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Using Negative Control Outcomes to Detect Selection Bias in Mendelian Randomization Studies
Apostolos Gkatzionis1, George Davey Smith1,2, Kate Tilling1,2
1MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Statistics in Medicine
|August 13, 2026
Summary
Negative control outcomes can detect selection bias in Mendelian randomization studies. These outcomes need to relate to confounders of the exposure and outcome, not necessarily selection itself.
Area of Science:
- Epidemiology
- Genetic Epidemiology
- Biostatistics
Background:
- Mendelian randomization (MR) uses genetic variants as instrumental variables to infer causal effects.
- MR is robust to confounding and reverse causation but susceptible to selection bias.
- Negative controls are used to detect biases in observational studies.
Purpose of the Study:
- To define conditions for using negative control outcomes in MR to detect selection bias.
- To investigate the utility of age and sex as negative control outcomes in MR.
- To apply these methods to real-world data and identify selection bias in common traits.
Main Methods:
- Focused on Mendelian randomization (MR) analyses.
- Defined criteria for negative control outcomes to detect selection bias.
- Utilized UK Biobank data for a real-data application involving 19 traits.
- Employed biological sex and natural hair color as negative control outcomes.
Main Results:
- Established that negative control outcomes must relate to confounders of the exposure and outcome.
- Demonstrated that the negative control's effect on selection is secondary.
- Identified selection bias in MR analyses of alcohol consumption, BMI, and educational attainment using UK Biobank data.
- Showed that age and sex can serve as valid negative control outcomes under specific conditions.
Conclusions:
- Negative control outcomes are valuable for detecting selection bias in Mendelian randomization.
- The proposed methodology can uncover hidden biases in genetic epidemiology studies.
- Findings highlight the importance of considering selection mechanisms in MR analyses of common traits.
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