Meta-Analysis of Gene Level Association Tests

Dajiang J. Liu; Gina M. Peloso; Xiaowei Zhan; Oddgeir Holmen; Matthew Zawistowski; Shuang Feng; Majid Nikpay; Paul L. Auer; Anuj Goel; He Zhang; Ulrike Peters; Martin Farrall; Marju Orho-Melander; Charles Kooperberg; Ruth McPherson; Hugh Watkins; Cristen J. Willer; Kristian Hveem; Olle Melander; Sekar Kathiresan; Gonçalo R. Abecasis

arXiv · 1305.1318

Meta-Analysis of Gene Level Association Tests

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Abstract

The vast majority of connections between complex disease and common genetic variants were identified through meta-analysis, a powerful approach that enables large samples sizes while protecting against common artifacts due to population structure, repeated small sample analyses, and/or limitations with sharing individual level data. As the focus of genetic association studies shifts to rare variants, genes and other functional units are becoming the unit of analysis. Here, we propose and evaluate new approaches for meta-analysis of rare variant association. We show that our approach retains useful features of single variant meta-analytic approaches and demonstrate its utility in a study of blood lipid levels in ~18,500 individuals genotyped with exome arrays.

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Dajiang J. Liu, Gina M. Peloso, Xiaowei Zhan, Oddgeir Holmen, Matthew Zawistowski, Shuang Feng, Majid Nikpay, Paul L. Auer, Anuj Goel, He Zhang, Ulrike Peters, Martin Farrall, Marju Orho-Melander, Charles Kooperberg, Ruth McPherson, Hugh Watkins, Cristen J. Willer, Kristian Hveem, Olle Melander, Sekar Kathiresan, Gonçalo R. Abecasis. 2013-05-06. Meta-Analysis of Gene Level Association Tests. https://arxiv.org/abs/1305.1318

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