Polygenic Score (PGS) ID: PGS000155

Predicted Trait
Reported Trait Glioma
Mapped Trait(s) glioma (EFO_0005543)
Released in PGS Catalog: April 29, 2020
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Terms and Licenses
PGS obtained from the Catalog should be cited appropriately, and used in accordance with any licensing restrictions set by the authors. See EBI Terms of Use (https://www.ebi.ac.uk/about/terms-of-use/) for additional details.

Score Details

Score Construction
PGS Name cGRS_Glioma
Development Method
Name Pruning and Thresholding (P+T)
Parameters GWAS significant and r2 < 0.2. PGS levels were computed as product(dosage*weight/expected risk ef... fect), where the expected risk effect for each variant was calculated based on the risk allele frequence (f) and risk allele weight (OR) as f^2*OR^2 + 2f(1-f)OR + (1-f)^2.Show more
Variants
Original Genome Build NR
Number of Variants 19
Effect Weight Type Odds Ratio over expected risk
PGS Source
PGS Catalog Publication (PGP) ID PGP000075
Citation (link to publication) Shi Z et al. Cancer Med (2019)
Ancestry Distribution
Source of Variant
Associations (GWAS)
European: 100%
103,614 individuals (100%)
PGS Evaluation
European: 100%
1 Sample Sets

Development Samples

Source of Variant Associations (GWAS)
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Study Identifiers
Sample Numbers
Sample Ancestry
Cohort(s)
GWAS Catalog: GCST000439
Europe PMC: 19578367
5,548 individualsEuropeanNR
GWAS Catalog: GCST003220
Europe PMC: 2642405
9,218 individualsEuropeanNR
GWAS Catalog: GCST003227
Europe PMC: 2642405
9,799 individualsEuropeanNR
GWAS Catalog: GCST004347
Europe PMC: 28346443
30,659 individualsEuropeanNR
GWAS Catalog: GCST004348
Europe PMC: 28346443
24,009 individualsEuropeanNR
GWAS Catalog: GCST004349
Europe PMC: 28346443
24,381 individualsEuropeanNR
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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.

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PGS Performance
Metric ID (PPM)
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
PPM000475PSS000275|
European Ancestry|
14,419 individuals
PGP000075 |
Shi Z et al. Cancer Med (2019)
Reported Trait: Glioma——Mean realative risk: 1.22 [1.18, 1.26]
Wilcoxon test (case vs. control) p-value: 1.39e-37		——
PPM000486PSS000275|
European Ancestry|
14,419 individuals
PGP000075 |
Shi Z et al. Cancer Med (2019)
Reported Trait: Glioma——Odds Ratio (OR; high vs. average risk groups): 1.8 [1.55, 2.1]——
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Evaluated Samples

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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
PSS000275Primary tumor samples from TCGA—
[
  • 992 cases
  • , 0 controls
]
Mean = 52.0 years
Sd = 16.0 years		European—TCGA—
PSS000275——
[
  • 0 cases
  • , 13,427 controls
]
—European—eMERGE—
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