Dr. Siming Zhao, Assistant Professor of Biomedical Data Science at Dartmouth, is advancing precision medicine through innovative computational approaches that improve genetic risk prediction across populations with different genetic ancestries. Precision medicine has the potential to transform healthcare by tailoring disease prevention and treatment to an individual’s genetic profile. However, many existing polygenic risk prediction models have been developed using datasets that do not fully capture the breadth of human genetic variation, limiting their performance across different ancestral backgrounds.
Through the SYNERGY CORES Pilot Program program, Dr. Zhao collaborated with investigators at Penn State CTSI on the project, “Leveraging causal genetic mechanisms to achieve generalizable polygenic prediction of health and disease in underrepresented populations.” The project focuses on developing computational methods that improve the generalizability of polygenic risk prediction across populations with different genetic ancestries. By improving how polygenic risk models perform across a broader range of genetic backgrounds, the research aims to support precision medicine approaches that are more widely applicable and strengthen future genomic research.
The team’s work was presented at the 2025 Association for Clinical and Translational Science (ACTS) Annual Meeting, where Dr. Zhao shared the collaboration’s ongoing research with investigators from across the national clinical and translational science community, fostering scientific dialogue and new collaborative opportunities.
This project reflects the mission of the SYNERGY CORES Pilot Program by supporting interdisciplinary partnerships and innovative translational research that addresses complex biomedical challenges. As the research progresses, the collaboration is expected to advance methods for improving genomic prediction across populations with different genetic ancestries and generate knowledge that can inform future translational and clinical research.