Testing for non-linear causal effects using a binary genotype in a Mendelian randomization study: application to alcohol and cardiovascular traits

Abstract

Background: Mendelian randomization studies have so far restricted attention to linear associations relating the genetic instrument to the exposure, and the exposure to the outcome. In some cases, however, observational data suggest a non-linear association between exposure and outcome. For example, alcohol consumption is consistently reported as having a U-shaped association with cardiovascular events. In principle, Mendelian randomization could address concerns that the apparent protective effect of light-to-moderate drinking might reflect ‘sick-quitters’ and confounding.Methods: The Alcohol-ADH1B Consortium was established to study the causal effects of alcohol consumption on cardiovascular events and biomarkers, using the single nucleotide polymorphism rs1229984 in ADH1B as a genetic instrument. To assess non-linear causal effects in this study, we propose a novel method based on estimating local average treatment effects for discrete levels of the exposure range, then testing for a linear trend in those effects. Our method requires an assumption that the instrument has the same effect on exposure in all individuals. We conduct simulations examining the robustness of the method to violations of this assumption, and apply the method to the Alcohol-ADH1B Consortium data.Results: Our method gave a conservative test for non-linearity under realistic violations of the key assumption. We found evidence for a non-linear causal effect of alcohol intake on several cardiovascular traits.Conclusions: We believe our method is useful for inferring departure from linearity when only a binary instrument is available. We estimated non-linear causal effects of alcohol intake which could not have been estimated through standard instrumental variable approaches.

Genetic Evidence for Causal Relationships Between Maternal Obesity-Related Traits and Birth Weight

“…We have not tested for non-linear associations which, in a Mendelian randomization design, would require very large numbers 30 . H owever, for maternal BMI, fasting glucose and systolic blood pressure, there is observational evidence of such linear associations across the distribution, with no evidence of threshold or curvilinear associations 5,10,31 .…”

Using Genetic Variation to Explore the Causal Effect of Maternal Pregnancy Adiposity on Future Offspring Adiposity: A Mendelian Randomisation Study

“…Although methods for assessing nonlinear effects using MR have been recently developed [74], these require very large sample sizes and we are not able to apply these to our data. Thus, we cannot rule out the possibility of, for example, a nonlinear threshold effect of extreme maternal obesity having a causal intrauterine effect on offspring adiposity.…”

Vascular Endothelial Growth Factor and Ischemic Heart Disease Risk: A Mendelian Randomization Study

“…Nevertheless, we conducted MR‐Egger regression, which is more robust to the inclusion of invalid SNPs, and generally found no evidence for the relation of VEGF and IHD risk, with estimates close to null. Furthermore, we were unable to examine potential nonlinearity of VEGF on IHD because we only used summary statistics in this study, whereas the existing method for assessing nonlinearity in MR requires individual‐level data . Nevertheless, this could be further explored in the UK Biobank once it accumulates enough IHD cases .…”