PGS Preprint: PGP000137

Publication Information (EuropePMC)
Title Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases
doi 10.1101/2019.12.14.876474
Publication Date Dec. 19, 2019
Journal bioRxiv Preprint
Author(s) Ritchie SC, Lambert SA, Arnold M, Teo SM, Lim S, Scepanovic P, Marten J, Zahid S, Chaffin M, Liu Y, Abraham G, Ouwehand WH, Roberts DJ, Watkins NA, Drew BG, Calkin A, Di Angelantonio E, Soranzo N, Burgess S, Chapman M, Kathiresan S, Khera AV, Danesh J, Butterworth AS, Inouye M.
Released in PGS Catalog: Feb. 23, 2021

Associated Polygenic Score(s)

PGS Developed By This Publication

Polygenic Score (PGS) ID PGS Name PGS Publication (PGP) ID Reported Trait Mapped Trait(s) (Ontology) Number of Variants PGS Scoring File (FTP Link)
PGS000727 AF_PGS PGP000137
Ritchie SC et al. bioRxiv (2019)
Pre
Atrial fibrillation atrial fibrillation 2,210,336 http://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000727/ScoringFiles/PGS000727.txt.gz
PGS000728 CKD_PGS PGP000137
Ritchie SC et al. bioRxiv (2019)
Pre
Chronic kidney disease chronic kidney disease 1,958,860 http://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000728/ScoringFiles/PGS000728.txt.gz
PGS000729 T2D_PGS PGP000137
Ritchie SC et al. bioRxiv (2019)
Pre
Type 2 diabetes type II diabetes mellitus 2,017,388 http://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000729/ScoringFiles/PGS000729.txt.gz

External PGS Evaluated By This Publication

Polygenic Score (PGS) ID PGS Name PGS Publication (PGP) ID Reported Trait Mapped Trait(s) (Ontology) Number of Variants PGS Scoring File (FTP Link)
PGS000018 metaGRS_CAD PGP000007
Inouye M et al. J Am Coll Cardiol (2018)
Coronary artery disease coronary artery disease 1,745,179 http://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000018/ScoringFiles/PGS000018.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 ID)
Evaluated Score PGS Sample Set ID
(PSS ID)
Performance Source Trait PGS Effect Sizes
(per SD change)
PGS Classification Metrics Other Metrics Covariates Included in the Model PGS Performance: Other Relevant Information
PPM001666 PGS000018
(metaGRS_CAD)
PSS000868 PGP000137
Ritchie SC et al. bioRxiv (2019)
Ext.Pre
Reported Trait: Incident myocardial infarction HR: 2.89 [1.66, 5.04] age, sex, 10 genetic PCs
PPM001667 PGS000729
(T2D_PGS)
PSS000869 PGP000137
Ritchie SC et al. bioRxiv (2019)
Pre
Reported Trait: Incident type 2 diabetes HR: 2.0 [1.36, 2.94] age, sex, 10 genetic PCs
PPM001668 PGS000727
(AF_PGS)
PSS000867 PGP000137
Ritchie SC et al. bioRxiv (2019)
Pre
Reported Trait: Incident atrial fibrillation HR: 1.72 [1.2, 2.47] age, sex, 10 genetic PCs
PPM001669 PGS000728
(CKD_PGS)
PSS000870 PGP000137
Ritchie SC et al. bioRxiv (2019)
Pre
Reported Trait: Estimated Glomerular Filtration Rate (eGFR) β: -0.9 [-1.45, -0.36] age, sex, 10 genetic PCs

Evaluated Samples

PGS Sample Set ID
(PSS ID)
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
PSS000870 Serum creatinine was quantified by Metabolon HD4 metabolomics in mg/dL units, and adjusted for sample measurement batch, sample measurement plate, and days between blood draw and sample processing. Subsequently, eGFR was quantified from serum creatinine using the CKD-EPI equation 3,037 individuals,
51.0 % Male samples
Median = 44.0 years
Iqr = [30.5, 54.7] years
European INTERVAL
PSS000867 CALIBER rule-based phenotyping algorithms (https://www.caliberresearch.org/portal). ICD-10: I48 Median = 6.9 years
[
  • 33 cases
  • , 3,054 controls
]
,
51.0 % Male samples
Median = 44.0 years
Iqr = [30.5, 54.7] years
European INTERVAL
PSS000868 CALIBER rule-based phenotyping algorithms (https://www.caliberresearch.org/portal). ICD-10: I21-I23, I24.1, I25.2 Median = 6.9 years
[
  • 15 cases
  • , 3,072 controls
]
,
51.0 % Male samples
Median = 44.0 years
Iqr = [30.5, 54.7] years
European INTERVAL
PSS000869 CALIBER rule-based phenotyping algorithms (https://github.com/spiros/chronological-map-phenotypes#diabetes) Median = 6.9 years
[
  • 27 cases
  • , 3,060 controls
]
,
51.0 % Male samples
Median = 44.0 years
Iqr = [30.5, 54.7] years
European INTERVAL