A0818
Title: Bayesian Mendelian randomization analysis of latent exposures leveraging GWAS summary statistics for observed biomarkers
Authors: Yue Yu - University of Pennsylvania (United States)
Andrew Lakkis - University of Pennsylvania (United States)
Bingxin Zhao - University of Pennsylvania (United States)
Jin Jin - University of Pennsylvania (United States) [presenting]
Abstract: Mendelian randomization analysis is a popular method to infer causal relationships between exposures and outcomes, utilizing data from genome-wide association studies (GWAS) to overcome the limitations of observational research by treating genetic variants as instrumental variables. The focus is on a specific problem setting, where causal signals may exist among a series of correlated traits, but the exposures of interest, such as biological functions or lower-dimensional latent factors that regulate the observable traits, are not directly observable. A Bayesian Mendelian randomization analysis framework is proposed that allows joint analysis of the causal effects of multiple latent exposures on a disease outcome leveraging GWAS summary-level association statistics for traits co-regulated by the exposures. Simulation studies are conducted to show the validity and superiority of the method in terms of type I error control and power due to a more flexible modeling framework and a more stable algorithm compared to an alternative approach and traditional single- and multi-exposure analysis approaches not specifically designed for the problem. The method is also applied to reveal evidence of the causal effects of psychiatric factors, including compulsive, psychotic, neurodevelopmental, and internalizing factors, on neurodegenerative, autoimmune, digestive, and cardiometabolic diseases.
