  The limited representation of minorities and disadvantaged populations in
large-scale clinical and genomics research has become a barrier to translating
precision medicine research into practice. Due to heterogeneity across
populations, risk prediction models are often found to be underperformed in
these underrepresented populations, and therefore may further exacerbate known
health disparities. In this paper, we propose a two-way data integration
strategy that integrates heterogeneous data from diverse populations and from
multiple healthcare institutions via a federated transfer learning approach.
The proposed method can handle the challenging setting where sample sizes from
different populations are highly unbalanced. With only a small number of
communications across participating sites, the proposed method can achieve
performance comparable to the pooled analysis where individual-level data are
directly pooled together. We show that the proposed method improves the
estimation and prediction accuracy in underrepresented populations, and reduces
the gap of model performance across populations. Our theoretical analysis
reveals how estimation accuracy is influenced by communication budgets, privacy
restrictions, and heterogeneity across populations. We demonstrate the
feasibility and validity of our methods through numerical experiments and a
real application to a multi-center study, in which we construct polygenic risk
prediction models for Type II diabetes in AA population.
