O'Connor LJ, Price AL. Distinguishing genetic correlation from causation across 52 diseases and complex traits.
Nat Genet 2018;
50:1728-1734. [PMID:
30374074 DOI:
10.1038/s41588-018-0255-0]
[Citation(s) in RCA: 212] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Accepted: 09/13/2018] [Indexed: 12/22/2022]
Abstract
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 [Formula: see text] and [Formula: see text] of marginal effect sizes for each trait; if trait 1 is causal for trait 2, then SNPs affecting trait 1 (large [Formula: see text]) will have correlated effects on trait 2 (large α1α2), 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.
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