Evidence map›Paper›PMID 42323305›Full record

ArticleNature communications2026

PGS Browser: a public platform for personalized polygenic score analysis and interpretation.

Nikita Kolosov, Mary P Reeve, Pietro Della Briotta Parolo, Mitja I Kurki, FinnGen, Vincent Llorens, Timo Petteri Sipila, Adam Herman, Ivan Molotkov, Mervi Aavikko and 4 more

Abstract read
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. Not yet cited in PubMed.

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

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

14 authors.

Nikita KolosovThe Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA.ORCID http://orcid.org/0000-0002-2139-6775
Mary P ReeveInstitute of Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland.
Pietro Della Briotta ParoloInstitute of Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland.
Mitja I KurkiInstitute of Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland.
FinnGen
Vincent LlorensInstitute of Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland.
Timo Petteri SipilaInstitute of Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland.ORCID http://orcid.org/0000-0001-6739-1606
Adam HermanThe Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA.
Ivan MolotkovThe Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA.
Mervi AavikkoInstitute of Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland.ORCID http://orcid.org/0000-0003-2583-0150
Samuli RipattiInstitute of Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland.ORCID http://orcid.org/0000-0002-0504-1202
Aarno PalotieInstitute of Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland.ORCID http://orcid.org/0000-0002-2527-5874
Mark J DalyInstitute of Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland.ORCID http://orcid.org/0000-0002-0949-8752
Mykyta ArtomovThe Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, USA. mykyta.artomov@nationwidechildrens.org.ORCID http://orcid.org/0000-0001-5282-8764

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Polygenic scores (PGSs) quantify individual genetic susceptibility to complex diseases and can identify high-risk individuals well before clinical onset. Their clinical translation, however, requires population-based reference resources, standardized benchmarking, and accessible tools for translating individual scores into disease likelihood. In this article, we systematically evaluate 3168 PGS models, primarily from the PGS Catalog, in 473,681 FinnGen participants, placing all models on a common performance scale to enable cross-model and cross-trait comparison. For each PGS, we create ancestry-adjusted reference distributions, providing a biobank-scale resource for interpreting individual scores. We perform phenome-wide association studies for each PGS, identifying 439,070 significant phenotypic associations, demonstratin g that integrating multiple scores improves predictive performance for most complex diseases, and providing public access to 11 top-performing interactive time-to-event models. All resources are accessible through the PGS Browser ( pgs.nchigm.org ), which offers a population-aware framework for score interpretation and lays groundwork for the clinical application of PGSs.

Indexed as

Genetic Predisposition to DiseaseMultifactorial InheritanceDatabases, GeneticGenetic Risk ScoreGenome-Wide Association StudyHumansPhenotypePolymorphism, Single Nucleotide

Identifiers

PMID42323305
PMCPMC13439439

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.