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Meta-analysis of genome-wide association studies (GWASs) has led to the discoveries of many common variants associated with complex human diseases. There is a growing recognition that identifying "causal" rare variants also requires large-scale meta-analysis. The fact that association tests with rare variants are performed at the gene level rather than at the variant level poses unprecedented challenges in the meta-analysis. First, different studies may adopt different gene-level tests, so the results are not compatible. Second, gene-level tests require multivariate statistics (i.e., components of the test statistic and their covariance matrix), which are difficult to obtain. To overcome these challenges, we propose to perform gene-level tests for rare variants by combining the results of single-variant analysis (i.e., p values of association tests and effect estimates) from participating studies. This simple strategy is possible because of an insight that multivariate statistics can be recovered from single-variant statistics, together with the correlation matrix of the single-variant test statistics, which can be estimated from one of the participating studies or from a publicly available database. We show both theoretically and numerically that the proposed meta-analysis approach provides accurate control of the type I error and is as powerful as joint analysis of individual participant data. This approach accommodates any disease phenotype and any study design and produces all commonly used gene-level tests. An application to the GWAS summary results of the Genetic Investigation of ANthropometric Traits (GIANT) consortium reveals rare and low-frequency variants associated with human height. The relevant software is freely available.

Original publication

DOI

10.1016/j.ajhg.2013.06.011

Type

Journal article

Journal

American journal of human genetics

Publication Date

08/2013

Volume

93

Pages

236 - 248

Addresses

Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.

Keywords

Genetic Investigation of ANthropometric Traits (GIANT) Consortium, Humans, Receptors, Odorant, Receptors, LDL, Gene Frequency, Genotype, Phenotype, Polymorphism, Single Nucleotide, Models, Genetic, Computer Simulation, Software, Genetic Variation, Genome-Wide Association Study