Speaker: Chunlin Li, Assistant Professor, Department of Statistics, Iowa State University

Title: Quantifying High-Dimensional Omics Mediators: Unraveling the Link from Alcohol Intake to Coronary Heart Disease

Abstract: While many existing epidemiological studies have examined associations between alcohol and cardiovascular outcomes, less has been done to explore causal biological pathways and mechanisms of the observed associations at the molecular level. To investigate this relationship, we propose a new causal measure to quantify the mediating role of molecular phenotypes, such as DNA methylation, in bridging alcohol intake and cardiovascular outcomes. The challenge of estimating this measure is three-fold. First, since alcohol consumption is associated with genome-wide changes at the molecular level, it is biologically plausible that many omics mediators with weak but collectively considerable effects are involved in the pathway; however, existing methods are plagued by inconsistency in the presence of non-sparse mediators. To address this issue, we develop a method to consistently estimate the proposed measure in such situations. Second, many epidemiological studies use case-control sampling, which introduces ascertainment bias in mediation analysis but has been largely ignored in the literature. To correct this bias, we propose a method of moment based on the Haseman-Elston regression, motivated by heritability estimation. Finally, a significant challenge in this research is the potential for residual confounding in observational studies, which can seriously compromise the validity of scientific findings. We will discuss the plausible approaches we will explore in the future to address this important yet under-studied problem in causal mediation analysis.

This talk is based on an on-going project with Rohit Kanrar and my collaborators at the University of Minnesota and MD Anderson Cancer Center.