title: Mendelian randomization analysis with multiple genetic variants using summarized data.
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 data give similar estimates and precision to the two-stage least squares method for individual-level data, even when there are gene-gene interactions. However, these summarized data methods overstate precision when variants are in linkage disequilibrium. If the P-value in a linear regression of the risk factor for each variant is less than 1×10⁻⁵, then weak instrument bias will be small. We use these methods to estimate the causal association of low-density lipoprotein cholesterol (LDL-C) on coronary artery disease using published data on five genetic variants. A 30% reduction in LDL-C is estimated to reduce coronary artery disease risk by 67% (95% CI: 54% to 76%). We conclude that Mendelian randomization investigations using summarized data from uncorrelated variants are similarly efficient to those using individual-level data, although the necessary assumptions cannot be so fully assessed.
authors.affiliation: Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Stephen
authors.initials: S
authors.lastname: Burgess
authors.forename: Adam
authors.initials: A
authors.lastname: Butterworth
authors.forename: Simon G
authors.initials: SG
authors.lastname: Thompson
mesh_terms: D015982:Bias; D008078:Cholesterol, LDL / Q000096:biosynthesis / Q000235:genetics / Q000378:metabolism; D003327:Coronary Disease / Q000235:genetics / Q000378:metabolism / Q000503:physiopathology; D005796:Genes / Q000235:genetics; D014644:Genetic Variation / Q000235:genetics*; D055106:Genome-Wide Association Study; D006801:Humans; D016018:Least-Squares Analysis; D016013:Likelihood Functions; D016014:Linear Models; D015810:Linkage Disequilibrium / Q000235:genetics; D057182:Mendelian Randomization Analysis / Q000379:methods*; D008957:Models, Genetic; D016017:Odds Ratio; D010641:Phenotype; D012307:Risk Factors
chemical_list: D008078:Cholesterol, LDL
keywords: Mendelian randomization; causal inference; genome-wide association study; instrumental variables; weak instruments