Genomic Disaggregation Reveals Distinct Admixture Patterns and Cardiometabolic Risk Loci in Black Hawaiians

Background The systematic aggregation of distinct admixed subpopulations into broad racial categories creates genomic blind spots that undermine the promise of precision medicine. Black Hawaiians (BH) exemplify this exclusion. Characterized by a unique tri-continental ancestry (African, European, and Native Hawaiian/Pacific Islander) and disproportionate cardiometabolic burden, their population-specific risk drivers remain masked by systematic conflation with broader ancestral cohorts. Methods We performed the first comprehensive genomic analysis of 287 BH participants from the NIH All of Us Research Program using whole-genome sequencing (WGS). Following haplotype phasing (SHAPEIT5), we characterized population structure (ADMIXTURE, PCA), inferred local ancestry tracts (RFMix), and reconstructed demographic history (SMC++). Genome-wide allele frequency differentiation (AFD) was calculated against tri-continental reference panels, and Electronic Health Record (EHR) data were integrated to quantify the population’s cardiometabolic burden. Results The cohort exhibited complex tri-continental admixture (mean: 67.0% African, 22.1% European, 10.9% NHPI) with high inter-individual heterogeneity. Phenotypic analysis confirmed a substantial disease burden (34.8% hypertension, mean BMI 31.2 kg/m 2 ), while SMC++ reconstruction revealed a sharp demographic bottleneck in recent generations. Genome-wide AFD analysis of 8.9M variants demonstrated systematic differentiation (mean Δ vs African: 0.041, NHPI: 0.069, European: 0.084). The top 100 differentiated variants mapped to 31 unique genes, identifying distinct candidates including MYO9A , RAB37, and PEAR1 . Notably, differentiation in the cytoskeletal regulator MYO9A suggests a mechanostructural etiology for kidney disease distinct from classical APOL1 cytotoxicity, while PEAR1 variants implicate population-specific pharmacogenomic resistance to antiplatelet therapy. Conclusion This study highlights the critical necessity of data disaggregation in genomic research, using the Black Hawaiian population as a paradigmatic example. By distinguishing this community from broader aggregate groups, we uncovered a distinct genomic architecture with unique admixture patterns that drive specific cardiometabolic risks. These findings demonstrate the necessity of granular resolution for achieving equitable precision medicine.