Evidence map›Paper›PMID 40203832›Full record

ArticleAmerican journal of human genetics2025

EndoPRS: Incorporating endophenotype information to improve polygenic risk scores for clinical endpoints-A study in asthma.

Elena V Kharitonova, Quan Sun, Franklin Ockerman, Brian Chen, Laura Y Zhou, Micah R Hysong, Bjoernar Tuftin, Hongyuan Cao, Rasika A Mathias, Paul L Auer and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article 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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors.

Elena V KharitonovaDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Quan SunDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Center for Computation and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
Franklin OckermanDepartment of Biostatistics, 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.
Laura Y ZhouDepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
Micah R HysongDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Bjoernar TuftinDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Hongyuan CaoDepartment of Statistics, Florida State University, Tallahassee, FL 32306, USA.
Rasika A MathiasDepartment of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
Paul L AuerDivision of Biostatistics, Data Science Institute, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
Carole OberDepartment of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
Laura M RaffieldDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Alexander P ReinerDepartment of Epidemiology, University of Washington, Seattle, WA 98105, USA.
Nancy J CoxDivision of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Samir N P KeladaDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Ran TaoVanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA. Electronic address: r.tao@vumc.org.
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; Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. Electronic address: yun_li@med.unc.edu.

Funding

Back Pain Consortium (BACPAC) Research Program Data Integration, Algorithm Development and Operations Management CenterU24AR076730 · NIAMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ANSTROM, KEVIN J, IVANOVA, ANASTASIA · 2019 to 2024
$60.0M
Polygenic Risk Scores for Diverse Populations - Bridging Research and Clinical CareR01HL151152 · NHLBI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Christy Leigh Avery, Jennifer Below · 2020 to 2026
$12.3M
Next generation functional genomics of hematology traitsR01HL146500 · NHLBI · UNIVERSITY OF WASHINGTON · PI ALEXANDER P REINER · 2020 to 2026
$5.7M
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
NHGRI NIH HHS U01 HG011720NHLBI NIH HHS R01 HL146500NHLBI NIH HHS R01 HL151152NIAMS NIH HHS U24 AR076730
6 · The paper itself

Abstract

Polygenic risk score (PRS) prediction of complex diseases can be improved by leveraging related phenotypes. This has motivated the development of several multi-trait PRS methods that jointly model genetically correlated traits. However, these methods do not account for vertical pleiotropy, where one trait acts as a mediator for another. Here, we introduce endoPRS, a weighted lasso model that incorporates information from relevant endophenotypes to improve disease risk prediction without making assumptions about the genetic architecture underlying the endophenotype-disease relationship. Through extensive simulation analysis, we demonstrate the robustness of endoPRS in a variety of complex genetic frameworks. We also apply endoPRS to predict the risk of childhood-onset asthma in UK Biobank and All of Us by leveraging a paired genome-wide association study of eosinophil count, a relevant endophenotype. We find that endoPRS significantly improves prediction and transferability compared to many existing PRS methods, including multi-trait PRS methods MTAG and wMT-BLUP, which suggests advantages of endoPRS in real-life clinical settings.

Indexed as

AsthmaEndophenotypesGenetic Predisposition to DiseaseMultifactorial InheritanceChildEosinophilsFemaleGenetic Risk ScoreGenome-Wide Association StudyHumansMaleModels, GeneticPhenotypePolymorphism, Single NucleotideRisk Factorschildhood-onset asthmaendophenotypeseosinophilsmulti-trait polygenic risk scoreweighted lasso regression

Identifiers

PMID40203832
PMCPMC12120184

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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.