Meta-Analysis of Gene Level Tests for Rare Variant Association
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Author
Liu, Dajiang J.
Zhan, Xiaowei
Holmen, Oddgeir L.
Zawistowski, Matthew
Feng, Shuang
Nikpay, Majid
Auer, Paul L.
Goel, Anuj
Zhang, He
Peters, Ulrike
Farrall, Martin
Orho-Melander, Marju
Kooperberg, Charles
McPherson, Ruth
Watkins, Hugh
Willer, Cristen J.
Hveem, Kristian
Melander, Olle
Kathiresan, Sekar
Abecasis, Gonçalo R.
Note: Order does not necessarily reflect citation order of authors.
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https://doi.org/10.1038/ng.2852Metadata
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Liu, D. J., G. M. Peloso, X. Zhan, O. L. Holmen, M. Zawistowski, S. Feng, M. Nikpay, et al. 2014. “Meta-Analysis of Gene Level Tests for Rare Variant Association.” Nature genetics 46 (2): 200-204. doi:10.1038/ng.2852. http://dx.doi.org/10.1038/ng.2852.Abstract
The vast majority of connections between complex disease and common genetic variants were identified through meta-analysis, a powerful approach that enables large sample 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 performing meta-analysis of rare variant association tests, including burden tests, weighted burden tests, variable threshold tests and tests that allow variants with opposite effects to be grouped together. 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.Other Sources
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3939031/pdf/Terms of Use
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http://nrs.harvard.edu/urn-3:HUL.InstRepos:12785981
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