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Published on: June 21, 2018
Using the global randomization test as a Mendelian randomization falsification test for the exclusion restriction
Louise A C Millard1,2, George Davey Smith3,4, Kate Tilling3,4
1MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK. louise.millard@bristol.ac.uk.
Abstract:
Mendelian randomization may give biased causal estimates if the instrument affects the outcome not solely via the exposure of interest (violating the exclusion restriction assumption). We demonstrate use of a global randomization test as a falsification test for the exclusion restriction assumption. Using simulations, we explored the statistical power of the randomization test to detect an association between a genetic instrument and a covariate set due to (a) selection bias or (b) horizontal pleiotropy, compared to three approaches examining associations with individual covariates: (i) Bonferroni correction for the number of covariates, (ii) correction for the effective number of independent covariates, and (iii) an r2 permutation-based approach. We conducted proof-of-principle analyses in UK Biobank, using CRP as the exposure and coronary heart disease (CHD) as the outcome. In simulations, power of the randomization test was higher than the other approaches for detecting selection bias when the correlation between the covariates was low (r2 < 0.1), and at least as powerful as the other approaches across all simulated horizontal pleiotropy scenarios. In our applied example, we found strong evidence of selection bias using all approaches (e.g., global randomization test p < 0.002). We identified 51 of the 58 CRP genetic variants as horizontally pleiotropic, and estimated effects of CRP on CHD attenuated somewhat to the null when excluding these from the genetic risk score (OR = 0.96 [95% CI: 0.92, 1.00] versus 0.97 [95% CI: 0.90, 1.05] per 1-unit higher log CRP levels). The global randomization test can be a useful addition to the MR researcher's toolkit.
Insights
A new global randomization test helps Mendelian randomization (MR) studies detect bias from selection or horizontal pleiotropy. This falsification test proved powerful in simulations and real-world data, improving causal estimate reliability.
Area of Science:
- Genetics and Epidemiology
- Statistical Genetics
- Causal Inference
Background:
- Mendelian randomization (MR) relies on instrumental variables, assuming they affect the outcome only through the exposure.
- Violation of the exclusion restriction assumption can lead to biased causal estimates in MR studies.
- Identifying and correcting for such violations is crucial for reliable genetic epidemiology research.
Purpose of the Study:
- To introduce and evaluate a global randomization test as a falsification method for the exclusion restriction assumption in MR.
- To compare the statistical power of the global randomization test against existing methods for detecting selection bias and horizontal pleiotropy.
- To demonstrate the practical application of the global randomization test using real-world data.
Main Methods:
- Simulations were conducted to assess the power of the global randomization test in detecting selection bias and horizontal pleiotropy.
- The test's performance was compared against Bonferroni correction, effective number of covariates correction, and an r2 permutation approach.
- Proof-of-principle analyses were performed using UK Biobank data, with C-reactive protein (CRP) as the exposure and coronary heart disease (CHD) as the outcome.
Main Results:
- The global randomization test showed higher power than other methods for detecting selection bias when covariate correlations were low.
- It demonstrated comparable or superior power across all simulated horizontal pleiotropy scenarios.
- Applied analyses revealed significant evidence of selection bias, identified 51 pleiotropic variants for CRP, and showed attenuated CRP-CHD effects after their exclusion.
Conclusions:
- The global randomization test is a valuable tool for MR researchers to falsify the exclusion restriction assumption.
- It effectively detects selection bias and horizontal pleiotropy, enhancing the validity of MR causal estimates.
- This method contributes to more robust causal inference in genetic epidemiology.
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