A general framework for meta-analyzing dependent studies with overlapping subjects in association mapping

Abstract

Meta-analysis strategies have become critical to augment power of genome-wide association studies (GWAS). To reduce genotyping or sequencing cost, many studies today utilize shared controls, and these individuals can inadvertently overlap among multiple studies. If these overlapping individuals are not taken into account in meta-analysis, they can induce spurious associations. In this article, we propose a general framework for adjusting association statistics to account for overlapping subjects within a meta-analysis. The key idea of our method is to transform the covariance structure of the data, so it can be used in downstream analyses. As a result, the strategy is very flexible and allows a wide range of meta-analysis methods, such as the random effects model, to account for overlapping subjects. Using simulations and real datasets, we demonstrate that our method has utility in meta-analyses of GWAS, as well as in a multi-tissue mouse expression quantitative trait loci (eQTL) study where our method increases the number of discovered eQTL by up to 19% compared with existing methods.

Genome-Wide Meta-Analyses of Breast, Ovarian, and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by at Least Two Cancer Types

“…To account for correlation between the breast and ovarian cancer studies due to the 8,564 controls shared between them, we repeated the meta-analysis for the 13 index variants using a statistical adjustment for studies with overlapping controls that required only summary statistics and exact sample counts contributing to the association at each variant from the corresponding data sets (21). Two of the variants fell just short while 11 remained at P < 10 −8 after this adjustment (Supplementary Table S3, P adjusted column).…”

“…To account for correlation between the data sets due to overlapping controls, we applied a general statistical decoupling framework that involves adjusting the standard errors of each variant from the dependent data sets using a correlation matrix generated from the sample overlap counts (21). The data sets can then be analyzed as independent data sets.…”

Habitual coffee consumption and risk of type 2 diabetes, ischemic heart disease, depression and Alzheimer’s disease: a Mendelian randomization study

“…Associations of coffee consumption with these outcomes were obtained using weighted generalized linear regression for correlated SNPs62, with a correlation matrix to account for correlation between genetic variants obtained from SNAP using the same catalog as used in the GWAS of the outcome62. Given the two IHD case-control studies overlap (57.5% of the cases and 40.1% of controls)47, we also combined their results for IHD accounting for this overlap using the Lin and Sullivan approach63. Estimates are shown with all genome-wide significant SNPs with potentially pleiotropic effects included and excluded.…”

Genetic predisposition to mosaic Y chromosome loss in blood is associated with genomic instability in other tissues and susceptibility to non-haematological cancers

“…First, the tetrachoric correlation of binary transformed Z-scores was used to estimate the correlation between individual-cancer summary statistics that is attributable to control sample overlap 84 . Second, individual-cancer summary statistic standard errors were decoupled to account for the estimated correlation 85 and third, the METASOFT software 86 was used to perform fixed effect inverse-variance weighted meta-analyses for the combination of four cancers.…”