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

Y.J. Hu, S.I. Berndt, S. Gustafsson, A. Ganna, J.N. Hirschhorn, K.E. North, E. Ingelsson, D.-Y. Lin, J.J. Hottenga, G. Willemsen, D.I. Boomsma, B.W.J.H. Penninx, C.M. van Duijn

Research output: Contribution to JournalArticleAcademicpeer-review

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. © 2013 The American Society of Human Genetics.
Original languageEnglish
Pages (from-to)236-248
JournalAmerican Journal of Human Genetics
Volume93
Issue number2
Early online date8 Aug 2013
DOIs
Publication statusPublished - 2013

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