Polygenic Score (PGS) ID: PGS000156

Predicted Trait
Reported Trait Lung cancer
Mapped Trait(s) lung carcinoma (EFO_0001071)
Released in PGS Catalog: April 29, 2020
Download Score FTP directory
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_Lung
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 effect), 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.
Variants
Original Genome Build NR
Number of Variants 6
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%
258,478 individuals (100%)
PGS Evaluation
European: 100%
2 Sample Sets

Development Samples

Source of Variant Associations (GWAS)
Study Identifiers Sample Numbers Sample Ancestry Cohort(s)
GWAS Catalog: GCST002466
Europe PMC: 24880342
27,209 individuals European NR
GWAS Catalog: GCST003587
Europe PMC: 27197191
123,671 individuals European NR
GWAS Catalog: GCST000506
Europe PMC: 19836008
11,587 individuals European NR
GWAS Catalog: GCST004748
Europe PMC: 2860473
85,716 individuals European NR
GWAS Catalog: GCST000257
Europe PMC: 18978787
10,295 individuals European B58C, GELCAPS, IARC, MDACCS

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)
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
PPM000487 PSS000276|
European Ancestry|
14,335 individuals
PGP000075 |
Shi Z et al. Cancer Med (2019)
Reported Trait: Lung cancer Odds Ratio (OR; high vs. average risk groups): 0.88 [0.57, 1.36]
PPM000476 PSS000276|
European Ancestry|
14,335 individuals
PGP000075 |
Shi Z et al. Cancer Med (2019)
Reported Trait: Lung cancer Mean realative risk: 1.01 [0.99, 1.02]
Wilcoxon test (case vs. control) p-value: 0.0116
PPM020284 PSS011324|
European Ancestry|
1,202 individuals
PGP000539 |
Lebrett MB et al. Genet Med (2023)
|Ext.
Reported Trait: Lung cancer AUROC: 0.732 [0.704, 0.76] Age, sex, current smoking status, BMI, forced expiratory volume in 1 second/forced vital capacity ratio

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
PSS000276
[
  • 0 cases
  • , 13,427 controls
]
European eMERGE
PSS011324
[
  • 652 cases
  • , 550 controls
]
,
47.0 % Male samples
Median = 65.0 years European MCRC
PSS000276 Primary tumor samples from TCGA
[
  • 908 cases
  • , 0 controls
]
Mean = 67.0 years
Sd = 9.0 years
European TCGA