Improving genetic risk prediction across diverse population by disentangling ancestry representations.

ABSTRACT: 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 applie

SUBMITTER: Gyawali PK 

PROVIDER: S-EPMC10517023 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature