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

Park, Y.; He, L.; Kellis, M.

doi:10.1101/531673

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bioRxiv · 10.1101/531673

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

Park, Y.·He, L.·Kellis, M.

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Abstract

MotivationMuch 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. ResultsWe 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. ContactYP (ypp@mit.edu) and MK (manoli@mit.edu)

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Park, Y., He, L., Kellis, M.. 2019-01-28. A causal inference framework for estimating genetic variance and pleiotropy from GWAS summary data. https://doi.org/10.1101/531673

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