{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Burgess S"],"funding":["British Heart Foundation","Medical Research Council","National Institute for Health Research (NIHR)","Wellcome Trust"],"pagination":["658-65"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC4377079"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["37(7)"],"pubmed_abstract":["Genome-wide association studies, which typically report regression coefficients summarizing the associations of many genetic variants with various traits, are potentially a powerful source of data for Mendelian randomization investigations. We demonstrate how such coefficients from multiple variants can be combined in a Mendelian randomization analysis to estimate the causal effect of a risk factor on an outcome. The bias and efficiency of estimates based on summarized data are compared to those based on individual-level data in simulation studies. We investigate the impact of gene-gene interactions, linkage disequilibrium, and 'weak instruments' on these estimates. Both an inverse-variance weighted average of variant-specific associations and a likelihood-based approach for summarized dat"],"journal":["Genetic epidemiology"],"pubmed_title":["Mendelian randomization analysis with multiple genetic variants using summarized data."],"pmcid":["PMC4377079"],"funding_grant_id":["MR/L003120/1","NF-SI-0512-10165","SP/08/007/23628","RG/08/014/24067"],"pubmed_authors":["Thompson SG","Burgess S","Butterworth A"],"additional_accession":[]},"is_claimable":false,"name":"Mendelian randomization analysis with multiple genetic variants using summarized data.","description":"Genome-wide association studies, which typically report regression coefficients summarizing the associations of many genetic variants with various traits, are potentially a powerful source of data for Mendelian randomization investigations. We demonstrate how such coefficients from multiple variants can be combined in a Mendelian randomization analysis to estimate the causal effect of a risk factor on an outcome. The bias and efficiency of estimates based on summarized data are compared to those based on individual-level data in simulation studies. We investigate the impact of gene-gene interactions, linkage disequilibrium, and 'weak instruments' on these estimates. Both an inverse-variance weighted average of variant-specific associations and a likelihood-based approach for summarized dat","dates":{"release":"2013-01-01T00:00:00Z","publication":"2013 Nov","modification":"2025-04-19T00:45:56.595Z","creation":"2019-03-27T01:48:56Z"},"accession":"S-EPMC4377079","cross_references":{"pubmed":["24114802"],"doi":["10.1002/gepi.21758"]}}