Mendelian Randomization Approach for Robust and Powerful Estimation and Statistical Inference of Causal Effects

• Activity Number: 176 - Statistical Genetics III – Predictive Modeling, GxE Interaction, and Causal Inference
• Type: Contributed
• Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Haoyu Zhang* and Xihong Lin
• Companies: Harvard T.H. Chan School of Public Health and Harvard TH Chan School of Public Health
• Keywords: Mendelian randomization; causal inference; instrumental variables; polygenetic risk scores; robust estimation; breast cancer

Abstract

Mendelian randomization (MR) is a major tool to test the causal association between risk factors and disease using genetic variants as instrumental variables (IVs). The method only requires summary-level statistics from GWAS, which efficiently decrease the analysis cost and make the method widely used. MR makes several strong assumptions, which can be violated in practice and lead to biased estimates and statistical inference, such as confidence interval construction and hypothesis testing. To resolve this issue, we will develop robust hypothesis testing and confidence interval construction in the presence of small-effect genetic variants as IVs. We will empower MR analysis using polygenetic risk scores (PRS) as IVs. The method will be evaluated across a wide range of realistic simulations materials. In the meantime, we will apply the methods to estimate the causal effect between epidemiological risk factors and cardiovascular diseases, breast cancer, etc.