Lap Sum Chan

I'm a Postdoctoral Associate in the Division of Biostatistics and Health Data Science at the University of Minnesota School of Public Health, supervised by Professor Wei Pan. My research revolves around statistical methods for omics (e.g., GWAS, microbiome and metabolome) data, high dimensional statistics, and causal inference. I am passionate about advancing causal discovery techniques to better understand the underlying mechanisms of complex diseases. Currently, I am focusing on Alzheimer's disease, developing robust Mendelian randomization methods to identify causal exposures linked to this condition.

Developed a novel MR method for highly correlated exposures: Discovered two key metabolomic clusters (glutamine and lipids) putatively causal for Alzheimer’s disease, analyzing data from 480K+ individuals in large-scale biobanks
• Pioneered an MR method for multimorbidity: Created the first framework to determine whether risk factors are shared or distinct across multiple diseases, which is also applicable to highly correlated exposures
• Extended fine mapping (SuSiE) for MR applications: Adapted SuSiE to identify distinct risk factor clusters within correlated omics data, improving causal inference
• Performed the first multi-layer multi-omics MR analysis: Integrated microbiome and metabolome data to uncover novel causal risk factors for Alzheimer’s disease, offering new insights
• Collaborated with clinical teams: Co-developed atrial fibrillation risk models with a lead cardiologist and mentored MD-PhD students, resulting in improved early detection strategies in clinical practice

Ph.D. in Biostatistics, University of Michigan (Graduated 2023)