Meta-analysis of sequencing studies with heterogeneous genetic associations.

Recent advances in sequencing technologies have made it possible to explore the influence of rare variants on complex diseases and traits. Meta-analysis is essential to this exploration because large sample sizes are required to detect rare variants. Several methods are available to conduct meta-analysis for rare variants under fixed-effects models, which assume that the genetic effects are the same across all studies. In practice, genetic associations are likely to be heterogeneous among studies because of differences in population composition, environmental factors, phenotype and genotype measurements, or analysis method. We propose random-effects models which allow the genetic effects to vary among studies and develop the corresponding meta-analysis methods for gene-level association tests. Our methods take score statistics, rather than individual participant data, as input and thus can accommodate any study designs and any phenotypes. We produce the random-effects versions of all commonly used gene-level association tests, including burden, variable threshold, and variance-component tests. We demonstrate through extensive simulation studies that our random-effects tests are substantially more powerful than the fixed-effects tests in the presence of moderate and high between-study heterogeneity and achieve similar power to the latter when the heterogeneity is low. The usefulness of the proposed methods is further illustrated with data from National Heart, Lung, and Blood Institute Exome Sequencing Project (NHLBI ESP). The relevant software is freely available.