The UK BiLEVE and Mendelian randomisation: Using multivariable instrumental variables to address “damned if you, dammed if you don’t” adjustment problems

Abstract

Objective: To explore the use of multivariable instrumental variables to resolve the "dammed if you do, dammed if you don't" adjustment problem created for Mendelian randomisation (MR) analysis using the smoking or lung function related phenotypes in the UK Biobank (UKB). Result: "dammed if you do, dammed if you don't" adjustment problems occur when both adjusting and not-adjusting for a variable will induce bias in an analysis. One instance of this occurs because the genotyping chip of UKB participants differed based on lung function/smoking status. In simulations, we show that multivariable instrumental variables analyses can attenuate potential collider bias introduced by adjusting for a proposed covariate, such as the UKB genotyping chip. We then explore the effect of adjusting for genotyping chip in a multivariable MR model exploring the effect of smoking on seven medical outcomes (lung cancer, emphysema, hypertension, stroke, heart diseases, depression, and disabilities). We additionally compare our results to a traditional univariate MR analysis using genome-wide analyses summary statistics which had and had not adjusted for genotyping chip. This analysis implies that the difference in genotyping chip has introduced only a small amount of bias.

WITHDRAWN: Assessing the Causal Effects of Environmental Tobacco Smoke Exposure: A meta-analytic Mendelian randomisation study

“…However, for smoking or lung function-related phenotypes, adjusting for genotyping chip could additionally introduce collider/selection bias. Although such bias can theoretically be removed by using MVMR (40,41), prior research indicates that the use of MVMR in this context has little overall effect on the final MR estimates (40). Furthermore, adding additional covariates in an MVMR model greatly increases the risk of weak instrument bias and reduces power.…”