High-throughput multivariable Mendelian randomization analysis prioritizes apolipoprotein B as key lipid risk factor for coronary artery disease

Abstract

BackgroundGenetic variants can be used to prioritize risk factors as potential therapeutic targets via Mendelian randomization (MR). An agnostic statistical framework using Bayesian model averaging (MR-BMA) can disentangle the causal role of correlated risk factors with shared genetic predictors. Here, our objective is to identify lipoprotein measures as mediators between lipid-associated genetic variants and coronary artery disease (CAD) for the purpose of detecting therapeutic targets for CAD.MethodsAs risk factors we consider 30 lipoprotein measures and metabolites derived from a high-throughput metabolomics study including 24,925 participants. We fit multivariable MR models of genetic associations with CAD estimated in 453,595 participants (including 113,937 cases) regressed on genetic associations with the risk factors. MR-BMA assigns to each combination of risk factors a model score quantifying how well the genetic associations with CAD are explained. Risk factors are ranked by their marginal score and selected using false discovery rate (FDR) criteria. We perform sensitivity and replication analyses varying the dataset for genetic associations with CAD.ResultsIn the main analysis, the top combination of risk factors ranked by the model score contains apolipoprotein B (ApoB) only. ApoB is also the highest ranked risk factor with respect to the marginal score (FDR< 0.005). Additionally, ApoB is selected in all replication analyses. No other measure of cholesterol or triglyceride is consistently selected otherwise.ConclusionsOur agnostic genetic investigation prioritizes ApoB across all datasets considered, suggesting that ApoB, representing the total number of hepatic-derived lipoprotein particles, is the primary lipid determinant of CAD.Key messagesIt is a common consensus that lipoproteins increase cardiovascular disease risk, yet little is known about the exact mechanisms.We use genetic associations with high-throughput metabolomics features to draw a detailed picture of lipid traits and characteristics allowing for an unprecedented resolution when considering lipids as risk factors for cardiovascular disease.This study integrates genetic data from a large scale metabolomics study including 25,000 samples and the largest study on cardiovascular disease risk including 113,937 cases and 339,658 controls.MR-BMA, a novel algorithm for multivariable MR (Zuber and Burgess, Nature Communications 2020; 11(1):29) is used to identify the most likely causal lipid determinants of cardiovascular disease from a large set of candidate risk factors with shared genetic predictors.Our agnostic genetic investigation prioritizes apolipoprotein B across all datasets considered, suggesting that apolipoprotein B, representing the total number of hepatic-derived lipoprotein particles, is the primary lipid determinant of cardiovascular disease risk.