Advances in Mendelian Randomization: Robust Causal Inference and Identification of Risk Factors for Coronary Artery Disease

Mendelian randomization (MR) is a commonly used approach in human genetics to infer causal risk factors for complex diseases. In this talk, I present research focused on the development and application of MR methods for robust causal inference testing, interpretation, and the identification of causal risk factors for coronary artery disease. First, I demonstrate the detection of widespread horizontal pleiotropy in MR testing between complex traits and diseases. Next, I describe a method that empirically quantifies horizontal pleiotropy in human genetic variation, showing that it is pervasive and primarily driven by extreme polygenicity of complex traits and diseases. Third, I introduce the concept of causal variance to quantify the contribution of a risk factor to disease. Additionally, I present work related to the application of MR in dissecting causal influences for coronary artery disease. This includes the identification of plasma triglycerides as a causal risk factor for coronary artery disease and a phenome-wide MR analysis of plasma triglycerides on 2,600 disease traits. Finally, I highlight a study on using MR as a tool to identify modifiable lifestyle factors that are causal towards coronary artery disease risk.