Meta-Analysis of Gene Level Association Tests
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.
Keywords
Cite
@article{arxiv.1305.1318,
title = {Meta-Analysis of Gene Level Association Tests},
author = {Dajiang J. Liu and Gina M. Peloso and Xiaowei Zhan and Oddgeir Holmen and Matthew Zawistowski and Shuang Feng and Majid Nikpay and Paul L. Auer and Anuj Goel and He Zhang and Ulrike Peters and Martin Farrall and Marju Orho-Melander and Charles Kooperberg and Ruth McPherson and Hugh Watkins and Cristen J. Willer and Kristian Hveem and Olle Melander and Sekar Kathiresan and Gonçalo R. Abecasis},
journal= {arXiv preprint arXiv:1305.1318},
year = {2013}
}