An integrative association analysis for complex diseases in underrepresented groups by leveraging the trans-ethnic genetic similarity.

Genome-wide association studies (GWASs) have been conducted primarily in European (EUR) populations, limiting insights into underrepresented groups such as East Asian (EAS), but cross-ancestry GWASs have demonstrated high trans-ethnic genetic similarity between EUR and non-EUR populations. To enhance association analysis power in EAS populations, we propose tranScore, a novel summary-statistics-based transfer learning method that leverages trans-ethnic genetic similarity through hierarchical modeling. By considering EUR as auxiliary population, tranScore performs joint testing of genetic effects in auxiliary and target populations via well-established P-value combination procedures. Simulations demonstrate that tranScore maintains control of type I error rates and provides substantial power gains for diverse genetic architectures, showing robustness against various challenges including incomplete SNP overlap and effect heterogeneity. In the real-data application of eight diseases from the China Kadoorie Biobank (CKB), after incorporating the genetic information of the EUR population, tranScore identified significantly more genes than the traditional score test which ignored such information. Approximately 41.9% of discovered genes were replicated in the Biobank Japan cohort. Overall, tranScore represents a flexible and powerful statistical approach for association analysis of complex diseases and traits through transfer learning of shared genetic similarities between the auxiliary and target populations.