Trait: skin aging measurement

Experimental Factor Ontology (EFO) Information
Identifier EFO_0008006
Description quantification of some aspect of skin aging such as wrinkling or photoaging. Skin aging can be assessed using teh 6-point Beagley and Gibson (BG6) microtopography scoring system of skin patterning regularity and complexity
Trait category
Other measurement

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:
Display options:
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
Polygenic Score ID & Name PGS Publication ID (PGP) Reported Trait Mapped Trait(s) (Ontology) Number of Variants Ancestry distribution Scoring File (FTP Link)
PGS001071
(GBE_BIN_FC30001757)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Facial aging, looking 'about your age' skin aging measurement 6,782
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001071/ScoringFiles/PGS001071.txt.gz
PGS001072
(GBE_BIN_FC20001757)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Facial aging, looking 'older than you are' skin aging measurement 39
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001072/ScoringFiles/PGS001072.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
PPM008122 PGS001072
(GBE_BIN_FC20001757)
PSS003829|
European Ancestry|
62,155 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Facial ageing (Older than you are) AUROC: 0.73561 [0.72134, 0.74988] : 0.08185
Incremental AUROC (full-covars): 0.00813
PGS R2 (no covariates): 0.01106
PGS AUROC (no covariates): 0.58021 [0.56385, 0.59658]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM008121 PGS001072
(GBE_BIN_FC20001757)
PSS003828|
South Asian Ancestry|
7,175 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Facial ageing (Older than you are) AUROC: 0.67677 [0.6489, 0.70465] : 0.05882
Incremental AUROC (full-covars): 0.00163
PGS R2 (no covariates): 0.00212
PGS AUROC (no covariates): 0.53009 [0.50029, 0.55988]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM008120 PGS001072
(GBE_BIN_FC20001757)
PSS003827|
European Ancestry|
23,150 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Facial ageing (Older than you are) AUROC: 0.75503 [0.73445, 0.77561] : 0.1045
Incremental AUROC (full-covars): 0.00672
PGS R2 (no covariates): 0.02014
PGS AUROC (no covariates): 0.6104 [0.5865, 0.6343]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM008119 PGS001072
(GBE_BIN_FC20001757)
PSS003826|
East Asian Ancestry|
1,592 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Facial ageing (Older than you are) AUROC: 0.80121 [0.73505, 0.86737] : 0.17243
Incremental AUROC (full-covars): 0.00575
PGS R2 (no covariates): 0.00236
PGS AUROC (no covariates): 0.52793 [0.43865, 0.61721]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM008118 PGS001072
(GBE_BIN_FC20001757)
PSS003825|
African Ancestry|
6,181 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Facial ageing (Older than you are) AUROC: 0.7208 [0.67263, 0.76898] : 0.07731
Incremental AUROC (full-covars): -0.00071
PGS R2 (no covariates): 0.0002
PGS AUROC (no covariates): 0.48696 [0.43244, 0.54149]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM008117 PGS001071
(GBE_BIN_FC30001757)
PSS003888|
European Ancestry|
62,155 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Facial ageing (About your age) AUROC: 0.58711 [0.58201, 0.59221] : 0.02661
Incremental AUROC (full-covars): 0.0251
PGS R2 (no covariates): 0.01403
PGS AUROC (no covariates): 0.56225 [0.55712, 0.56738]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM008116 PGS001071
(GBE_BIN_FC30001757)
PSS003887|
South Asian Ancestry|
7,175 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Facial ageing (About your age) AUROC: 0.60693 [0.59118, 0.62268] : 0.03623
Incremental AUROC (full-covars): 0.00128
PGS R2 (no covariates): 0.0047
PGS AUROC (no covariates): 0.5379 [0.52202, 0.55377]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM008115 PGS001071
(GBE_BIN_FC30001757)
PSS003886|
European Ancestry|
23,150 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Facial ageing (About your age) AUROC: 0.60868 [0.60014, 0.61722] : 0.03975
Incremental AUROC (full-covars): 0.01725
PGS R2 (no covariates): 0.02392
PGS AUROC (no covariates): 0.58543 [0.57677, 0.5941]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM008114 PGS001071
(GBE_BIN_FC30001757)
PSS003885|
East Asian Ancestry|
1,592 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Facial ageing (About your age) AUROC: 0.63785 [0.59837, 0.67732] : 0.06465
Incremental AUROC (full-covars): 0.00386
PGS R2 (no covariates): 0.00756
PGS AUROC (no covariates): 0.54955 [0.50923, 0.58986]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM008113 PGS001071
(GBE_BIN_FC30001757)
PSS003884|
African Ancestry|
6,181 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Facial ageing (About your age) AUROC: 0.60393 [0.57749, 0.63037] : 0.02228
Incremental AUROC (full-covars): -0.00776
PGS R2 (no covariates): 5e-05
PGS AUROC (no covariates): 0.5003 [0.47293, 0.52766]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method

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
PSS003884
[
  • 475 cases
  • , 5,706 controls
]
African unspecified UKB
PSS003885
[
  • 238 cases
  • , 1,354 controls
]
East Asian UKB
PSS003886
[
  • 5,384 cases
  • , 17,766 controls
]
European non-white British ancestry UKB
PSS003887
[
  • 1,618 cases
  • , 5,557 controls
]
South Asian UKB
PSS003888
[
  • 16,027 cases
  • , 46,128 controls
]
European white British ancestry UKB Testing cohort (heldout set)
PSS003825
[
  • 118 cases
  • , 6,063 controls
]
African unspecified UKB
PSS003826
[
  • 40 cases
  • , 1,552 controls
]
East Asian UKB
PSS003827
[
  • 580 cases
  • , 22,570 controls
]
European non-white British ancestry UKB
PSS003828
[
  • 379 cases
  • , 6,796 controls
]
South Asian UKB
PSS003829
[
  • 1,255 cases
  • , 60,900 controls
]
European white British ancestry UKB Testing cohort (heldout set)