Improving genetic risk prediction across diverse population by disentangling ancestry representations

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. A deep-learning framework leverages data from diverse populations and disentangles ancestry from the phenotype-relevant information in its representation.

Automated summary

Risk prediction models using genetic data are important in genomics. However, most models are developed using data from participants with similar ancestry, leading to poor prediction for minority populations. To address this, we propose a deep-learning framework that leverages diverse population data and separates ancestry from phenotype-relevant information, improving risk prediction for minority populations.