معرفی
Tianyuan Lu is an Assistant Professor in the Department of Biostatistics and Medical Informatics at the University of Wisconsin–Madison, affiliated with the School of Medicine and Public Health. His research focuses on developing statistical genetics methodologies for genetic epidemiology, predictive modeling, and biomarker discovery, with applications in complex diseases including cardiovascular conditions, diabetes, mental health disorders, and infectious diseases like severe COVID-19. He leads the Lulab, which integrates multi-omics data with advanced statistical techniques to uncover disease mechanisms.
His work emphasizes Mendelian randomization, proteogenomic analysis, and polygenic risk prediction, addressing critical questions about gene-environment interactions and causal pathways. Notable contributions include methods for nonlinear Mendelian randomization, ancestry-aware proteomic studies, and scalable machine learning models for single-cell data. He has developed open-source tools such as COSIME and SharePro to advance collaborative genomic research.
Research highlights include identifying COL6A3-derived endotrophin as an obesity-coronary artery disease mediator, discovering vitamin D’s role in Alzheimer’s via two-sample MR, and creating plasma protein-based hip fracture risk scores. His projects often involve large-scale datasets like the UK Biobank, leveraging multi-omics integration to bridge basic science and clinical translation.
