Monday, Aug 5: 8:45 AM - 8:50 AM
3392
Contributed Speed
Oregon Convention Center
Mendelian randomization (MR) analysis is widely used in genetic epidemiology to estimate the causal effect of a risk factor on an outcome of interest. Increasing evidence shows the importance of sex differences in health and disease mechanisms. However, research on sex-specific causal effects is lacking due to limited sex-specific GWASs. Motivated by GWASs from the Million Veteran Program, in which only 10% of individuals are female, a major limitation to MR analyses is weak IVs, which manifest as poor variant-exposure effect estimates that lead to unstable causal effect estimates. We propose a Bayesian framework to stabilize female exposure GWAS effect sizes by borrowing information from the male population. By specifying a particular prior distribution on female exposure GWAS effect sizes, we demonstrate two special cases of posterior means, including the inverse variance-weighted meta-analysis and the adaptive weight approach. We perform a series of simulation studies to examine the performance of our proposed Bayesian approach in MR analysis. Finally, we apply the proposed method to estimate the causal effects of sleep phenotypes on cardiovascular-related diseases
MR analysis
Bayesian framework
Sex-specific causal effect
Main Sponsor
Section on Statistics in Genomics and Genetics