| Experimental Factor Ontology (EFO) Information | |
| Identifier | EFO_0005208 |
| Description |
|
Trait category |
Other measurement
|
| Synonyms | GFR |
| Mapped term(s) |
3 mapped terms
|
| Child trait(s) | GFR change measurement |
| Polygenic Score ID & Name | PGS Publication ID (PGP) | Reported Trait | Mapped Trait(s) (Ontology) | Number of Variants | Ancestry distribution | PGS Scoring File (FTP Link) |
|---|---|---|---|---|---|---|
| PGS000303 (GRS253_eGFR) |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
Estimated glomerular filtration rate | glomerular filtration rate | 253 | - |
http://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000303/ScoringFiles/PGS000303.txt.gz |
| PGS000664 (GRS7_GFR) |
PGP000124 | Gorski M et al. Kidney Int (2020) |
Rapid decline of glomerular filtration rate (GFR) | GFR change measurement | 7 | - |
http://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000664/ScoringFiles/PGS000664.txt.gz |
| PGS000682 (snpnet.eGFR) |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
eGFR [ml/min/1.73m2] | glomerular filtration rate | 17,467 | - |
http://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000682/ScoringFiles/PGS000682.txt.gz |
|
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 |
|---|---|---|---|---|---|---|---|---|---|
| PPM000773 | PGS000303 (GRS253_eGFR) |
PSS000376| European Ancestry| 1,354 individuals |
PGP000092 | Xie T et al. Circ Genom Precis Med (2020) |
Reported Trait: Estimated glomerular filtration rate (mL/min per 1.73 m2) | — | — | R²: 0.0504 | Sex, age | — |
| PPM001371 | PGS000664 (GRS7_GFR) |
PSS000600| European Ancestry| 11,440 individuals |
PGP000124 | Gorski M et al. Kidney Int (2020) |
Reported Trait: Rapid decline of glomerular filtration rate estimated from creatinine (CKDi25) | — | — | Odds Ratio (OR, high vs. low risk): 1.29 [1.06, 1.57] | Age, sex and baseline eGFRcrea | — |
| PPM001372 | PGS000664 (GRS7_GFR) |
PSS000599| European Ancestry| 3,447 individuals |
PGP000124 | Gorski M et al. Kidney Int (2020) |
Reported Trait: Acute kidney injury | — | — | Odds Ratio (OR, high vs. low risk): 1.2 [1.08, 1.33] | Matching variables (age-group and sex), quantitative age | — |
| PPM001410 | PGS000682 (snpnet.eGFR) |
PSS000805| African Ancestry| 6,016 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: eGFR [ml/min/1.73m2] | — | — | R²: 0.17769 Spearman's ρ: 0.138 |
Age, sex, PCs(1-40) | — |
| PPM001445 | PGS000682 (snpnet.eGFR) |
PSS000806| East Asian Ancestry| 1,081 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: eGFR [ml/min/1.73m2] | — | — | R²: 0.21054 Spearman's ρ: 0.199 |
Age, sex, PCs(1-40) | — |
| PPM001480 | PGS000682 (snpnet.eGFR) |
PSS000807| European Ancestry| 23,576 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: eGFR [ml/min/1.73m2] | — | — | R²: 0.25538 Spearman's ρ: 0.299 |
Age, sex, PCs(1-40) | — |
| PPM001515 | PGS000682 (snpnet.eGFR) |
PSS000808| South Asian Ancestry| 7,339 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: eGFR [ml/min/1.73m2] | — | — | R²: 0.23182 Spearman's ρ: 0.252 |
Age, sex, PCs(1-40) | — |
| PPM001550 | PGS000682 (snpnet.eGFR) |
PSS000809| European Ancestry| 63,758 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: eGFR [ml/min/1.73m2] | — | — | R²: 0.24611 Spearman's ρ: 0.313 |
Age, sex, PCs(1-40) | — |
| PPM001592 | PGS000682 (snpnet.eGFR) |
PSS000810| European Ancestry| 2,129 individuals |
PGP000128 | Sinnott-Armstrong N et al. Nat Genet (2021) |
Reported Trait: eGFR [ml/min/1.73m2] | — | — | Spearman's ρ: 0.219 | Age, sex | — |
|
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 |
|---|---|---|---|---|---|---|---|---|
| PSS000807 | — | — | 23,576 individuals | — | European | Non-British White | UKB | — |
| PSS000808 | — | — | 7,339 individuals | — | South Asian | — | UKB | — |
| PSS000809 | — | — | 63,758 individuals | — | European (British) |
— | UKB | — |
| PSS000810 | — | — | 2,129 individuals | — | European | Participants self-identifying as white | MESA | — |
| PSS000376 | We measured weight and height using regularly calibrated equipment (scales and stadiometer models 770 and 214, respectively; Seca, Hamburg, Germany). Body mass index (BMI; in kg/m2) was also calculated. We measured waist circumference at the midpoint between the lower costal margin and the iliac crest. The hip circumference was measured over both trochanter majores (tangible bone on the outside of the hip joint). Waist to hip ratio was also calculated. We performed all measurements in duplicate, and, if the difference between these measurements exceeded a predefined value, a third measurement was performed. All available measurements were used to calculate means. Heart rate, systolic (SBP) and diastolic (DBP) blood pressure were measured in duplicate with a Dinamap Critikon 1846SX (Critikon Inc, Tampa, FL), from which we calculated means. At the third visit, fasting blood sample of participants were drawn for the measurement of glucose (Roche Diagnostics, Basel, Switzerland), insulin (Diagnostic Systems Laboratories Inc, Webster, TX), HbA1c (high performance liquid chromatography, Variant, Bio-Rad), triglycerides, total cholesterol, HDL cholesterol (Roche Diagnostics) and LDL cholesterol (calculated according to Friedewald’s equation5), as well as alanine transaminase (Photometric determination according to the reference method of the International Federation of Clinical Chemistry (IFCC)6) and lipoprotein(a) (Nephelometric method, BN2, DadeBehring). Serum creatinine was measured by photometric determination with the Jaffé method without deproteinisation (Ecoline® MEGA, DiaSys Diagnostic Systems GmbH. Merck). eGFR for adolescents who were younger than 18 years old was calculated using the Schwartz formula.7 High‐sensitivity C‐reactive protein (hsCRP) was determined using an immunonephelometric method, BN2 (CardioPhase hsCRP, Siemens) with a lower detection limit of 0.175 mg/L. Total IgE measurements were performed using the Phadia Immunocap 100 system with fluoroenzyme immunoassay (FEIA). | — | 1,354 individuals, 47.56 % Male samples |
Mean = 16.22 years Sd = 0.66 years |
European | — | TRAILS | — |
| PSS000599 | Cases: ICD 10 code N17. Controls: no ICD10 code N17, frequency-matched by age-group and sex | — | [
|
— | European | — | UKB | Possible overlap between GWAS cohorts and this dataset. |
| PSS000600 | CKDi25 cases defined as >25% eGFRcrea decline during follow-up together with a movement from eGFRcrea≥60 mL/min/1.73m^2 at baseline to eGFR<60 mL/min/1.73m^2 at follow up compared to CKDi25 controls defined as eGFRcrea≥60 mL/min/1.73m^2. High risk groups had 8-14 adverse alleles. Low risk groups had 0-5 adverse alleles. | — | [
|
— | European | — | DIACORE, KORA, UKB | 87.61% overlap between the CKDi25 GWAS cohort and this dataset. |
| PSS000805 | — | — | 6,016 individuals | — | African unspecified | — | UKB | — |
| PSS000806 | — | — | 1,081 individuals | — | East Asian | — | UKB | — |