Frontiers in GeneticsGeneticsPeer-reviewed journal article
Use of multivariable Mendelian randomization to address biases due to competing risk before recruitment
Catherine Mary SchoolingiD, PRISCILLA M. LOPEZiD, Zhao YangiD, Jie V. ZhaoiD, Shiu Lun Au YeungiD, Jian V. HuangiD
Abstract
BackgroundMendelian randomization (MR) provides unconfounded estimates. MR is open to selection bias particularly when the underlying sample is selected on surviving the genetically instrumented exposure and other conditions that share etiology with the outcome (competing risk before recruitment). Few methods to address this bias exist. MethodsWe use directed acyclic graphs to show this selection bias can be addressed by adjusting for common causes of survival and outcome. We use multivariable MR to obtain a corrected MR estimate, specifically, the effect of statin use on ischemic stroke, because statins affect survival and stroke typically occurs later in life than ischemic heart disease so is open to competing risk. ResultsIn univariable MR the genetically instrumented effect of statin use on ischemic stroke was in a harmful direction in MEGASTROKE and the UK Biobank (odds ratio (OR) 1.33, 95% confidence interval (CI) 0.80 to 2.20). In multivariable MR adjusted for major causes of survival and ischemic stroke, (blood pressure, body mass index and smoking initiation) the effect of statin use on stroke in the UK Biobank was as expected (OR 0.81, 95% CI 0.68 to 0.98) with a Q-statistic indicating absence of genetic pleiotropy or selection bias, but not in MEGASTROKE. ConclusionMR studies concerning late onset chronic conditions with shared etiology based on samples recruited in later life need to be conceptualized within a mechanistic understanding, so as to any identify potential bias due to competing risk before recruitment, and to inform the analysis and interpretation.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- research focus: benchmarking
- approach: statistical model
- approach: Bayesian inference
- Citations
- 102
- FWCI
- 12.59
- Field percentile
- top 1%
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