Evidence map›Paper›PMID 40394127›Full record

ArticleScientific reports2025

Optimization of multi-ancestry polygenic risk score disease prediction models.

Jon Lerga-Jaso, Andrew Terpolovsky, Biljana Novković, Alex Osama, Charlie Manson, Sandra Bohn, Adriano De Marino, Mark Kunitomi, Puya G Yazdi

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Clinical utility of polygenic risk scores in cardiovascular disorders.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2026
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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

9 authors.

Jon Lerga-JasoResearch & Development, Omics Edge, Miami, FL, USA.
Andrew TerpolovskyResearch & Development, Omics Edge, Miami, FL, USA.
Biljana NovkovićResearch & Development, Omics Edge, Miami, FL, USA.
Alex OsamaResearch & Development, Omics Edge, Miami, FL, USA.
Charlie MansonResearch & Development, Omics Edge, Miami, FL, USA.
Sandra BohnResearch & Development, Omics Edge, Miami, FL, USA.
Adriano De MarinoResearch & Development, Omics Edge, Miami, FL, USA.
Mark KunitomiAlmaden Genomics, San Francisco, CA, USA.
Puya G YazdiResearch & Development, Omics Edge, Miami, FL, USA. pyazdi@omicsedge.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Polygenic risk scores (PRS) have ushered in a new era in genetic epidemiology, offering insights into individual predispositions to a wide range of diseases. However, despite recent marked enhancements in predictive power, PRS-based models still need to overcome several hurdles before they can be broadly applied in the clinic. Chiefly, they need to achieve sufficient accuracy, easy interpretability and portability across diverse populations. Leveraging trans-ancestry genome-wide association study (GWAS) meta-analysis, we generated novel, diverse summary statistics for 30 medically-related traits and benchmarked the performance of six existing PRS algorithms using UK Biobank. We built an ensemble model using logistic regression to combine outputs of top-performing algorithms and validated it on the diverse eMERGE and PAGE MEC cohorts. It surpassed current state-of-the-art PRS models, with minimal performance drops in external cohorts, indicating good calibration. To enhance predictive accuracy for clinical application, we incorporated easily-accessible clinical characteristics such as age, gender, ancestry and risk factors, creating disease prediction models intended as prospective diagnostic tests, with easily interpretable positive or negative outcomes. After adding clinical characteristics, 12 out of 30 models surpassed 80% AUC. Further, 25 traits exceeded the diagnostic odds ratio (DOR) of five, and 19 traits exceeded DOR of 10 for all ancestry groups, indicating high predictive value. Our PRS model for coronary artery disease identified 55-80 times more true coronary events than rare pathogenic variant models, reinforcing its clinical potential. The polygenic component modulated the effect of high-risk rare variants, stressing the need to consider all genetic components in clinical settings. These findings show that newly developed PRS-based disease prediction models have sufficient accuracy and portability to warrant consideration of being used in the clinic.

Indexed as

Genetic Predisposition to DiseaseMultifactorial InheritanceAlgorithmsFemaleGenetic Risk ScoreGenome-Wide Association StudyHumansMaleModels, GeneticPolymorphism, Single NucleotideRisk AssessmentRisk Factors

Identifiers

PMID40394127
PMCPMC12092622

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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.