Duan Lab

Research focus

NIH R01 · 2024

Federated and transfer learning methods for cross-ancestry and cross-phenotype integration of genomic datasets

Abstract This proposal aims to develop advanced data integration methods for improving genetic risk prediction in under-represented non-European populations. Genome-Wide Association Studies (GWAS) have yielded important biological insights into the heritable basis of many complex traits and diseases, and polygenic risk scores (PRS) have shown promising potentials for disease risk stratification. However, since the vast majority of participants in large-scale genomic datasets are from European ancestry (EA) populations, the performance of current PRS is much poorer in non-EA populations than in EA populations, which may exacerbate existing health disparities. Despite some recent inclusive…

From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.