Improving genetic risk prediction across diverse population by disentangling ancestry representations

Prashnna K. Gyawali, Yann Le Guen, Xiaoxia Liu, Michael E. Belloy, Hua Tang, James Zou, Zihuai He

Research output: Contribution to journalArticlepeer-review


Risk prediction models using genetic data have seen increasing traction in genomics. However, most of the polygenic risk models were developed using data from participants with similar (mostly European) ancestry. This can lead to biases in the risk predictors resulting in poor generalization when applied to minority populations and admixed individuals such as African Americans. To address this issue, largely due to the prediction models being biased by the underlying population structure, we propose a deep-learning framework that leverages data from diverse population and disentangles ancestry from the phenotype-relevant information in its representation. The ancestry disentangled representation can be used to build risk predictors that perform better across minority populations. We applied the proposed method to the analysis of Alzheimer’s disease genetics. Comparing with standard linear and nonlinear risk prediction methods, the proposed method substantially improves risk prediction in minority populations, including admixed individuals, without needing self-reported ancestry information.

Original languageEnglish
Article number964
JournalCommunications Biology
Issue number1
StatePublished - Dec 2023


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