Evidence map›Paper›PMID 40189831›Full record

ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2025

Addressing overfitting bias due to sample overlap in polygenic risk scoring.

Seokho Jeong, Manu Shivakumar, Sang-Hyuk Jung, Hong-Hee Won, Kwangsik Nho, Heng Huang, Christos Davatzikos, Andrew J Saykin, Paul M Thompson, Li Shen and 4 more

Abstract read
In one paragraph

Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
–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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Methods for modeling gene-environment interplay using polygenic risk scores.Statistical applications in genetics and molecular biology · 2026
    Review
  5. Article
  6. Addressing overfitting bias due to sample overlap in polygenic risk scoring.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Article
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

14 authors.

Seokho JeongGraduate School of Data Science, Seoul National University, Seoul, Republic of Korea.
Manu ShivakumarDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Sang-Hyuk JungDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Hong-Hee WonSamsung Advanced Institute for Health Sciences and Technology (SAIHST), Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea.
Kwangsik NhoIndiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Heng HuangDepartment of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Christos DavatzikosCenter for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Andrew J SaykinDepartment of Radiology and Imaging Sciences, and Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Paul M ThompsonImaging Genetics Center, Laboratory of Neuro Imaging, Department of Neurology & Psychiatry, UCLA School of Medicine, Los Angeles, California, USA.
Li ShenDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Young Jin KimDivision of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju, Republic of Korea.
Bong-Jo KimDivision of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju, Republic of Korea.
Seunggeun LeeGraduate School of Data Science, Seoul National University, Seoul, Republic of Korea.
Dokyoon KimDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0002-4592-9564

Funding

Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease BiobanksU01AG068057 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Christos Davatzikos, Heng Huang · 2020 to 2026
$20.7M
Translational big data analytic approaches to advance drug repurposing for Alzheimer's diseaseR01AG071470 · NIA · UNIVERSITY OF PENNSYLVANIA · PI KIM, DOKYOON, NING, XIA · 2021 to 2025
$3.8M
Methods for Enhancing Polygenic Risk Prediction Models for Complex DiseaseR01HL169458 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI Dokyoon Kim, MARYLYN D RITCHIE · 2023 to 2026
$3.1M
Unravelling genetic basis of comorbidity using EHR-linked biobank dataR01GM138597 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI KIM, DOKYOON · 2020 to 2023
$2.2M
NHLBI NIH HHS R01 HL169458NIA NIH HHS R01 AG071470NIA NIH HHS U01 AG068057NIGMS NIH HHS R01 GM138597NIH HHS NIH U01 AG068057NIH HHS R01 AG071470NIH HHS R01 GM138597NIH HHS R01 HL169458the Ministry of Science and ICT 2020H1D3A2A03100666
6 · The paper itself

Abstract

introductionNumerous studies on Alzheimer's disease polygenic risk scores (PRSs) overlook sample overlap between International Genomics of Alzheimer's Project (IGAP) and target datasets like Alzheimer's Disease Neuroimaging Initiative (ADNI).

methodsTo address this, we developed overlap-adjusted PRS (OA PRS) and tested it on simulated data to assess biases from different scenarios by varying training, testing, and overlap proportions. OA PRS was used to adjust for sample bias in simulations; then, we applied OA PRS to IGAP and ADNI datasets and validated through visual diagnosis.

resultsOA PRS effectively adjusted for sample overlap in all simulation scenarios, as well as for IGAP and ADNI. The original IGAP PRS showed an inflated area under the receiver operating characteristic (AUROC: 0.915) on overlapping samples. OA PRS reduced the AUROC to 0.726, closely aligning with the AUROC of non-overlapping samples (0.712). Further, visual diagnostics confirmed the effectiveness of our adjustments. DISCUSSION: With OA PRS, we were able to adjust the IGAP summary-based PRS for the overlapped ADNI samples, allowing the dataset to be fully used without the risk of overfitting. HIGHLIGHTS: Sample overlap between large Alzheimer's disease (AD) cohorts poses overfitting bias when using AD polygenic risk scores (PRSs). This study highlighted the effectiveness of overlap-adjusted PRS (OA -PRS) in mitigating overfitting and improving the accuracy of PRS estimations. New PRSs based on adjusted effect sizes showed increased power in association with clinical features.

Indexed as

Alzheimer DiseaseGenetic Predisposition to DiseaseMultifactorial InheritanceAgedBiasComputer SimulationFemaleGenome-Wide Association StudyHumansMaleNeuroimagingRisk FactorsAlzheimer's diseasegenetic risk factorpolygenic risk scoresprecision medicinesample overlap

Identifiers

PMID40189831
PMCPMC11972974

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.