A summary-data-based Mendelian randomization method with application to correlated lipidomic data

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PROJECT SUMMARY In recent years, state-of-the-art technologies have allowed for high-throughput profilings of -omics markers, and these emerging data have been powerful tools to understand complex biological systems. Meanwhile, genome- wide association studies (GWAS) with -omics markers have been conducted, and the emerging summary statistics has provided evaluable opportunity to examine causality between -omics markers and disease outcomes using Mendelian randomization (MR). However, most of the time, these novel -omics data are high- dimensional and correlated, imposing significant challenges in use of MR. We will develop a summary-data- based MR method for use with high-dimensional correlated data. Our mode is a multivariable linear mixed model, and the innovation lies in that we will account for random errors in both independent variables and dependent variable under fixed- and random-effects models. Our model allows for multiple -omics markers as independent variables, and boosts power by including variants with less stringent P value as IVs and implementing stepwise selection which retains the most important -omics markers. We will conduct stratified analysis by tissue expression and biological function of genetic variants to test for robustness of causal associations. W e will develop an R script and share it with the whole research community in Github (aim 1). We will conduct a simulation study to compare the performance of our MR model to previous summary-data-based-MR methods (aim 2). High-density lipoprotein cholesterol (HDL-c) levels have long been recognized an important inverse predictor of coronary heart disease. However, clinical trials and Mendelian randomization suggest that level of HDL-c is a poor biomarker of HDL function, and raise an important question about whether other novel measures of HDL (i.e. lipids in HDLs of different particle sizes) may have causal effects on cardiometabolic disease. HDL is a collection of particles with different sizes highly heterogeneous in lipid composition. Small HDL that exerts stronger cholesterol efflux capacity has higher contents of phospholipids and lower triglycerides and cholesterols, while large HDL has higher contents of triglycerides and cholesterols and lower phospholipids. Thus, it is imperative to understand whether lipids in HDL lipoproteins are causally related to cardiometabolic disease. We will apply our MR method to examine causal effects of lipids (cholesterol, phospholipids, and triglycerides) in HDL particles (very large, large, medium, small, very small) on risk of cardiometabolic disease (coronary heart disease, stroke, and type 2 diabetes) using the recently released lipidomics data in the UK Biobank and existing summary statistics with lipid fractions from the MAGNETIC consortium (aim 3).