Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/88220
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dc.contributor.authorHu, Y.-
dc.contributor.authorBerndt, S.-
dc.contributor.authorGustafsson, S.-
dc.contributor.authorGanna, A.-
dc.contributor.authorGenetic Investigation of ANthropometric Traits (GIANT) Consortium,-
dc.contributor.authorHirschhorn, J.-
dc.contributor.authorNorth, K.-
dc.contributor.authorIngelsson, E.-
dc.contributor.authorLin, D.-
dc.date.issued2013-
dc.identifier.citationAmerican Journal of Human Genetics, 2013; 93(2):236-248-
dc.identifier.issn0002-9297-
dc.identifier.issn1537-6605-
dc.identifier.urihttp://hdl.handle.net/2440/88220-
dc.descriptionLyle J. Plamer is a member of the GIANT Consortium-
dc.description.abstractMeta-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.-
dc.description.statementofresponsibilityYi-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-
dc.language.isoen-
dc.publisherUniversity of Chicago Press-
dc.rights© 2013 by The American Society of Human Genetics. All rights reserved-
dc.source.urihttp://dx.doi.org/10.1016/j.ajhg.2013.06.011-
dc.subjectNo keywords specified-
dc.titleMeta-analysis of gene-level associations for rare variants based on single-variant statistics-
dc.typeJournal article-
dc.identifier.doi10.1016/j.ajhg.2013.06.011-
pubs.publication-statusPublished-
Appears in Collections:Aurora harvest 7
Translational Health Science publications

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