Thursday, May 14 — 2:30 to 3:30 p.m.
Chair
Tianyuan Lu
University of Wisconsin–Madison
Dr. Tianyuan Lu is an Assistant Professor in the Department of Population Health Sciences and the Department of Biostatistics and Medical Informatics at the University of Wisconsin-Madison. He earned his bachelor’s degree in Biological Sciences from Fudan University and his PhD in Quantitative Life Sciences from McGill University. He was a Schmidt AI in Science Postdoctoral Fellow at the University of Toronto before joining UW-Madison. His research focuses on developing and implementing rigorous statistical genetics and genetic epidemiology methods to improve the prevention, diagnosis, and treatment of complex diseases, and translating research findings into new medical care approaches and therapies.
Panelists
Matthew Maxwell
University of Wisconsin-Madison
Matthew J. Maxwell is a PhD Candidate in philosophy at the Unviersity of Wisconsin – Madison. His research is on the philosophy of biology with an emphasis on evolutionary theory and population genetics.
Presentation or paper
Population Subdivision and the Search for Disease-Linked Loci: Must K Be Arbitrary?
Shihui Peng
University of Wisconsin-Madison
Shihui Peng is a PhD student in the Epidemiology program in the Department of Population Health Sciences at the University of Wisconsin-Madison. Her work focuses on genetic epidemiology and statistical genetics, with an emphasis on studying complex traits using large-scale genomic data. She is particularly interested in improving genetic risk prediction and better understanding the genetic architecture of complex diseases.
Presentation or paper
Improving Cross-Ancestry Generalizability of Genetic Risk Prediction for Short Stature Using a Meta-Polygenic Risk Score
Yuchang Wu
University of Wisconsin-Madison
Yuchang Wu is an Assistant Scientist in the BMI department at the University of Wisconsin-Madison working in Qiongshi Lu’s group. His research interests include statistical genetics and social genomics.
Presentation or paper
Identifying Overlapping Samples in the PGS Cohort Using Only GWAS Summary Statistics