Biobanks increasingly include individuals with admixed genomes, yet conventional genome-wide association study frameworks either exclude participants who cannot be confidently assigned to a discrete ancestry group or ignore ancestry-specific effects. We present FELIX, a scalable framework for local-ancestry-aware genetic analysis that retains all participants without requiring discrete ancestry assignment. FELIX combines a compact ancestry-resolved genotype representation (FELIXla) with an adaptive association test that jointly evaluates shared-effect and ancestry-specific models at each variant (FELIXassoc). Simulations demonstrated well-calibrated inference under case-control imbalance and power that adapted to the locus-optimal model. Across 24 phenotypes in 240,038 All of Us participants, FELIX analyzed the 12.1% of individuals excluded by global-ancestry clustering and identified 15.4% more genome-wide significant loci than global-ancestry meta-analysis. Additional discoveries arose from recovering ancestry-specific haplotypes carried by admixed participants and from detecting ancestry-dependent marginal effects. Full-cohort effect estimates also improved polygenic score prediction across ancestries and traits.
L. Hu, T. Tan, K. Yuan et al.· medRxiv· 0 citations
Polygenic risk scores (PRSs) trained on multiancestry data can improve prediction in under-represented groups, but large linked genetic and health datasets capturing broad human diversity remain limited. Using 245,388 whole-genome sequences from the All of Us research program (AoU) together with UK Biobank data, we developed multiancestry PRSs for 32 traits and diseases. We evaluated how ancestry, methodology and genetic architecture influenced PRS performance across ancestrally diverse AoU participants. Increased diversity in the AoU improved PRS accuracy for several traits, especially in under-represented populations. However, maximizing sample size by meta-analyzing AoU and UK Biobank was not universally optimal: for less polygenic traits, AoU-only training performed best in African ancestry participants, consistent with ancestry-enriched effects. Individual PRS accuracy declined linearly with increasing ancestry divergence from the discovery GWAS, but this decay was attenuated using multiancestry training data. These findings underscore the value of more representative biobanks for equitable PRS performance.
K. Tsuo, Zhuozheng Shi, Tian Ge et al.· Nature Genetics· 0 citations
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