Meta-analysis of gene-level tests for rare variant association

Dajiang J. Liu, Gina M. Peloso, Xiaowei Zhan, Oddgeir L. 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 KathiresanGonçalo R. Abecasis

Research output: Contribution to journalArticlepeer-review

139 Scopus citations


The majority of reported complex disease associations for common genetic variants have been identified through meta-analysis, a powerful approach that enables the use of large sample sizes while protecting against common artifacts due to population structure and repeated small-sample analyses sharing individual-level data. As the focus of genetic association studies shifts to rare variants, genes and other functional units are becoming the focus 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 from single-variant meta-analysis approaches and demonstrate its use in a study of blood lipid levels in ∼18,500 individuals genotyped with exome arrays.

Original languageEnglish (US)
Pages (from-to)200-204
Number of pages5
JournalNature Genetics
Issue number2
StatePublished - Feb 2014

All Science Journal Classification (ASJC) codes

  • Genetics


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