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Distinguishing genetic correlation from causation across 52 diseases and complex traits
Distinguishing genetic correlation from causation across 52 diseases and complex traits
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Distinguishing genetic correlation from causation across 52 diseases and complex traits
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Distinguishing genetic correlation from causation across 52 diseases and complex traits
Distinguishing genetic correlation from causation across 52 diseases and complex traits
Journal Article

Distinguishing genetic correlation from causation across 52 diseases and complex traits

2018
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Overview
Mendelian randomization, a method to infer causal relationships, is confounded by genetic correlations reflecting shared etiology. We developed a model in which a latent causal variable mediates the genetic correlation; trait 1 is partially genetically causal for trait 2 if it is strongly genetically correlated with the latent causal variable, quantified using the genetic causality proportion. We fit this model using mixed fourth moments E ( α 1 2 α 1 α 2 ) and E α 2 2 α 1 α 2 of marginal effect sizes for each trait; if trait 1 is causal for trait 2, then SNPs affecting trait 1 (large α 1 2 ) will have correlated effects on trait 2 (large α 1 α 2 ), but not vice versa. In simulations, our method avoided false positives due to genetic correlations, unlike Mendelian randomization. Across 52 traits (average n  = 331,000), we identified 30 causal relationships with high genetic causality proportion estimates. Novel findings included a causal effect of low-density lipoprotein on bone mineral density, consistent with clinical trials of statins in osteoporosis. This study presents a new latent causal variable (LCV) model that distinguishes between genetic correlation and causation. Applying LCV to genome-wide association summary statistics for 52 traits identified genetically causal effects for 59 pairs of traits.