medRxivGenetic & Genomic MedicinePreprint
A unified framework for local-ancestry-aware genetic association analysis across biobanks
Linfeng HuiD, Taotao TaniD, Kai YuaniD, Ying WangiD, Bram L. GorisseniD, Yi-Sian LiniD, Pragati KoreiD, Wenhan LuiD, Ravi MandlaiD, Zhuozheng ShiiD, Kangcheng HouiD, Konrad J. KarczewskiiD, Hailiang HuangiD, Benjamin M. NealeiD, Mark J. DalyiD, Alicia R. MartiniD, Bogdan PaşaniuciD, Elizabeth G. AtkinsoniD, Wei ZhouiD
Abstract
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.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 0
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Biobank-Scale Polygenic Prediction in Admixed Populations Using Local Ancestry via the Group Lasso
David Bonet, James Yang, … Alexander G. Ioannidis·bioRxiv·Mar 16, 2026
- Admixture mapping identifies complex trait associations with local ancestry in the All of Us Research Program
Wilfredo G Gonzalez Rivera, Youwen Liu, … Melissa Gymrek·medRxiv·Dec 29, 2025
- Tractor: A framework allowing for improved inclusion of admixed individuals in large-scale association studies.
Elizabeth G. Atkinson, Adam X. Maihofer, … Benjamin M. Neale·Nature GeneticsPeer-reviewed·Jan 18, 2021
- Ancestry-specific association mapping in admixed populations
Line Skotte, Emil Jørsboe, … Anders Albrechtsen·Genetic EpidemiologyPeer-reviewed·Mar 18, 2019
- Pan-UK Biobank GWAS improves discovery, analysis of genetic architecture, and resolution into ancestry-enriched effects
Konrad J. Karczewski, Rahul Gupta, … Alicia R. Martin·medRxiv·Mar 13, 2024
- Spatial localization of recent ancestors for admixed individuals
Wen-Yun Yang, Alexander Platt, … Bogdan Pasaniuc·bioRxiv·May 4, 2014