250217 Unbiased causal inference with Mendelian randomization and covariate-adjusted GWAS data HGG Advances (2025)

Mendelian randomization 분석에 covariate-adjusted GWAS 결과를 사용할 경우

UVMR보다 MVMR이 더 unbiased causal estimate을 얻을 수 있다는 논문 

 

Summary

Mendelian randomization (MR) facilitates causal inference with observational data using publicly available genome-wide association study (GWAS) results. In a GWAS, one or more heritable covariates may be adjusted for to estimate the direct effects of SNPs on a focal trait or to improve statistical power, which may introduce collider bias in SNP-trait association estimates, thus affecting downstream MR analyses. Numerical studies suggested that using covariate-adjusted GWAS summary data might introduce bias in univariable Mendelian randomization (UVMR), which can be mitigated by multivariable Mendelian randomization (MVMR). However, it remains unclear and even mysterious why/how MVMR works; a rigorous theory is needed to explain and substantiate the above empirical observation. In this paper, we derive some analytical results when multiple covariates are adjusted for in the GWAS of exposure and/or the GWAS of outcome, thus supporting and explaining the empirical results. Our analytical results offer insights to how bias arises in UVMR and how it is avoided in MVMR, regardless of whether collider bias is present. We also consider applying UVMR or MVMR methods after collider-bias correction. We conducted extensive simulations to demonstrate that with covariate-adjusted GWAS summary data, MVMR had an advantage over UVMR by producing nearly unbiased causal estimates; however, in some situations it is advantageous to apply UVMR after bias correction. In real data analyses of the GWAS data with body mass index (BMI) being adjusted for metabolomic principal components, we examined the causal effect of BMI on blood pressure, confirming the above points.

 

Our theoretical analysis also offers additional insights into how MVMR compares with UVMR with covariate-adjusted GWAS data with or without collider bias. For example, we show that biased causal estimates by UVMR may arise in the absence of collider bias using covariate-adjust GWAS data. Interestingly, under the scenario without collider bias, using covariate-adjusted GWAS data of exposure or covariate-adjusted GWAS data of outcome, but not both, will in general lead to biased estimates in UVMR, but not so in MVMR; however, if both GWAS data of exposure and outcome are covariate adjusted, both UVMR and MVMR lead to unbiased causal estimates, but UVMR is more efficient with more precise estimates. We will also consider the alternative approach based on collider bias correction.14,15,16,17 After (effective) bias correction to covariate-adjusted GWAS association estimates, the situation is the same as that without collider bias discussed above. Furthermore, we will assess the performance of various MR methods in more realistic scenarios with invalid IVs with pleiotropic effects.