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A causal inference framework for estimating genetic variance and pleiotropy from GWAS summary data

Motivation Much of research in genome-wide association studies has only searched for significantly associated signals without explicitly removing unwanted source of variation. Confounder correction is a necessary step to reveal causal effects, but often skipped in a summary-based analysis. Results We present a novel causal inference algorithm that controls unwanted sources in genetic variance and covariance estimation tasks. We demonstrate substantially improved statistical power and accuracy in extensive simulations. In real-world applications on the UK biobank summary statistics data, our method recapitulates well-known pleiotropic modules, suggesting new insights into biobank-scale GWAS analysis. Contact YP (ypp@mit.edu) and MK (manoli@mit.edu)