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

Abstract

Background: Mendelian Randomisation (MR) has been widely used to study the causal effects of risk factors. However, almost all MR studies concentrate on the population's average causal effects. With the advent of precision medicine, the individualised treatment effect (ITE) is often of greater interest. For instance, certain risk factors may pose a higher risk to some individuals than others, and the benefits of treatments may vary across individuals. This study proposes a framework for estimating individualised causal effects in large-scale observational studies where unobserved confounding factors may be present. Methods: We propose a framework (MR-ITE) that expands the scope of MR from estimating average causal effects to individualised causal effects. We present several approaches for estimating ITEs within this MR framework, primarily grounded on the principles of the “R-learner”. To evaluate the presence of causal effect heterogeneity, we also proposed two permutation testing methods. We employed polygenic risk score (PRS) as instruments and proposed methods to improve the accuracy of ITE estimates by removal of potentially pleiotropic single nucleotide polymorphisms (SNPs). The validity of our approach was substantiated through comprehensive simulations. The proposed framework also allows the identification of important effect modifiers contributing to individualised differences in treatment effects. We applied our framework to study the individualised causal effects 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) based on the UK-Biobank (UKBB). We also studied the ITE of C-reactive protein (CRP) and insulin-like growth factor 1 (IGF-1) on CAD as secondary analyses. Findings: Simulation studies demonstrated that MR-ITE outperformed traditional causal forest approaches in identifying ITEs when unobserved confounders were present. The integration of the contamination mixture (ConMix) approach to remove invalid pleiotropic SNPs further enhanced MR-ITE's performance. In real-world applications, we identified positive causal associations between CAD and several factors (LDL-C, Total Cholesterol, and IGF-1 levels). Our permutation tests revealed significant heterogeneity in these causal associations across individuals. Using Shapley value analysis, we identified the top effect modifiers contributing to this heterogeneity. Interpretation: We introduced a new framework, MR-ITE, capable of inferring individualised causal effects in observational studies based on the MR approach, utilizing PRS as instruments. MR-ITE extends the application of MR from estimating the average treatment effect to individualised treatment effects. Our real-world application of MR-ITE underscores the importance of identifying ITEs in the context of precision medicine. Funding: This work was supported partially by a National Natural Science Foundation of China grant (NSFC; grant number 81971706), the KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Institute of Zoology and The Chinese University of Hong Kong, China, and the Lo Kwee Seong Biomedical Research Fund from The Chinese University of Hong Kong.published_or_final_versio