Statistics and Data Science Seminar
Yuehua Cui
Michigan State University
Mendelian randomization for causal inference with longitudinal traits
Abstract: Mendelian randomization (MR) uses genetic variants as instrument variables to determine whether an observational association between an exposure and an outcome is causal. The use of Mendelian Randomization reduces regression bias and provides reliable estimate of the likely underlying causal relationship between an exposure and a disease outcome. Most current Mendelian randomization methods are focused on cross-sectional phenotypic traits. Longitudinal studies track the same individual at different time points and have a number of advantages over cross-sectional studies. Motivated by a real study to evaluate the causal effect of hormone level on eating behavior, we propose two MR models to investigate the causal effects in a longitudinal study. In the first model, we assume the current exposure affects the current outcome. In the second model, we assume that the past and/or current exposures contribute to the current outcome. The delayed causal effect is determined by data through a variable selection algorithm. Point-wise and simultaneous testing are developed to assess the existence of causal effects. The method was illustrated via simulation studies and an application to an eating behavior dataset.
Wednesday October 26, 2022 at 4:00 PM in Zoom