Evidence map›Paper›PMID 41904631›Full record

ArticleHGG advances2026

Explicitly modeling genetic ancestry to improve polygenic prediction accuracy for height in a large, admixed cohort of US Latinos: Findings from HCHS/SOL.

Xin Wang, Tamar Sofer, Oleksandr Frei, Robert Kaplan, Krista M Perreira, Nora Franceschini, Humberto Parada, Laura Zhou, Ole A Andreassen, Hector Gonzalez and 2 more

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In one paragraph

Article in HGG advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

12 authors.

Xin WangDepartment of Neurosciences, University of California, San Diego, San Diego, CA 92093, USA.
Tamar SoferCardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02215, USA.
Oleksandr FreiInstitute of Clinical Medicine, University of Oslo, 0316 Oslo, Norway; NORMENT, K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, 0316 Oslo, Norway.
Robert KaplanDepartment of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
Krista M PerreiraDepartment of Public Policy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Nora FranceschiniDepartment of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA.
Humberto ParadaDivision of Epidemiology and Biostatistics, School of Public Health, San Diego State University, San Diego, CA 92182, USA.
Laura ZhouDepartment of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA.
Ole A AndreassenInstitute of Clinical Medicine, University of Oslo, 0316 Oslo, Norway; NORMENT, K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, 0316 Oslo, Norway.
Hector GonzalezDepartment of Neurosciences, University of California, San Diego, San Diego, CA 92093, USA.
Anders M DaleDepartment of Neurosciences, University of California, San Diego, San Diego, CA 92093, USA.
Iris J BroceDepartment of Neurosciences, University of California, San Diego, San Diego, CA 92093, USA; Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA 94143, USA. Electronic address: ibroce@health.ucsd.edu.

Funding

Novel imaging genetic biomarkers for sporadic frontotemporal dementia through machine learningK01AG070376 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BROCE-DIAZ, IRIS J · 2021 to 2025
$528k
NIA NIH HHS K01 AG070376
6 · The paper itself

Abstract

Polygenic scores (PGSs) offer moderate to high prediction accuracy for complex traits, but most are developed in European ancestry cohorts, reducing their performance in populations of other ancestries. This study aimed to improve standing height prediction, a heritable and ancestry-influenced trait, in an admixed Latino cohort, the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), by modeling ancestry using principal components (PCs) alongside PGSs. SNPs were selected from a large European ancestry genome-wide association study (GWAS) using various p value thresholds, and weights were trained using traditional and penalized regression in the UK Biobank (UKB). PGSs with PCs were trained separately in the HCHS/SOL and UKB. Compared to PGSs alone, modeling PGSs with PCs moderately improved height prediction in the HCHS/SOL (squared correlation [R

Indexed as

Body HeightHispanic or LatinoModels, GeneticMultifactorial InheritanceCohort StudiesFemaleGenetic Risk ScoreGenome-Wide Association StudyHumansMalePolymorphism, Single NucleotideUnited Statesadmixed populationgenetic ancestryheight predictionHispanic Community Health Study/Study of Latinospolygenic scoresprincipal componentsUK Biobank

Identifiers

PMID41904631
PMCPMC13157043

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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.