Mendelian randomization with a binary exposure variable: interpretation and presentation of causal estimates

Abstract

Mendelian randomization uses genetic variants to make causal inferences about a modifiable exposure. Subject to a genetic variant satisfying the instrumental variable assumptions, an association between the variant and outcome implies a causal effect of the exposure on the outcome. Complications arise with a binary exposure that is a dichotomization of a continuous risk factor (for example, hypertension is a dichotomization of blood pressure). This can lead to violation of the exclusion restriction assumption: the genetic variant can influence the outcome via the continuous risk factor even if the binary exposure does not change. Provided the instrumental variable assumptions are satisfied for the underlying continuous risk factor, causal inferences for the binary exposure are valid for the continuous risk factor. Causal estimates for the binary exposure assume the causal effect is a stepwise function at the point of dichotomization. Even then, estimation requires further parametric assumptions. Under monotonicity, the causal estimate represents the average causal effect in ‘compliers’, individuals for whom the binary exposure would be present if they have the genetic variant and absent otherwise. Unlike in randomized trials, genetic compliers are unlikely to be a large or representative subgroup of the population. Under homogeneity, the causal effect of the exposure on the outcome is assumed constant in all individuals; rarely a plausible assumption. We here provide methods for causal estimation with a binary exposure (although subject to all the above caveats). Mendelian randomization investigations with a dichotomized binary exposure should be conceptualized in terms of an underlying continuous variable.

Mendelian Randomization Study of Obesity and Cerebrovascular Disease

“…Although abnormal cholesterol and triglyceride levels are also associated with obesity, given inconsistent and sometimes diverging risk associations between lipid levels and stroke risk, we chose to limit mediation analysis to SBP and FG . We prioritized the continuous traits of FG and hemoglobin A1c (HbA1c) over diabetes case/control status, as dichotomous exposure variables can introduce bias into MR analyses related to violation of the monotonicity assumption, as the variants employed as instruments will not predict the exposure phenotype in all individuals . Compared to HbA1c, FG is less affected by erythrocyte lifespan in nondiabetic participants and is informed by a more powered GWAS.…”

“…16 We prioritized the continuous traits of FG and hemoglobin A1c (HbA1c) over diabetes case/control status, as dichotomous exposure variables can introduce bias into MR analyses related to violation of the monotonicity assumption, as the variants employed as instruments will not predict the exposure phenotype in all individuals. 17 Compared to HbA1c, FG is less affected by erythrocyte lifespan in nondiabetic participants 18 and is informed by a more powered GWAS. FG was therefore selected for the primary analysis, and HbA1c was included for confirmation.…”

Diabetes and cancer risk: A Mendelian randomization study

“…Because diabetes was a rare disease in Japan during the study period, the odds approximates the probability. We then exponentiated the estimates to obtain the hazard ratios for cancer per doubling in the probability of diabetes …”

“…We then exponentiated the estimates to obtain the hazard ratios for cancer per doubling in the probability of diabetes. 32 MR analyses derive valid estimates where the following assumptions are met 33 : (i) the SNPs are correlated with diabetes, (ii) the SNPs affect cancer risk only through their effects on diabetes and (iii) the SNPs are independent of any confounding factors for the association between diabetes and cancer risk. For assumption (i), because we selected SNPs that were identified through the GWAS and replicated in Asian populations, such SNPs are likely to be correlated with diabetes in the source population.…”

Exploring diseases/traits and blood proteins causally related to expression of ACE2, the putative receptor of SARS-CoV-2: A Mendelian Randomization analysis highlights tentative relevance of diabetes-related traits

“…Regarding the effect size of casual associations, since the exposures were binary, the regression coefficients (beta) from MR may be roughly interpreted as average change in the outcome (increase in normalized expression level) per 2.72-fold increase in the prevalence of the exposure 34 . For type II diabetes, or self-reported diabetes from UKBB which presumably comprised mainly type II diabetes, the causal estimates ranged from ~0.1621 to 0.1835.…”