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)

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:
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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 PGS Scoring File (FTP Link)
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
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
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 ID & Name PGS Publication ID (PGP) Reported Trait Mapped Trait(s) (Ontology) Number of Variants Ancestry distribution 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)
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
PPM001666 PGS000018
(metaGRS_CAD)
PSS000868|
European Ancestry|
3,087 individuals
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|
European Ancestry|
3,087 individuals
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|
European Ancestry|
3,087 individuals
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|
European Ancestry|
3,037 individuals
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)
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