A framework for detecting causal effects of risk factors at an individual level based on principles of Mendelian randomization: Applications to modelling individualized effects of lipids on coronary artery disease

Abstract

AbstractMendelian Randomization (MR), a method that employs genetic variants as instruments for causal inference, has gained popularity in assessing the causal effects of risk factors. However, almost all MR studies primarily concentrate on the population’saveragecausal effects. With the advent of precision medicine, theindividualizedtreatment effect (ITE) is often of greater interest. For instance, certain risk factors may pose a higher risk to some individuals compared to others, and the benefits of a treatment may vary among individuals. This highlights the importance of considering individual differences in risk and treatment response.We propose a new framework that expands the concept of MR to investigateindividualizedcausal effects. We presented several approaches for estimating Individualized Treatment Effects (ITEs) within this MR framework, primarily grounded on the principles of the”R-learner”. To evaluate the existence of causal effect heterogeneity, we proposed two permutation testing methods. We employed Polygenic Risk Scores (PRS) as the instrument and demonstrated that the removal of potentially pleiotropic SNPs could enhance the accuracy of ITE estimates. The validity of our approach was substantiated through comprehensive simulations.We applied our framework to study the individualized causal effect of various lipid traits, including Low-Density Lipoprotein Cholesterol (LDL-C), High-Density Lipoprotein Cholesterol (HDL-C), Triglycerides (TG), and Total Cholesterol (TC), on the risk of Coronary Artery Disease (CAD) using data from the UK Biobank. Our findings indicate that an elevated level of LDL-C is causally linked to increased CAD risks, with the effect demonstrating significant heterogeneity. Similar results were observed for TC. We also revealed clinical factors contributing to the heterogeneity of ITE based on Shapley value analysis. Furthermore, we identified clinical factors contributing to the heterogeneity of ITEs through Shapley value analysis. This underscores the importance of individualized treatment plans in managing CAD risks.