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A data-adaptive method for investigating effect heterogeneity with high-dimensional covariates in Mendelian
Haodong Tian1, Brian D M Tom2, Stephen Burgess2,3
1MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK. haodong.tian@mrc-bsu.cam.ac.uk.
BMC Medical Research Methodology
|February 10, 2024
Summary
Mendelian randomization can reveal personalized effects of body mass index on lung function. A new data-adaptive method identifies subgroups who benefit most from interventions, overcoming bias in observational data.
Area of Science:
- Epidemiology
- Biostatistics
- Genetics
Background:
- Mendelian randomization (MR) uses genetic variants as instrumental variables for causal inference in observational studies.
- Standard MR estimates population-averaged effects, similar to randomized trials.
- Stratifying MR by covariates can reveal effect heterogeneity but may induce collider bias.
Purpose of the Study:
- To develop and validate a data-adaptive method for estimating stratum-specific effects in MR robust to collider bias.
- To assess effect heterogeneity of body mass index (BMI) on lung function using this novel approach.
Main Methods:
- Extension of the doubly-ranked method for stratification based on a single covariate.
- Application of a data-adaptive random forest method for stratum-specific estimates with high-dimensional covariates.
- Utilizing Q statistics to assess heterogeneity and variable importance.
Main Results:
- The effect of body mass index (BMI) on lung function is heterogeneous.
- Hip circumference and weight were key drivers of this heterogeneity.
- The predicted effect of BMI on lung function varied from positive to strongly negative across subgroups.
Conclusions:
- The data-adaptive approach enables exploration of MR effect heterogeneity.
- This method provides insights into disease etiology and identifies subgroups for targeted interventions.
- Understanding effect heterogeneity can optimize personalized health strategies.
Keywords:
GeneticsHeterogenous effectInstrumental variableRandom forestStratificationVariable importanceMore Related Videos
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