Evidence map›Paper›PMID 40866628›Full record

ArticleNature genetics2025

Cross-biobank generalizability and accuracy of electronic health record-based predictors compared to polygenic scores.

Kira E Detrois, Tuomo Hartonen, Maris Teder-Laving, Bradley Jermy, Kristi Läll, Zhiyu Yang, Estonian Biobank research team, FinnGen, Reedik Mägi, Samuli Ripatti and 1 more

Abstract read
In one paragraph

Article in Nature 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. Article
  2. Article
  3. 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

11 authors.

Kira E Detrois *Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.ORCID http://orcid.org/0000-0002-7193-624X
Tuomo Hartonen *Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.ORCID http://orcid.org/0000-0002-5535-1698
Maris Teder-LavingEstonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.ORCID http://orcid.org/0000-0002-5872-1850
Bradley JermyInstitute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
Kristi LällEstonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
Zhiyu YangInstitute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.ORCID http://orcid.org/0000-0002-8074-838X
Estonian Biobank research team
FinnGen
Reedik MägiEstonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.ORCID http://orcid.org/0000-0002-2964-6011
Samuli RipattiInstitute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.ORCID http://orcid.org/0000-0002-0504-1202
Andrea GannaInstitute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland. andrea.ganna@helsinki.fi.ORCID http://orcid.org/0000-0002-8147-240X

Funding

Academy of Finland (Suomen Akatemia) 323116EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) 101016775
6 · The paper itself

Abstract

Electronic health record (EHR)-based phenotype risk scores (PheRS) leverage individuals' health trajectories to estimate disease risk, similar to how polygenic scores (PGS) use genetic information. While PGS generalizability has been studied, less is known about PheRS generalizability across healthcare systems and whether PheRS are complementary to PGS. We trained elastic-net-based PheRS to predict the onset of 13 common diseases for 845,929 individuals (age = 32-70 years) from three biobank-based studies in Finland (FinnGen), the UK (UKB) and Estonia (EstB). All PheRS were statistically significantly associated with the diseases of interest and most generalized well without retraining when applied to other studies. PheRS and PGS were only moderately correlated and models including both predictors improved onset prediction compared to PGS alone for 8 of 13 diseases. Our results indicate that EHR-based risk scores can transfer well between EHRs, capture largely independent information from PGS, and provide additive benefits for disease risk prediction.

Indexed as

Biological Specimen BanksElectronic Health RecordsMultifactorial InheritanceAdultAgedEstoniaFemaleFinlandGenetic Predisposition to DiseaseHumansMaleMiddle AgedPhenotypeUnited Kingdom

Identifiers

PMID40866628
PMCPMC12425822

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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.