Mendelian Randomization That Accounts for LD and Correlated Horizontal Pleiotropy

• Activity Number: 308 - Statistical Methods for Studying Spatial Transcriptomics, Tissue Heterogeneity, and Pleiotropy
• Type: Topic-Contributed
• Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Jin Liu*
• Companies: Duke-NUS Medical School, Health Service & Systems Research

Abstract

Mendelian randomization (MR) is a valuable tool to examine the causal relationships between health risk factors and outcomes from observational studies. Along with the proliferation of genome-wide association studies (GWASs), a variety of two-sample MR methods for summary data have been developed to account for horizontal pleiotropy (HP), primarily based on the assumption that the effects of variants on exposure ($\bfgamma$) and horizontal pleiotropy ($\bfalpha$) are independent. In practice, this assumption is too strict and can be easily violated because of the correlated HP. To account for this correlated HP, we propose a Bayesian approach that uses the orthogonal projection to reparameterize the bivariate normal distribution for $\bfgamma$ and $\bfalpha$. We have also developed an efficient algorithm with paralleled Gibbs sampling. To demonstrate the advantages of the proposed method, we conducted comprehensive simulation studies to compare for both type-I error control and point estimates in various scenarios. We further analyzed multiple real exposure-outcome pairs using GWAS summary statistics.