PGS Publication: PGP000794

Publication Information (EuropePMC)
Title Genomic risk prediction of cardiovascular diseases among type 2 diabetes patients in the UK Biobank.
PubMed ID 38239805(Europe PMC)
doi 10.3389/fbinf.2023.1320748
Publication Date Jan. 4, 2024
Journal Front Bioinform
Author(s) Ye Y, Hu J, Pang F, Cui C, Zhao H.
Released in PGS Catalog: April 13, 2026

Associated Polygenic Score(s)

Filter PGS by Participant Ancestry
Individuals included in:
G - Source of Variant Associations (GWAS)
D - Score Development/Training
E - PGS Evaluation
List of ancestries includes:
Display options:
Ancestry legend
Multi-ancestry (including European)
Multi-ancestry (excluding European)
African
East Asian
South Asian
Additional Asian Ancestries
European
Greater Middle Eastern
Hispanic or Latin American
Additional Diverse Ancestries
Not Reported

PGS Developed By This Publication

Polygenic Score ID & Name PGS Publication ID (PGP) Reported Trait Mapped Trait(s) (Ontology) Number of Variants Ancestry distribution
GWAS
Dev
Eval
 Scoring File (FTP Link)
PGS012551
(PRS_stroke)
 PGP000794 |
Ye Y et al. Front Bioinform (2024)
 Stroke stroke disorder 1,997,066
-
 https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS012551/ScoringFiles/PGS012551.txt.gz
PGS012552
(PRS_HF)
 PGP000794 |
Ye Y et al. Front Bioinform (2024)
 Heart failure heart failure 1,935,626
-
 https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS012552/ScoringFiles/PGS012552.txt.gz

External PGS Evaluated By This Publication

Polygenic Score ID & Name PGS Publication ID (PGP) Reported Trait Mapped Trait(s) (Ontology) Number of Variants Ancestry distribution
GWAS
Dev
Eval
 Scoring File (FTP Link)
PGS005236
(CAD_AnnoPred)
 PGP000740 |
Hu J et al. PLoS Comput Biol (2025)
 Coronary artery disease coronary artery disorder 2,994,055
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS005236/ScoringFiles/PGS005236.txt.gz

Performance Metrics

Disclaimer: The performance metrics are displayed as reported by the source studies. It is important to note that metrics are not necessarily comparable with each other. For example, metrics depend on the sample characteristics (described by the PGS Catalog Sample Set [PSS] ID), phenotyping, and statistical modelling. Please refer to the source publication for additional guidance on performance.

PGS Performance
Metric ID (PPM) 		Evaluated Score PGS Sample Set ID
(PSS) 		Performance Source Trait PGS Effect Sizes
(per SD change) 		Classification Metrics Other Metrics Covariates Included in the Model PGS Performance:
Other Relevant Information
PPM030660 PGS012551
(PRS_stroke)
 PSS012229|
European Ancestry|
21,092 individuals
 PGP000794 |
Ye Y et al. Front Bioinform (2024)
 Reported Trait: Stroke — AUROC: 0.542 [0.499, 0.585] AUROC of meta-PRS (PRS_CAD + PRS_IS + PRS_HF) for stroke: 0.523 [0.48, 0.566] PRS_CAD (PGS Catalog ID: PGS005236) + PRS_IS (this paper) + PRS_HF (this paper) combined to derive meta-PRS The meta-PRS was constructed by combining PRS_CAD, PRS_IS, and PRS_HF at the PRS level using weights described in the study. Implementation details are available at: https://github.com/JqiHu/meta-PRS-CVD
PPM030661 PGS012552
(PRS_HF)
 PSS012228|
European Ancestry|
21,092 individuals
 PGP000794 |
Ye Y et al. Front Bioinform (2024)
 Reported Trait: HF — AUROC: 0.524 [0.495, 0.554] AUROC of meta-PRS (PRS_CAD + PRS_IS + PRS_HF) for HF: 0.558 [0.528, 0.588] PRS_CAD (PGS Catalog ID: PGS005236) + PRS_IS (this paper) + PRS_HF (this paper) combined to derive meta-PRS The meta-PRS was constructed by combining PRS_CAD, PRS_IS, and PRS_HF at the PRS level using weights described in the study. Implementation details are available at: https://github.com/JqiHu/meta-PRS-CVD
PPM030662 PGS005236
(CAD_AnnoPred)
 PSS012227|
European Ancestry|
18,732 individuals
 PGP000794 |
Ye Y et al. Front Bioinform (2024)
|Ext.		 Reported Trait: CAD — AUROC: 0.597 [0.571, 0.623] AUROC of meta-PRS (PRS_CAD + PRS_IS + PRS_HF) for CAD: 0.598 [0.573, 0.625] PRS_CAD (PGS Catalog ID: PGS005236) + PRS_IS (this paper) + PRS_HF (this paper) combined to derive meta-PRS The meta-PRS was constructed by combining PRS_CAD, PRS_IS, and PRS_HF at the PRS level using weights described in the study. Implementation details are available at: https://github.com/JqiHu/meta-PRS-CVD
PPM030663 PGS005236
(CAD_AnnoPred)
 PSS012227|
European Ancestry|
18,732 individuals
 PGP000794 |
Ye Y et al. Front Bioinform (2024)
|Ext.		 Reported Trait: Late CAD — AUROC: 0.549 [0.525, 0.573] AUROC of meta-PRS (PRS_CAD + PRS_IS + PRS_HF) for late CAD: 0.557 [0.532, 0.581] PRS_CAD (PGS Catalog ID: PGS005236) + PRS_IS (this paper) + PRS_HF (this paper) combined to derive meta-PRS The meta-PRS was constructed by combining PRS_CAD, PRS_IS, and PRS_HF at the PRS level using weights described in the study. Implementation details are available at: https://github.com/JqiHu/meta-PRS-CVD
PPM030664 PGS005236
(CAD_AnnoPred)
 PSS012227|
European Ancestry|
18,732 individuals
 PGP000794 |
Ye Y et al. Front Bioinform (2024)
|Ext.		 Reported Trait: Early CAD — AUROC: 0.604 [0.551, 0.657] AUROC of meta-PRS (PRS_CAD + PRS_IS + PRS_HF) for early CAD: 0.614 [0.561, 0.665] PRS_CAD (PGS Catalog ID: PGS005236) + PRS_IS (this paper) + PRS_HF (this paper) combined to derive meta-PRS The meta-PRS was constructed by combining PRS_CAD, PRS_IS, and PRS_HF at the PRS level using weights described in the study. Implementation details are available at: https://github.com/JqiHu/meta-PRS-CVD

Evaluated Samples

PGS Sample Set ID
(PSS) 		Phenotype Definitions and Methods Participant Follow-up Time Sample Numbers Age of Study Participants Sample Ancestry Additional Ancestry Description Cohort(s) Additional Sample/Cohort Information
PSS012227 — —
[
  • 263 cases
  • , 18,469 controls
]
 — European
(British)		 — UKB —
PSS012228 — —
[
  • 1,989 cases
  • , 19,103 controls
]
 — European
(British)		 — UKB —
PSS012229 — —
[
  • 909 cases
  • , 20,183 controls
]
 — European
(British)		 — UKB —