Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/88220
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Type: Journal article
Title: Meta-analysis of gene-level associations for rare variants based on single-variant statistics
Author: Hu, Y.
Berndt, S.
Gustafsson, S.
Ganna, A.
Genetic Investigation of ANthropometric Traits (GIANT) Consortium
Hirschhorn, J.
North, K.
Ingelsson, E.
Lin, D.
Citation: American Journal of Human Genetics, 2013; 93(2):236-248
Publisher: University of Chicago Press
Issue Date: 2013
ISSN: 0002-9297
1537-6605
Statement of
Responsibility: 
Yi-Juan Hu, Sonja I. Berndt, Stefan Gustafsson, Andrea Ganna, Genetic Investigation of ANthropometric Traits, GIANT, Consortium, Joel Hirschhorn, Kari E. North, Erik Ingelsson, and Dan-Yu Lin
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.
Keywords: No keywords specified
Description: Lyle J. Plamer is a member of the GIANT Consortium
Rights: © 2013 by The American Society of Human Genetics. All rights reserved
RMID: 0020137941
DOI: 10.1016/j.ajhg.2013.06.011
Appears in Collections:Translational Health Science publications

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