Evidence map›Paper›PMID 42062286›Full record

ArticleNature communications2026

Admixture-informed polygenic risk reporting using the ePRS framework.

Yu-Jyun Huang, Nuzulul Kurniansyah, Matthew O Goodman, Brian W Spitzer, Jiongming Wang, Adrienne Stilp, Cecelia Laurie, Paul S de Vries, Han Chen, Yuan-I Min and 23 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

33 authors.

Yu-Jyun HuangDepartment of Medicine, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0002-7851-2722
Nuzulul KurniansyahDepartment of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Matthew O GoodmanDepartment of Medicine, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0002-3982-7940
Brian W SpitzerCardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA.
Jiongming WangDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0009-0003-1350-390X
Adrienne StilpDepartment of Biostatistics, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0002-3910-0776
Cecelia LaurieDepartment of Biostatistics, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0001-6569-2501
Paul S de VriesHuman Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.ORCID http://orcid.org/0000-0003-0964-0111
Han ChenHuman Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.ORCID http://orcid.org/0000-0002-9510-4923
Yuan-I MinDepartment of Medicine, University of Mississippi Medical Center, Jackson, MS, USA.ORCID http://orcid.org/0000-0003-2470-0004
Mario SimsDepartment of Social Medicine, Population and Public Health, University of California at Riverside School of Medicine, Riverside, CA, USA.
Gina M PelosoDepartment of Biostatistics, Boston University School of Public Health, Boston, MA, USA.ORCID http://orcid.org/0000-0002-5355-8636
Xiuqing GuoThe Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.ORCID http://orcid.org/0000-0002-5264-5068
Joshua C BisCardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0002-3409-1110
Jennifer A BrodyCardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0001-8509-148X
Laura M RaffieldDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.ORCID http://orcid.org/0000-0002-7892-193X
Jennifer A SmithDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0002-3575-5468
Wei ZhaoDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0001-7388-0692
Jerome I RotterThe Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.ORCID http://orcid.org/0000-0001-7191-1723
Stephen S RichDepartment of Genome Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA.ORCID http://orcid.org/0000-0003-3872-7793
Susan RedlineDepartment of Medicine, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0002-6585-1610
Myriam FornageHuman Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.ORCID http://orcid.org/0000-0003-0677-8158
Robert KaplanDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Nora FranceschiniDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.ORCID http://orcid.org/0009-0001-8346-3662
Daniel LevyThe Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA.
Alanna C MorrisonHuman Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.ORCID http://orcid.org/0000-0001-6381-4296
Eric BoerwinkleHuman Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Nicholas L SmithKaiser Permanente Washington Health Research Institute, Seattle, WA, USA.
Charles KooperbergDivision of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.ORCID http://orcid.org/0000-0002-7986-8560
Bruce M PsatyCardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0002-7278-2190
Sebastian ZöllnerDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0001-7362-156X
Trans-Omics for Precision Medicine Consortium
Tamar SoferDepartment of Medicine, Harvard Medical School, Boston, MA, USA. tsofer@bidmc.harvard.edu.ORCID http://orcid.org/0000-0001-8520-8860

Funding

Using polygenic risk scores and omics to study how suboptimal sleep accelerates cognitive aging in diverse populationsR01AG080598 · NIA · BETH ISRAEL DEACONESS MEDICAL CENTER · PI Tamar Sofer · 2023 to 2026
$3.6M
Leveraging omics data to understand sleep health and its consequences among diverse Hispanics/LatinosR01HL161012 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI Tamar Sofer · 2022 to 2026
$3.2M
Infrastructure for mentored access to CHS data and specimensR01HL172803 · NHLBI · UNIVERSITY OF WASHINGTON · PI James S Floyd, Michelle Christina Odden · 2025 to 2026
$2.7M
NHLBI NIH HHS R01 HL161012NHLBI NIH HHS R01 HL172803NIA NIH HHS R01 AG080598U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) R01AG080598
6 · The paper itself

Abstract

Polygenic risk score values vary with genetic ancestry due to differences in population-specific allele frequencies and linkage disequilibrium patterns. We present a framework to calibrate polygenic risk scores based on ancestral makeup. We propose the "expected polygenic risk score" or ePRS, defined as the expected value of a polygenic risk score based on one's global or local admixture patterns. We further define the "residual polygenic risk score" or rPRS as measuring the deviation of the polygenic risk score from the ePRS. The ePRS reflects the baseline ancestry-driven component of genetic risk, whereas the rPRS isolates an ancestry-agnostic measure of genetic liability. Simulation studies confirm that it suffices to adjust for ePRS to obtain nearly unbiased estimates of the polygenic risk score-outcome association without further adjusting for principal components. Using the TOPMed and the All of Us datasets, effect size estimates for the rPRS (adjusted for ePRS) are similar to those obtained from polygenic risk scores adjusting for genetic principal components. The ePRS framework can protect from population stratification in association analysis and provide an equitable strategy to interpret genetic risk across diverse populations.

Indexed as

Genetic Predisposition to DiseaseMultifactorial InheritanceComputer SimulationGene FrequencyGenetic Risk ScoreGenome-Wide Association StudyHumansLinkage DisequilibriumModels, GeneticPolymorphism, Single Nucleotide

Identifiers

PMID42062286
PMCPMC13323344

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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.