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Improved allele frequencies in gnomAD through local ancestry inference

Author

Listed:
  • Pragati Kore

    (Baylor College of Medicine)

  • Michael W. Wilson

    (The Broad Institute of MIT and Harvard)

  • Grace Tiao

    (The Broad Institute of MIT and Harvard)

  • Katherine Chao

    (The Broad Institute of MIT and Harvard)

  • Philip W. Darnowsky

    (The Broad Institute of MIT and Harvard)

  • Nicholas A. Watts

    (The Broad Institute of MIT and Harvard)

  • Jessica Honorato Mauer

    (Baylor College of Medicine)

  • Samantha M. Baxter

    (The Broad Institute of MIT and Harvard)

  • Heidi L. Rehm

    (The Broad Institute of MIT and Harvard
    Massachusetts General Hospital)

  • Mark J. Daly

    (The Broad Institute of MIT and Harvard
    The Broad Institute of MIT and Harvard
    Massachusetts General Hospital
    Institute for Molecular Medicine Finland)

  • Konrad J. Karczewski

    (The Broad Institute of MIT and Harvard
    The Broad Institute of MIT and Harvard
    Massachusetts General Hospital
    Broad Institute of MIT and Harvard)

  • Elizabeth G. Atkinson

    (Baylor College of Medicine
    The Broad Institute of MIT and Harvard
    Massachusetts General Hospital
    Texas Children’s Hospital)

Abstract

The Genome Aggregation Database (gnomAD) is a foundational resource for allele frequency data, widely used in genomic research and clinical interpretation. However, traditional estimates rely on individual-level genetic ancestry groupings that may obscure variation in recently admixed populations. To improve resolution, we applied local ancestry inference (LAI) to over 27 million variants in two admixed groups: Admixed American (n = 7612) and African/African American (n = 20,250), deriving ancestry-specific allele frequencies. We show that 78.5% and 85.1% of variants in these groups, respectively, exhibit at least a twofold difference in ancestry-specific frequencies. Moreover, 81.49% of variants with LAI information would be assigned a higher gnomAD-wide maximum frequency after incorporating LAI, potentially altering clinical interpretations. This LAI-informed release reveals clinically relevant frequency differences that are masked in aggregate estimates and may support reclassifying some variants from Uncertain Significance to Benign or Likely Benign.

Suggested Citation

  • Pragati Kore & Michael W. Wilson & Grace Tiao & Katherine Chao & Philip W. Darnowsky & Nicholas A. Watts & Jessica Honorato Mauer & Samantha M. Baxter & Heidi L. Rehm & Mark J. Daly & Konrad J. Karcze, 2025. "Improved allele frequencies in gnomAD through local ancestry inference," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-63340-2
    DOI: 10.1038/s41467-025-63340-2
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