Statistical Inference for Binary Outcomes in Two-Sample Summary-Data Mendelian Randomization

Music City Center 

Mendelian randomization (MR) is a powerful tool for evaluating causal effects in the presence of unmeasured confounding. With the ever-growing sample sizes in genome-wide association studies, there is a rising trend to perform MR analyses using summary data from genetic associations across diverse phenotypes. Traditional two-sample summary-data MR methods require that the genetic variants employed satisfy the exclusion restriction-a condition frequently violated due to pleiotropy. Although several approaches have been introduced to mitigate this issue, existing methods still fall short when it comes to precisely estimating causal effect sizes for binary outcomes. In this study, we introduce a novel statistical method specifically designed for binary outcome data within the two-sample summary-data MR framework, addressing challenges that commonly arise in practical applications. We demonstrate the efficacy of our method through extensive simulations under various scenarios and provide a comprehensive comparison with current methodologies.

mendelian randomization

binary outcome

summary data

pleiotropy effects

causal inference 

Section on Statistics in Genomics and Genetics