{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["O'Connor LJ"],"funding":["NIMH NIH HHS","NHGRI NIH HHS","NCI NIH HHS","NIH HHS"],"pagination":["1728-1734"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6684375"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["50(12)"],"pubmed_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 ?1?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."],"journal":["Nature genetics"],"pubmed_title":["Distinguishing genetic correlation from causation across 52 diseases and complex traits."],"pmcid":["PMC6684375"],"funding_grant_id":["R01 MH107649","R01 MH101244","T32 HG002295","U01 CA194393"],"pubmed_authors":["Price AL","O'Connor LJ"],"additional_accession":[]},"is_claimable":false,"name":"Distinguishing genetic correlation from causation across 52 diseases and complex traits.","description":"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 ?1?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.","dates":{"release":"2018-01-01T00:00:00Z","publication":"2018 Dec","modification":"2020-11-01T08:30:14Z","creation":"2019-08-12T07:06:54Z"},"accession":"S-EPMC6684375","cross_references":{"pubmed":["30374074"],"doi":["10.1038/s41588-018-0255-0"]}}