Evidence map›Paper›PMID 40661606›Full record

ArticlebioRxiv : the preprint server for biology2025

Improved Allele Frequencies in gnomAD through Local Ancestry Inference.

Pragati Kore, Michael W Wilson, Grace Tiao, Katherine Chao, Philip W Darnowsky, Nick Watts, Jessica Honorato Mauer, Samantha M Baxter, Genome Aggregation Database Consortium, Heidi L Rehm and 3 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Pragati KoreDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.ORCID 0000-0003-1877-5819
Michael W WilsonProgram in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Grace TiaoProgram in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Katherine ChaoProgram in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.ORCID 0000-0003-2897-2410
Philip W DarnowskyProgram in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Nick WattsProgram in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Jessica Honorato MauerDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.
Samantha M BaxterProgram in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Genome Aggregation Database Consortium
Heidi L RehmProgram in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Mark J DalyProgram in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Konrad J KarczewskiProgram in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Elizabeth G AtkinsonDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.ORCID 0000-0002-6308-776X

Funding

Genetics & Genomics Training ProgramT32GM139534 · NIGMS · BAYLOR COLLEGE OF MEDICINE · PI GAD SHAULSKY · 2021 to 2026
$4.8M
Empowering gene discovery and accelerating clinical translation for diverse admixed populationsR01HG012869 · NHGRI · BAYLOR COLLEGE OF MEDICINE · PI Elizabeth Grace Atkinson · 2023 to 2026
$3.1M
The Clinical Translational Research Certificate of Added Qualification ProgramT32GM136554 · NIGMS · BAYLOR COLLEGE OF MEDICINE · PI Melissa Suter, IGNATIA B VAN DEN VEYVER · 2020 to 2026
$3.0M
A framework enabling the genomic analysis of psychiatric traits across admixed populations.K01MH121659 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI ATKINSON, ELIZABETH GRACE · 2019 to 2022
$719k
NHGRI NIH HHS R01 HG012869NIGMS NIH HHS T32 GM136554NIGMS NIH HHS T32 GM139534NIMH NIH HHS K01 MH121659
6 · The paper itself

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 = 7,612) 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.

Indexed as

admixtureallele frequencyancestryreference panel

Identifiers

PMID40661606
PMCPMC12258901

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.