Improved score statistics for meta-analysis in single-variant and gene-level association studies

• Type of Material: Article
• Authors: Jingjing Yang, Emory University; Sai Chen, University of Michigan; Goncalo Abecasis, University of Michigan
• Language: English
• Date: 2018-06-01
• Publisher: Wiley: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2018 WILEY PERIODICALS, INC
• Final Published Version (URL): http://dx.doi.org/10.1002/gepi.22123
• Title of Journal or Parent Work: Genetic Epidemiology
• ISSN: 0741-0395
• Volume: 42
• Issue: 4
• Start Page: 333
• End Page: 343
• Grant/Funding Information: This work was supported by the National Institutes of Health (NIH) grant R01HG007022; and the start-up fund provided by Department of Human Genetics at the Emory University School of Medicine.
• Keywords: RISK; RARE VARIANTS; population stratification; Life Sciences & Biomedicine; score statistics; Genetics & Heredity; unbalanced studies; LOCI; Mathematical & Computational Biology; multi-ethnic studies; GENOME-WIDE ASSOCIATION; Science & Technology; meta-analysis
• Research Categories: Biology, Biostatistics; Biology, Genetics

Abstract

Meta-analysis is now an essential tool for genetic association studies, allowing them 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 the power loss problem by the standard meta-analysis methods for unbalanced studies, and further propose novel meta-analysis methods performing equivalently to the joint analysis under both balanced and unbalanced settings. We derive improved meta-score-statistics that can accurately approximate the joint-score-statistics with combined individual-level data, 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. In the simulated gene-level association studies under unbalanced settings, our method recovered up to 85% power loss caused by the standard methods. We further showed the power gain of our methods in gene-level tests with 26 unbalanced studies of age-related macular degeneration. In addition, we took the meta-analysis of three unbalanced studies of type 2 diabetes as an example to discuss the challenges of meta-analyzing multi-ethnic samples. In summary, our improved meta-score-statistics with corrections for population stratification 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.