Improved allele frequencies in gnomAD through local ancestry inference

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. Karczewski and Elizabeth G. Atkinson ()
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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
Mark J. Daly: The Broad Institute of MIT and Harvard
Konrad J. Karczewski: The Broad Institute of MIT and Harvard
Elizabeth G. Atkinson: Baylor College of Medicine

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-63340-2

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