Background

The polygenic risk score (PRS), which calculates the future risk of disease onset by aggregating individual DNA variants, has been recognized as a key tool in precision medicine. It is a method that statistically sums the effects of hundreds of thousands to millions of single nucleotide polymorphisms (SNPs) to assess individual susceptibility. However, existing genome dynamics research was trapped by a structural limitation of severe sample imbalance. This is because more than 80% of participants used in Genome-Wide Association Studies (GWAS) were biased toward European ancestry.

This distortion in genomic data has resulted in a sharp decline in prediction model performance for non-European populations. When applying existing PRS algorithms to African or Hispanic groups, prediction accuracy can drop to less than half that of European groups. This is because linkage disequilibrium (LD) patterns and allele frequencies vary across populations when assessing disease susceptibility. Consequently, continuous criticism has been raised that advancements in genomic research are deepening healthcare inequalities by benefiting only specific racial groups.

Key Findings

Researchers adopted a large-scale multi-ancestry meta-analysis strategy by combining data from the National Institutes of Health (NIH) All of Us Research Program (AoU) and the UK Biobank (UKB). The AoU is a cohort that has secured a proportion of underrepresented minorities of over 50%, groups historically excluded from medical research. The researchers reconstructed models for complex traits and major chronic diseases by integrating Whole Genome Sequencing (WGS) and genotyping data from hundreds of thousands of individuals registered in both large biobanks.

By applying a multi-ancestry Bayesian regression technique that simultaneously learns the genetic architectures of diverse populations, the predictive performance of the models in non-European groups improved significantly. In major chronic diseases such as type 2 diabetes, coronary artery disease, and hypertension, the prediction accuracy (AUC) for African American and admixed populations significantly improved compared to existing single-ancestry-based models. Notably, in the African-ancestry cohort, the prediction performance for continuous traits such as systolic blood pressure and body mass index (BMI) improved by up to 40%. This was achieved through an algorithm that selects causal genetic variants acting commonly across multi-ancestry groups and finely corrects for the unique LD structures of each group. This proves that securing genetic diversity, rather than just the absolute expansion of sample size, is the decisive factor in determining the performance of prediction algorithms.

Significance and Outlook

This study demonstrates that expanding data scale and collecting multi-ancestry samples are practical solutions to bridging the ancestry gap in clinical genomics. A turning point has been reached in expanding the scope of precision preventive medicine, which was previously limited to specific racial groups. As multi-ancestry algorithms become widespread, they will further enhance the accuracy of high-risk group identification by reducing false-negative errors occurring in early screening systems. In the global pharmaceutical industry, a path has opened to pre-verify the genetic validity of target discovery across diverse ethnic groups.

However, a cautious approach is required for clinical application. Even though non-European samples have increased, data density is still insufficient to fully reflect the high genetic heterogeneity within African-ancestry groups. Additionally, the interaction of non-genetic factors, such as socioeconomic factors or living environments, with complex diseases remains a task to be addressed. In the future, international data linkage with local cohorts from Asia, South America, and the African continent must follow to achieve true global genomic equity.