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Meta-analysis of gene-level associations for rare variants based on single-variant statistics
Hu, Yi-Juan Berndt, Sonja Gustafsson, Stefan Ganna, Andrea Hirschhorn, Joel North, Kari Ingelsson, Erik Lin, Dan-Yu Lorentzon, Karl Mattias al, et
Hu, Yi-Juan
Berndt, Sonja
Gustafsson, Stefan
Ganna, Andrea
Hirschhorn, Joel
North, Kari
Ingelsson, Erik
Lin, Dan-Yu
Lorentzon, Karl Mattias
al, et
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
GIANT, statistics, association tests, gene level, variant level
Date
2013
Type
Journal article
Journal
Book
Volume
93
Issue
2
Page Range
1-13
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ACU Department
Mary MacKillop Institute for Health Research
Faculty of Health Sciences
Faculty of Health Sciences
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© 2013 by The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.