Improved Score Statistics for Meta-analysis in Single-variant and Gene-level Association Studies

Abstract

Meta-analysis is now an essential tool for genetic association studies, allowing these to combine large studies and greatly accelerating the pace of genetic discovery. Although the standard meta-analysis methods perform equivalently as the more cumbersome joint analysis under ideal settings, they result in substantial power loss under unbalanced settings with various case-control ratios. Here, we investigate why the standard meta-analysis methods lose power under unbalanced settings, and further propose a novel meta-analysis method that performs as efficiently as joint analysis under general settings. Our proposed method can accurately approximate the score statistics obtainable by joint analysis, for both linear and logistic regression models, with and without covariates. In addition, we propose a novel approach to adjust for population stratification by correcting for known population structures through minor allele frequencies (MAFs). In the simulated gene-level association studies under unbalanced settings, our method recovered up to 85% power loss caused by the standard method. We further showed the power gain of our method in gene-level association studies with 26 unbalanced real studies of Age-related Macular Degeneration (AMD). In addition, we took the meta-analysis of three studies of type 2 diabetes (T2D) as an example to discuss the challenges of meta-analyzing multi-ethnic samples. In summary, we propose improved single-variant score statistics in meta-analysis, requiring \"accurate\" population-specific MAFs for multi-ethnic studies. These improved score statistics can be used to construct both single-variant and gene-level association studies, providing a useful framework for ensuring well-powered, convenient, cross-study analyses.