Graduate Student Research Assistant (GSRA) and PhD Candidate
Tai Yang, MS
Tai generates polygenic scores using published GWAS summary statistics and benchmarks them for mental health, sleep, and behavioral traits. He also develops distribution-valued regression methods.
Research
Polygenic score benchmarking
I work on PsyPRS, where I generate polygenic scores using published genome-wide association study (GWAS) summary statistics and systematically benchmark them for mental health, sleep, and behavioral traits. Using the Michigan Genomics Initiative and the NIH All of Us Research Program, I evaluate how well scores from different methods transfer across ancestry groups. The results are released through PsyPRSweb, an open resource that helps researchers choose among available scores.
This work is part of my graduate student research assistant (GSRA) role with Lars Fritsche on COMPASS.
Doctoral research
I develop distribution-valued regression methods for genome-wide association studies. These methods use an individual’s entire measured distribution as the phenotype instead of a small set of scalar summaries. My doctoral work focuses on wearable-device data, particularly continuous glucose monitoring.
Current training
I am a graduate student research assistant and doctoral researcher in Biostatistics. My work connects the lab’s programs in polygenic risk, EHR-linked biobanks, and precision mental health.
Presentations
I presented work on PsyPRS and PsyPRSweb at:
- Michigan Student Symposium for Interdisciplinary Statistical Sciences (MSSISS), 2026: “PsyPRS: A Repository with Polygenic Risk Scores for Mental Health Phenotypes.”
- Joint Statistical Meetings (JSM), 2026: “PsyPRSweb: An Online Repository with Polygenic Scores for Mental Health-Related Phenotypes.”