Meta-analysis of summary statistics from quantitative trait association studies with unknown sample overlap -conference paper abstract-
AuthorsArthur Gilly, Ioanna Tachmazidou and Eleftheria Zeggini
Lin and Sullivan provide a theoretical framework for case-control and quantitative trait meta-analysis that accounts for a known number of overlapping samples. Province and Borecki use tetrachoric correlation to estimate sample relatedness or overlap from summary statistics. However this p-value based method does not account for differences in genetic effect directions, nor does it produce a summary of these effects. We adapt this estimator and integrate it with Lin and Sullivan's inverse-variance based method to provide a meta-analysis of both effect sizes and p-values.
Using simulations based on GWAS genotypes from 10,000 individuals from the UKHLS cohort, we show that this method maintains the type-I error rate under an average 7% at a 5% significance threshold, even with very large sample overlaps, as opposed to a linear increase of 0.12% per 1% sample overlap using an uncorrected meta-analysis. Both the p-value correction and overlap estimation were robust to sample size variation and to MAF filtering of the input dataset. We demonstrate that tetrachoric correlation can estimate sample overlap with 95% accuracy.
We implement our method in a software package that scales to genome-wide sequencing data, and can control for unknown sample relatedness or overlap in meta-analysis of up to 15 studies.
Volume and page numbers39, 529-599