Distinguishing genetic correlation from causation across 52 diseases and complex traits

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(alpha(2)(1)alpha(1)alpha(2) and E(alpha(2)(2)alpha(1)alpha(2)) of marginal effect sizes for each trait; if trait 1 is causal for trait 2, then SNPs affecting trait 1 (large alpha(2)(1)) will have correlated effects on trait 2 (large alpha(1)alpha(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.