Biostatistics Department Seminar
We will be having two internal speakers, Ziqiao Wang and Harsh Parikh, present.
Title (Ziqiao Wang): Estimation of Direct and Indirect Polygenic Effects and Gene-Environment Interactions using Polygenic Scores in Case-Parent Trio Studies
Abstract: Hidden population stratification in GWAS of unrelated individuals can lead to overestimation of genetic effects, complicating the applications and interpretations for polygenic scores (PGS). Family-based studies, where genetic effects are estimated through within-family comparisons, provide an opportunity to test for PGS-trait associations while correcting for potential population stratification bias. We provide a novel and comprehensive likelihood-based framework for estimating a collection of risk parameters associated with PGS, including direct and indirect genetic effects, and gene-environment interactions, using case-parent trios. Simulation studies demonstrate our method can produce unbiased effect estimates and correct coverage probabilities under complex population structures. Our method reveals insights into multi-ancestry case-parent trio studies, including transmission-based estimates of PGS effects on orofacial clefts (OFCs) and autism, evidence of maternal indirect effects, gene-environment interactions, genetic correlations between autism with cognitive traits, and molecular mediating effects on OFCs through transcriptome-wide association studies.
Title (Harsh Parikh): Who Are We Missing? A Principled Approach to Characterizing the Underrepresented Population
Abstract: Randomized controlled trials (RCTs) serve as the cornerstone for understanding causal effects, yet extending inferences to target populations presents challenges due to effect heterogeneity and underrepresentation. Our paper addresses the critical issue of identifying and characterizing underrepresented subgroups in RCTs, proposing a novel framework for refining target populations to improve generalizability. We introduce an optimization-based approach, Rashomon Set of Optimal Trees (ROOT), to characterize underrepresented groups. ROOT optimizes the target subpopulation distribution by minimizing the variance of the target average treatment effect estimate, ensuring more precise treatment effect estimations. Notably, ROOT generates interpretable characteristics of the underrepresented population, aiding researchers in effective communication. Our approach demonstrates improved precision and interpretability compared to alternatives, as illustrated with synthetic data experiments. We apply our methodology to extend inferences from the Starting Treatment with Agonist Replacement Therapies (START) trial -- investigating the effectiveness of medication for opioid use disorder -- to the real-world population represented by the Treatment Episode Dataset: Admissions (TEDS-A). By refining target populations using ROOT, our framework offers a systematic approach to enhance decision-making accuracy and inform future trials in diverse populations.
Speakers
Ziqiao Wang and Harsh Parikh are postdoctoral fellows in the Department of Biostatistics, Bloomberg School of Public Health.
Zoom Registration
If you would like to join via Zoom, please register here.
2023-2024 Monday Seminar Series
All seminars are held at 12:05 PM via Zoom and onsite in Room W2008. View all seminar information here.
