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Bias due to participant overlap in two-sample Mendelian randomization
Stephen Burgess1, Neil M Davies2,3, Simon G Thompson4
1Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. sb452@medschl.cam.ac.uk.
Genetic Epidemiology
|September 15, 2016
Summary
Mendelian randomization with overlapping samples introduces bias. Simulations show bias is proportional to overlap, but can be avoided in case-control studies by excluding cases from risk factor analysis.
Area of Science:
- Epidemiology
- Statistical Genetics
- Biostatistics
Background:
- Mendelian randomization (MR) analyses commonly use summarized data from genetic consortia.
- One-sample MR with weak instruments is biased towards observational associations.
- Two-sample MR with non-overlapping samples is less biased, tending towards the null.
Purpose of the Study:
- To investigate bias and Type 1 error inflation in MR due to sample overlap.
- To evaluate bias in both continuous and binary (case-control) outcomes.
- To provide recommendations for consortia releasing genetic association data.
Main Methods:
- Simulation studies were conducted to model sample overlap.
- Analyses considered continuous outcomes and binary outcomes in a case-control setting.
- The impact of varying proportions of sample overlap was assessed.
Main Results:
- Bias in continuous outcomes is a linear function of sample overlap proportion.
- For a null causal effect, 50% overlap yields 5% relative bias, 30% overlap yields 3% relative bias.
- In case-control studies, bias is avoided if risk factor data are from controls only; bias is similar to continuous outcomes if cases are included.
Conclusions:
- Sample overlap in Mendelian randomization introduces bias, linearly related to the overlap proportion.
- Case-control studies can yield unbiased estimates if risk factor data exclude cases.
- Public data releases should specify estimates excluding cases from case-control samples to mitigate bias.
Keywords:
Mendelian randomizationaggregated datainstrumental variablessummarized dataweak instrument biasMore Related Videos
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