Evidence map›Paper›PMID 40185073›Full record

ReviewAmerican journal of human genetics2025

Opportunities and challenges of local ancestry in genetic association analyses.

Quan Sun, Andrea R V R Horimoto, Brian Chen, Frank Ockerman, Karen L Mohlke, Elizabeth Blue, Laura M Raffield, Yun Li

Abstract readReview
In one paragraph

Review in American journal of human genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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  5. Article
  6. Graph transformer for ancient ancestry inference.bioRxiv : the preprint server for biology · 2026
    Article
  7. Review
  8. Review
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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

8 authors.

Quan SunDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Center for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA. Electronic address: sunq@chop.edu.
Andrea R V R HorimotoDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Brian ChenDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Frank OckermanDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Karen L MohlkeDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Elizabeth BlueDivision of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA; Brotman Baty Institute, Seattle, WA 98195, USA.
Laura M RaffieldDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Yun LiDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. Electronic address: yunli@med.unc.edu.

Funding

Study of Selective Cell and System Vulnerability in Alzheimer's DiseaseR01AG079291 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Li Gan, Yun Li · 2023 to 2026
$5.4M
Polygenic risk scores for cardiometabolic disorders: the role of blood cells immune response and evolutionary adaptationU01HG011720 · NHGRI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Yun Li, ALEXANDER P REINER · 2021 to 2026
$5.4M
Construction and Application of Comprehensive Knowledge Graphs for Alzheimer's DiseaseR01AG085581 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Yun Li, Hongtu Zhu · 2024 to 2026
$3.7M
Genetics of Cardiovascular Disease in Chronic Kidney DiseaseR01HL163972 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI FRANCESCHINI, NORA, LI, YUN · 2022 to 2025
$1.9M
NHGRI NIH HHS U01 HG011720NHLBI NIH HHS R01 HL163972NIA NIH HHS R01 AG079291NIA NIH HHS R01 AG085581
6 · The paper itself

Abstract

Recently, admixed populations make up an increasing percentage of the US and global populations, and the admixture is not uniform over space or time or across genomes. Therefore, it becomes indispensable to evaluate local ancestry in addition to global ancestry to improve genetic epidemiological studies. Recent advances in representing human genome diversity, coupled with large-scale whole-genome sequencing initiatives and improved tools for local ancestry inference, have enabled studies to demonstrate that incorporating local ancestry information enhances both genetic association analyses and polygenic risk predictions. Along with the opportunities that local ancestry provides, there exist challenges preventing its full usage in genetic analyses. In this review, we first summarize methods for local ancestry inference and illustrate how local ancestry can be utilized in various analyses, including admixture mapping, association testing, and polygenic risk score construction. In addition, we discuss current challenges in research involving local ancestry, both in terms of the inference itself and its role in genetic association studies. We further pinpoint some future study directions and methodology development opportunities to help more effectively incorporate local ancestry in genetic analyses. It is worth the effort to pursue those future directions and address these analytical challenges because the appropriate use of local ancestry estimates could help mitigate inequality in genomic medicine and improve our understanding of health and disease outcomes.

Indexed as

Genetic Association StudiesGenetics, PopulationGenome, HumanGenome-Wide Association StudyHumansMultifactorial Inheritanceadmixed populationsadmixture mappingancestry inferencediverse populationsgenetic association studieslocal ancestrypolygenic scorespopulation genetics

Identifiers

PMID40185073
PMCPMC12081280

What OpenQuestion holds

Textmetadata
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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 OpenQuestion graph.