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Cell Press, American Journal of Human Genetics, 2(93), p. 236-248, 2013

DOI: 10.1016/j.ajhg.2013.06.011

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Meta-analysis of Gene-Level Associations for Rare Variants Based on Single-Variant Statistics

Journal article published in 2013 by Yi-Juan Hu, Stefan Gustafsson, Andrea Ganna ORCID, E. Thiering, Sonja I. Berndt, Joel Hirschhorn, Kari E. North, Erik Ingelsson, Dan-Yu Lin, Sonja I Berndt, Reedik Mägi, Eleanor Wheeler, Mary F. Feitosa, Anne E. Justice, Keri L. Monda and other authors.
This paper is available in a repository.
This paper is available in a repository.

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Abstract

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.