Meta-analysis of gene-level associations for rare variants based on single-variant statistics.
American journal of human genetics 2013 ; 93: 236-48.
Hu YJ, Berndt SI, Gustafsson S, Ganna A, Genetic Investigation of ANthropometric Traits (GIANT) Consortium, Hirschhorn J, North KE, Ingelsson E, and Lin DY
DOI : 10.1016/j.ajhg.2013.06.011
PubMed ID : 23891470
PMCID : PMC3738834
URL : https://linkinghub.elsevier.com/retrieve/pii/S0002929713002802
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.