
 

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