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The limited representation of minorities and disadvantaged populations inlarge-scale clinical and genomics research has become a barrier to translatingprecision medicine research into practice. Due to heterogeneity acrosspopulations, risk prediction models are often found to be underperformed inthese underrepresented populations, and therefore may further exacerbate knownhealth disparities. In this paper, we propose a two-way data integrationstrategy that integrates heterogeneous data from diverse populations and frommultiple healthcare institutions via a federated transfer learning approach.The proposed method can handle the challenging setting where sample sizes fromdifferent populations are highly unbalanced. With only a small number ofcommunications across participating sites, the proposed method can achieveperformance comparable to the pooled analysis where individual-level data aredirectly pooled together. We show that the proposed method improves theestimation and prediction accuracy in underrepresented populations, and reducesthe gap of model performance across populations. Our theoretical analysisreveals how estimation accuracy is influenced by communication budgets, privacyrestrictions, and heterogeneity across populations. We demonstrate thefeasibility and validity of our methods through numerical experiments and areal application to a multi-center study, in which we construct polygenic riskprediction models for Type II diabetes in AA population.

Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach