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.

Figure 1

Power of T5 at the Nominal Significance Level α of 0.001 for Quantitative Traits σ² is the error variance for studies 2 and 3. For SV-I and SV-E, single-variant p values are based on the score test. Each power estimate is based on 10,000 replicates.

Figure 2

Power of T5 at the Nominal Significance Level α of 0.001 for Binary Traits Ratio is the case-control ratio for studies 2 and 3. For SV-I and SV-E, single-variant p values are based on the score test. Each power estimate is based on 10,000 replicates.

Figure 3

Quantile-Quantile Plots of −log₁₀(p Values) in the Meta-analysis of the GIANT Extreme Height Studies The genes that pass the genome-wide significance threshold by the SV-I T5 tests are marked. The p values <10⁻¹² are truncated.