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Hu, Y.J.* ; Berndt, S.I.* ; Gustafsson, S.* ; Ganna, A.* ; GIANT Consortium (Heid, I.M. ; Thiering, E. ; Grallert, H. ; Heinrich, J. ; Illig, T.) ; Hirschhorn, J.* ; North, K.E.* ; Ingelsson, E.* ; Lin, D.Y.*

Meta-analysis of gene-level associations for rare variants based on single-variant statistics.

Am. J. Hum. Genet. 93, 236-248 (2013)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
ISSN (print) / ISBN 0002-9297
e-ISSN 1537-6605
Zeitschrift American Journal of Human Genetics, The
Quellenangaben Band: 93, Heft: 2, Seiten: 236-248 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort New York, NY
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Genetic Epidemiology (IGE)
Institute of Epidemiology II (EPI2)
CCG Biomarker for the subclassification of T2DM (KKG-KDB)
Research Unit Molecular Epidemiology (AME)