Evidence map›Paper›PMID 42523458›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Local ancestry-informed rare variant burden testing improves gene discovery in admixed populations.

Pragati Kore, Taotao Tan, Wenxuan Lu, Astrid Manuel-Friedman, Linfeng Hu, Nilanjan Chatterjee, Wei Zhou, Ryan S Dhindsa, Elizabeth G Atkinson

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. 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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Pragati KoreDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.ORCID 0000-0003-1877-5819
Taotao TanDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.ORCID 0000-0003-4904-8059
Wenxuan LuDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21218, USA.ORCID 0009-0000-0985-2291
Astrid Manuel-FriedmanDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.ORCID 0000-0001-8303-8116
Linfeng HuProgram in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Nilanjan ChatterjeeDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21218, USA.ORCID 0000-0002-9060-008X
Wei ZhouProgram in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Ryan S DhindsaDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.ORCID 0000-0002-8965-0813
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

Rare-variant association studies enable the discovery of high-impact genetic contributors often missed by conventional genome-wide association studies focused on common variation. However, standard burden tests aggregate variants without accounting for local ancestry in admixed genomes, reducing power when rare variant frequencies or genetic effects differ across ancestral backgrounds. Here, we introduce Tractor-Burden, an ancestry-aware gene-based association method that partitions rare-variant burden by inferred local ancestry and estimates ancestry-specific effects within a unified regression. In simulations, Tractor-Burden is well calibrated and improves power over standard burden tests under effect heterogeneity. Applied to whole-genome sequencing data from 47,152 admixed African-European individuals in the All of Us Research Program, Tractor-Burden recapitulates known associations, including ancestry-enriched effects at

Indexed as

admixtureancestryburden testingrare variants

Identifiers

PMID42523458
PMCPMC13405371

What Socratic holds

Textmetadata
LicenceCC BY-NC
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Registered trials

None linked

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 Socratic graph.