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

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.

journal_name

Am J Hum Genet

authors

Hu YJ,Berndt SI,Gustafsson S,Ganna A,Genetic Investigation of ANthropometric Traits (GIANT) Consortium.,Hirschhorn J,North KE,Ingelsson E,Lin DY

doi

10.1016/j.ajhg.2013.06.011

subject

Has Abstract

pub_date

2013-08-08 00:00:00

pages

236-48

issue

2

eissn

0002-9297

issn

1537-6605

pii

S0002-9297(13)00280-2

journal_volume

93

pub_type

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