Meta-analysis of gene-level associations for rare variants based on single-variant statistics.
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.
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.
- 2007-0224: Whole Genome Association Study (GWAS) of Bladder Cancer (Stella Koutros)
- 2007-0253: Genome-wide association study of renal cell carcinoma: replication scan (Mark Purdue - 2008)
- 2007-0004: A Whole Genome Association Study (WGAS) of Lung Cancer and Smoking (Neil Caporaso - 2007)
- 2007-0038: Genetic Determinants of Height and BMI in the PLCO Trial and Collaborating Studies (Sonja Berndt - 2007)
- 2006-0306: Whole Genome Scan of Incident Pancreatic Cancer in the Cohort Consortium (PanScan) (Rachael Stolzenberg-Solomon - 2006)