Trait: body weights and measures

Experimental Factor Ontology (EFO) Information
Identifier EFO_0004324
Description Measurements of the height, weight, length, area, etc., of the human and animal body or its parts.
Trait category
Body measurement
Synonym body measures
Mapped term MeSH:D001837
Child trait(s) 10 child traits

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
Note: This table shows all PGS for "body weights and measures" and any child terms of this trait in the EFO hierarchy by default.
Polygenic Score ID & Name PGS Publication ID (PGP) Reported Trait Mapped Trait(s) (Ontology) Number of Variants Ancestry distribution Scoring File (FTP Link)
PGS000027
(GPS_BMI)
PGP000017 |
Khera AV et al. Cell (2019)
Body Mass Index body mass index 2,100,302
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000027/ScoringFiles/PGS000027.txt.gz - Check Terms/Licenses
PGS000034
(GRS_BMI)
PGP000021 |
Song M et al. Diabetes (2017)
Adult Body Mass Index body mass index 97
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000034/ScoringFiles/PGS000034.txt.gz
PGS000297
(GRS3290_Height)
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Height body height 3,290
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000297/ScoringFiles/PGS000297.txt.gz
PGS000298
(GRS941_BMI)
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Body mass index body mass index 941
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000298/ScoringFiles/PGS000298.txt.gz
PGS000299
(GRS462_WHRadjBMI)
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Waist-to-hip ratio (body mass index adjusted) BMI-adjusted waist-hip ratio 462
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000299/ScoringFiles/PGS000299.txt.gz
PGS000320
(PRS_BMI)
PGP000096 |
Chami N et al. PLoS Med (2020)
Body mass index body mass index 263,640
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000320/ScoringFiles/PGS000320.txt.gz
PGS000716
(PGS295_elbs)
PGP000132 |
Richardson TG et al. BMJ (2020)
Early life body size body mass index,
comparative body size at age 10, self-reported
295
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000716/ScoringFiles/PGS000716.txt.gz
PGS000717
(PGS557_albs)
PGP000132 |
Richardson TG et al. BMJ (2020)
Adult life body size body mass index 557
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000717/ScoringFiles/PGS000717.txt.gz
PGS000758
(LASSO_Height)
PGP000163 |
Lu T et al. J Clin Endocrinol Metab (2021)
Adult standing height body height 33,938
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000758/ScoringFiles/PGS000758.txt.gz - Check Terms/Licenses
PGS000770
(PRS231)
PGP000177 |
de Toro-Martín J et al. Front Genet (2019)
Body mass index body mass index 231
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000770/ScoringFiles/PGS000770.txt.gz
PGS000827
(WaistCircumference_PGS_M)
PGP000210 |
Zubair N et al. Sci Rep (2019)
Waist circumference (male) waist circumference,
male
113
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000827/ScoringFiles/PGS000827.txt.gz - Check Terms/Licenses
PGS000828
(WaistCircumference_PGS_F)
PGP000210 |
Zubair N et al. Sci Rep (2019)
Waist circumference (female) waist circumference,
female
149
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000828/ScoringFiles/PGS000828.txt.gz - Check Terms/Licenses
PGS000829
(BMI_PGS_M)
PGP000210 |
Zubair N et al. Sci Rep (2019)
Body mass index (male) body mass index,
male
290
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000829/ScoringFiles/PGS000829.txt.gz - Check Terms/Licenses
PGS000830
(BMI_PGS_F)
PGP000210 |
Zubair N et al. Sci Rep (2019)
Body mass index (female) body mass index,
female
372
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000830/ScoringFiles/PGS000830.txt.gz - Check Terms/Licenses
PGS000841
(BMI)
PGP000211 |
Aly DM et al. Nat Genet (2021)
Body mass index body mass index 122
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000841/ScoringFiles/PGS000841.txt.gz
PGS000842
(WHR)
PGP000211 |
Aly DM et al. Nat Genet (2021)
Waist-hip ratio waist-hip ratio 39
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000842/ScoringFiles/PGS000842.txt.gz
PGS000843
(WHRadjBMI)
PGP000211 |
Aly DM et al. Nat Genet (2021)
WHR adjusted for BMI BMI-adjusted waist-hip ratio 63
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000843/ScoringFiles/PGS000843.txt.gz
PGS000910
(PRS_BMI)
PGP000238 |
Campos AI et al. medRxiv (2021)
|Pre
Body mass index body mass index 735,440
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000910/ScoringFiles/PGS000910.txt.gz
PGS000921
(PRS_BMI)
PGP000243 |
Borisevich D et al. PLoS One (2021)
Body mass index body mass index 1,947,711
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000921/ScoringFiles/PGS000921.txt.gz
PGS001006
(GBE_BIN_FC1002306)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Weight change compared with 1 year ago body weight,
self-reported trait
2,638
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001006/ScoringFiles/PGS001006.txt.gz
PGS001101
(GBE_INI23099)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Body fat percentage body fat percentage 27,396
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001101/ScoringFiles/PGS001101.txt.gz
PGS001102
(GBE_INI23123)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Body fat percentage (left arm) body fat percentage 25,266
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001102/ScoringFiles/PGS001102.txt.gz
PGS001103
(GBE_INI23115)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Body fat percentage (left leg) body fat percentage 28,134
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001103/ScoringFiles/PGS001103.txt.gz
PGS001104
(GBE_INI23111)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Body fat percentage (right leg) body fat percentage 28,163
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001104/ScoringFiles/PGS001104.txt.gz
PGS001105
(GBE_INI23127)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Body fat percentage (trunk fat) body fat percentage 25,651
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001105/ScoringFiles/PGS001105.txt.gz
PGS001106
(GBE_INI23119)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Percentage of right arm fat body fat percentage 23,943
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001106/ScoringFiles/PGS001106.txt.gz
PGS001154
(GBE_INI23110)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Impedance (left arm) body composition measurement 26,740
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001154/ScoringFiles/PGS001154.txt.gz
PGS001155
(GBE_INI23108)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Impedance (left leg) body composition measurement 28,999
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001155/ScoringFiles/PGS001155.txt.gz
PGS001156
(GBE_INI23109)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Impedance (right arm) body composition measurement 30,140
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001156/ScoringFiles/PGS001156.txt.gz
PGS001157
(GBE_INI23107)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Impedance (right leg) body composition measurement 30,300
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001157/ScoringFiles/PGS001157.txt.gz
PGS001158
(GBE_INI23118)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Left leg mass (predicted) body composition measurement 30,421
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001158/ScoringFiles/PGS001158.txt.gz
PGS001159
(GBE_INI23114)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Right leg mass (predicted) body composition measurement 32,103
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001159/ScoringFiles/PGS001159.txt.gz
PGS001160
(GBE_INI23130)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Trunk mass (predicted) body composition measurement 32,685
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001160/ScoringFiles/PGS001160.txt.gz
PGS001161
(GBE_INI23106)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Whole body impedance body composition measurement 32,218
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001161/ScoringFiles/PGS001161.txt.gz
PGS001162
(GBE_INI49)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Hip circumference hip circumference 24,605
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001162/ScoringFiles/PGS001162.txt.gz
PGS001226
(GBE_INI20022)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Birth weight birth weight 6,278
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001226/ScoringFiles/PGS001226.txt.gz
PGS001227
(GBE_INI48)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Waist circumference waist circumference 25,538
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001227/ScoringFiles/PGS001227.txt.gz
PGS001228
(GBE_INI21001)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Body mass index body mass index 27,126
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001228/ScoringFiles/PGS001228.txt.gz
PGS001229
(GBE_INI50)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Standing height body height 51,209
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001229/ScoringFiles/PGS001229.txt.gz
PGS001230
(GBE_INI21002)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Body weight body weight 31,222
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001230/ScoringFiles/PGS001230.txt.gz
PGS001234
(GBE_INI23126)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Left arm mass (predicted) body weights and measures 30,666
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001234/ScoringFiles/PGS001234.txt.gz
PGS001235
(GBE_INI23122)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Right arm mass (predicted) body weights and measures 28,988
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001235/ScoringFiles/PGS001235.txt.gz
PGS001405
(GBE_INI12144)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Height body height 3,166
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001405/ScoringFiles/PGS001405.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
PPM001708 PGS000716
(PGS295_elbs)
PSS000883|
European Ancestry|
62,541 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Obesity in individuals aged 30-69.9 AUROC: 0.569 [0.564, 0.574] Due to a lack of information in the HUNT dataset, only 277 of the 295 common variants used to build the childhood score by Richardson et al were included.
PPM001709 PGS000716
(PGS295_elbs)
PSS000876|
European Ancestry|
3,124 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 12-15.9 AUROC: 0.656 [0.626, 0.686] Due to a lack of information in the HUNT dataset, only 277 of the 295 common variants used to build the childhood score by Richardson et al were included.
PPM001710 PGS000716
(PGS295_elbs)
PSS000878|
European Ancestry|
2,896 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 16-17.9 AUROC: 0.623 [0.589, 0.657] Due to a lack of information in the HUNT dataset, only 277 of the 295 common variants used to build the childhood score by Richardson et al were included.
PPM001711 PGS000716
(PGS295_elbs)
PSS000880|
European Ancestry|
12,179 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 18-23.9 AUROC: 0.601 [0.589, 0.612] Due to a lack of information in the HUNT dataset, only 277 of the 295 common variants used to build the childhood score by Richardson et al were included.
PPM001712 PGS000716
(PGS295_elbs)
PSS000882|
European Ancestry|
17,139 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 14-29.9 AUROC: 0.591 [0.582, 0.6] Due to a lack of information in the HUNT dataset, only 277 of the 295 common variants used to build the childhood score by Richardson et al were included.
PPM001713 PGS000716
(PGS295_elbs)
PSS000884|
European Ancestry|
62,541 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 30-69.9 AUROC: 0.568 [0.563, 0.573] Due to a lack of information in the HUNT dataset, only 277 of the 295 common variants used to build the childhood score by Richardson et al were included.
PPM001714 PGS000717
(PGS557_albs)
PSS000875|
European Ancestry|
3,124 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Obesity in individuals aged 12-15.9 AUROC: 0.664 [0.574, 0.753] Due to a lack of information in the HUNT dataset, only 509 of the 557 common variants used to build the adult score by Richardson et al were included.
PPM001715 PGS000717
(PGS557_albs)
PSS000877|
European Ancestry|
2,896 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Obesity in individuals aged 16-17.9 AUROC: 0.691 [0.589, 0.793] Due to a lack of information in the HUNT dataset, only 509 of the 557 common variants used to build the adult score by Richardson et al were included.
PPM001716 PGS000717
(PGS557_albs)
PSS000879|
European Ancestry|
12,179 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Obesity in individuals aged 18-23.9 AUROC: 0.624 [0.601, 0.646] Due to a lack of information in the HUNT dataset, only 509 of the 557 common variants used to build the adult score by Richardson et al were included.
PPM000054 PGS000027
(GPS_BMI)
PSS000037|
European Ancestry|
288,016 individuals
PGP000017 |
Khera AV et al. Cell (2019)
Reported Trait: Body mass index β: 1.41 : 0.08526 First 10 genetic PCs Beta is in units of kg/m^2
PPM001717 PGS000717
(PGS557_albs)
PSS000881|
European Ancestry|
17,139 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Obesity in individuals aged 24-29.9 AUROC: 0.623 [0.607, 0.639] Due to a lack of information in the HUNT dataset, only 509 of the 557 common variants used to build the adult score by Richardson et al were included.
PPM001719 PGS000717
(PGS557_albs)
PSS000876|
European Ancestry|
3,124 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 12-15.9 AUROC: 0.587 [0.556, 0.618] Due to a lack of information in the HUNT dataset, only 509 of the 557 common variants used to build the adult score by Richardson et al were included.
PPM001720 PGS000717
(PGS557_albs)
PSS000878|
European Ancestry|
2,896 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 16-17.9 AUROC: 0.615 [0.58, 0.649] Due to a lack of information in the HUNT dataset, only 509 of the 557 common variants used to build the adult score by Richardson et al were included.
PPM000791 PGS000298
(GRS941_BMI)
PSS000369|
European Ancestry|
334 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Body mass index (kg/m2) : 0.0671 Sex, age
PPM000792 PGS000298
(GRS941_BMI)
PSS000370|
European Ancestry|
329 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Body mass index (kg/m2) : 0.057 Sex, age
PPM000793 PGS000298
(GRS941_BMI)
PSS000371|
European Ancestry|
288 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Body mass index (kg/m2) : 0.0886 Sex, age
PPM000794 PGS000298
(GRS941_BMI)
PSS000372|
European Ancestry|
265 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Body mass index (kg/m2) : 0.092 Sex, age
PPM000795 PGS000298
(GRS941_BMI)
PSS000373|
European Ancestry|
245 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Body mass index (kg/m2) : 0.1194 Sex, age
PPM001722 PGS000717
(PGS557_albs)
PSS000882|
European Ancestry|
17,139 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 14-29.9 AUROC: 0.598 [0.589, 0.607] Due to a lack of information in the HUNT dataset, only 509 of the 557 common variants used to build the adult score by Richardson et al were included.
PPM001723 PGS000717
(PGS557_albs)
PSS000884|
European Ancestry|
62,541 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 30-69.9 AUROC: 0.582 [0.578, 0.587] Due to a lack of information in the HUNT dataset, only 509 of the 557 common variants used to build the adult score by Richardson et al were included.
PPM000072 PGS000034
(GRS_BMI)
PSS000050|
European Ancestry|
5,640 individuals
PGP000021 |
Song M et al. Diabetes (2017)
Reported Trait: mean BMI difference β (per 10-allele increment): 0.17 [0.03, 0.31]
PPM001725 PGS000027
(GPS_BMI)
PSS000885|
European Ancestry|
19,588 individuals
PGP000141 |
Brandkvist M et al. PLoS Med (2020)
|Ext.
Reported Trait: Obesity between male siblings OR: 2.21 [1.92, 2.54] Sex, time of measurement, age, PCs (1-20), genotyping batch 2.07 million of the 2.1 milliion common variants (excluding those with insufficient quality of genotyping or imputation (r^2 < 0.8) in the HUNT cohort) were included in the polygenic score previously developed by Khera et al (2019).
PPM001726 PGS000027
(GPS_BMI)
PSS000886|
European Ancestry|
21,499 individuals
PGP000141 |
Brandkvist M et al. PLoS Med (2020)
|Ext.
Reported Trait: Obesity within female siblings OR: 2.09 [1.74, 2.51] Sex, time of measurement, age, PCs (1-20), genotyping batch 2.07 million of the 2.1 milliion common variants (excluding those with insufficient quality of genotyping or imputation (r^2 < 0.8) in the HUNT cohort) were included in the polygenic score previously developed by Khera et al (2019).
PPM000071 PGS000034
(GRS_BMI)
PSS000048|
European Ancestry|
5,956 individuals
PGP000021 |
Song M et al. Diabetes (2017)
Reported Trait: mean BMI difference β (per 10-allele increment): 0.54 [0.38, 0.71]
PPM001727 PGS000027
(GPS_BMI)
PSS000886|
European Ancestry|
21,499 individuals
PGP000141 |
Brandkvist M et al. PLoS Med (2020)
|Ext.
Reported Trait: Obesity between female siblings OR: 2.13 [1.87, 2.43] Sex, time of measurement, age, PCs (1-20), genotyping batch 2.07 million of the 2.1 milliion common variants (excluding those with insufficient quality of genotyping or imputation (r^2 < 0.8) in the HUNT cohort) were included in the polygenic score previously developed by Khera et al (2019).
PPM001728 PGS000027
(GPS_BMI)
PSS000887|
European Ancestry|
26,323 individuals
PGP000141 |
Brandkvist M et al. PLoS Med (2020)
|Ext.
Reported Trait: Obesity within male siblings OR: 2.15 [1.91, 2.41] Sex, time of measurement, age, PCs (1-20), genotyping batch 2.07 million of the 2.1 milliion common variants (excluding those with insufficient quality of genotyping or imputation (r^2 < 0.8) in the HUNT cohort) were included in the polygenic score previously developed by Khera et al (2019).
PPM000073 PGS000034
(GRS_BMI)
PSS000051|
European Ancestry|
2,942 individuals
PGP000021 |
Song M et al. Diabetes (2017)
Reported Trait: mean BMI difference β (per 10-allele increment): -0.22 [-0.39, -0.05]
PPM001730 PGS000027
(GPS_BMI)
PSS000888|
European Ancestry|
29,527 individuals
PGP000141 |
Brandkvist M et al. PLoS Med (2020)
|Ext.
Reported Trait: Obesity within female siblings OR: 1.87 [1.68, 2.09] Sex, time of measurement, age, PCs (1-20), genotyping batch 2.07 million of the 2.1 milliion common variants (excluding those with insufficient quality of genotyping or imputation (r^2 < 0.8) in the HUNT cohort) were included in the polygenic score previously developed by Khera et al (2019).
PPM001731 PGS000027
(GPS_BMI)
PSS000888|
European Ancestry|
29,527 individuals
PGP000141 |
Brandkvist M et al. PLoS Med (2020)
|Ext.
Reported Trait: Obesity between female siblings OR: 2.05 [1.89, 2.22] Sex, time of measurement, age, PCs (1-20), genotyping batch 2.07 million of the 2.1 milliion common variants (excluding those with insufficient quality of genotyping or imputation (r^2 < 0.8) in the HUNT cohort) were included in the polygenic score previously developed by Khera et al (2019).
PPM001732 PGS000027
(GPS_BMI)
PSS000889|
European Ancestry|
21,187 individuals
PGP000141 |
Brandkvist M et al. PLoS Med (2020)
|Ext.
Reported Trait: Obesity within male siblings OR: 2.0 [1.79, 2.23] Sex, time of measurement, age, PCs (1-20), genotyping batch 2.07 million of the 2.1 milliion common variants (excluding those with insufficient quality of genotyping or imputation (r^2 < 0.8) in the HUNT cohort) were included in the polygenic score previously developed by Khera et al (2019).
PPM001733 PGS000027
(GPS_BMI)
PSS000889|
European Ancestry|
21,187 individuals
PGP000141 |
Brandkvist M et al. PLoS Med (2020)
|Ext.
Reported Trait: Obesity between male siblings OR: 2.12 [1.96, 2.3] Sex, time of measurement, age, PCs (1-20), genotyping batch 2.07 million of the 2.1 milliion common variants (excluding those with insufficient quality of genotyping or imputation (r^2 < 0.8) in the HUNT cohort) were included in the polygenic score previously developed by Khera et al (2019).
PPM001734 PGS000027
(GPS_BMI)
PSS000890|
European Ancestry|
25,096 individuals
PGP000141 |
Brandkvist M et al. PLoS Med (2020)
|Ext.
Reported Trait: Obesity within female siblings OR: 2.0 [1.8, 2.21] Sex, time of measurement, age, PCs (1-20), genotyping batch 2.07 million of the 2.1 milliion common variants (excluding those with insufficient quality of genotyping or imputation (r^2 < 0.8) in the HUNT cohort) were included in the polygenic score previously developed by Khera et al (2019).
PPM001735 PGS000027
(GPS_BMI)
PSS000890|
European Ancestry|
25,096 individuals
PGP000141 |
Brandkvist M et al. PLoS Med (2020)
|Ext.
Reported Trait: Obesity between female siblings OR: 2.03 [1.88, 2.19] Sex, time of measurement, age, PCs (1-20), genotyping batch 2.07 million of the 2.1 milliion common variants (excluding those with insufficient quality of genotyping or imputation (r^2 < 0.8) in the HUNT cohort) were included in the polygenic score previously developed by Khera et al (2019).
PPM001707 PGS000716
(PGS295_elbs)
PSS000879|
European Ancestry|
12,179 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Obesity in individuals aged 18-23.9 AUROC: 0.618 [0.594, 0.641] Due to a lack of information in the HUNT dataset, only 277 of the 295 common variants used to build the childhood score by Richardson et al were included.
PPM001718 PGS000717
(PGS557_albs)
PSS000883|
European Ancestry|
62,541 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Obesity in individuals aged 30-69.9 AUROC: 0.597 [0.591, 0.602] Due to a lack of information in the HUNT dataset, only 509 of the 557 common variants used to build the adult score by Richardson et al were included.
PPM000074 PGS000034
(GRS_BMI)
PSS000049|
European Ancestry|
6,705 individuals
PGP000021 |
Song M et al. Diabetes (2017)
Reported Trait: mean BMI difference β (per 10-allele increment): 0.6 [0.42, 0.77]
PPM000075 PGS000034
(GRS_BMI)
PSS000052|
European Ancestry|
6,436 individuals
PGP000021 |
Song M et al. Diabetes (2017)
Reported Trait: mean BMI difference β (per 10-allele increment): 0.07 [-0.04, 0.19]
PPM000076 PGS000034
(GRS_BMI)
PSS000045|
European Ancestry|
1,699 individuals
PGP000021 |
Song M et al. Diabetes (2017)
Reported Trait: mean BMI difference β (per 10-allele increment): 0.23 [0.02, 0.44]
PPM000077 PGS000034
(GRS_BMI)
PSS000046|
European Ancestry|
1,634 individuals
PGP000021 |
Song M et al. Diabetes (2017)
Reported Trait: mean BMI difference β (per 10-allele increment): -0.1 [-0.29, 0.09]
PPM000078 PGS000034
(GRS_BMI)
PSS000047|
European Ancestry|
2,020 individuals
PGP000021 |
Song M et al. Diabetes (2017)
Reported Trait: mean BMI difference β (per 10-allele increment): -0.01 [-0.16, 0.14]
PPM002233 PGS000828
(WaistCircumference_PGS_F)
PSS001083|
Multi-ancestry (including European)|
2,531 individuals
PGP000210 |
Zubair N et al. Sci Rep (2019)
Reported Trait: Waist circumference (female) : 0.032 Age at baseline, sex, enrollment channel, PCs(1-7), observation season, observation vendor
PPM002234 PGS000827
(WaistCircumference_PGS_M)
PSS001083|
Multi-ancestry (including European)|
2,531 individuals
PGP000210 |
Zubair N et al. Sci Rep (2019)
Reported Trait: Waist circumference (male) : 0.018 Age at baseline, sex, enrollment channel, PCs(1-7), observation season, observation vendor
PPM001687 PGS000716
(PGS295_elbs)
PSS000879|
European Ancestry|
12,179 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Obesity in individuals aged 18-23.9 AUROC: 0.619 [0.596, 0.643] Due to a lack of information in the HUNT dataset, only 289 of the 295 common variants used to build the childhood score by Richardson et al were included. Additionally, 11 of the 289 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM001688 PGS000716
(PGS295_elbs)
PSS000881|
European Ancestry|
17,139 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Obesity in individuals aged 24-29.9 AUROC: 0.602 [0.585, 0.618] Due to a lack of information in the HUNT dataset, only 289 of the 295 common variants used to build the childhood score by Richardson et al were included. Additionally, 11 of the 289 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM001689 PGS000716
(PGS295_elbs)
PSS000883|
European Ancestry|
62,541 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Obesity in individuals aged 30-69.9 AUROC: 0.569 [0.564, 0.574] Due to a lack of information in the HUNT dataset, only 289 of the 295 common variants used to build the childhood score by Richardson et al were included. Additionally, 11 of the 289 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM001690 PGS000716
(PGS295_elbs)
PSS000876|
European Ancestry|
3,124 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 12-15.9 AUROC: 0.658 [0.628, 0.687] Due to a lack of information in the HUNT dataset, only 289 of the 295 common variants used to build the childhood score by Richardson et al were included. Additionally, 11 of the 289 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM001691 PGS000716
(PGS295_elbs)
PSS000878|
European Ancestry|
2,896 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 16-17.9 AUROC: 0.624 [0.589, 0.658] Due to a lack of information in the HUNT dataset, only 289 of the 295 common variants used to build the childhood score by Richardson et al were included. Additionally, 11 of the 289 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM001692 PGS000716
(PGS295_elbs)
PSS000880|
European Ancestry|
12,179 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 18-23.9 AUROC: 0.602 [0.59, 0.613] Due to a lack of information in the HUNT dataset, only 289 of the 295 common variants used to build the childhood score by Richardson et al were included. Additionally, 11 of the 289 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM001693 PGS000716
(PGS295_elbs)
PSS000882|
European Ancestry|
17,139 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 14-29.9 AUROC: 0.591 [0.582, 0.6] Due to a lack of information in the HUNT dataset, only 289 of the 295 common variants used to build the childhood score by Richardson et al were included. Additionally, 11 of the 289 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM001695 PGS000717
(PGS557_albs)
PSS000875|
European Ancestry|
3,124 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Obesity in individuals aged 12-15.9 AUROC: 0.66 [0.57, 0.75] Due to a lack of information in the HUNT dataset, only 546 of the 557 common variants used to build the adult score by Richardson et al were included. Additionally, 28 of the 546 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM001696 PGS000717
(PGS557_albs)
PSS000877|
European Ancestry|
2,896 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Obesity in individuals aged 16-17.9 AUROC: 0.661 [0.551, 0.77] Due to a lack of information in the HUNT dataset, only 546 of the 557 common variants used to build the adult score by Richardson et al were included. Additionally, 28 of the 546 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM001736 PGS000027
(GPS_BMI)
PSS000891|
European Ancestry|
14,487 individuals
PGP000141 |
Brandkvist M et al. PLoS Med (2020)
|Ext.
Reported Trait: Obesity within male siblings OR: 1.9 [1.68, 2.14] Sex, time of measurement, age, PCs (1-20), genotyping batch 2.07 million of the 2.1 milliion common variants (excluding those with insufficient quality of genotyping or imputation (r^2 < 0.8) in the HUNT cohort) were included in the polygenic score previously developed by Khera et al (2019).
PPM000757 PGS000297
(GRS3290_Height)
PSS000375|
European Ancestry|
1,313 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Height (cm) : 0.1172 Sex, age
PPM000758 PGS000297
(GRS3290_Height)
PSS000376|
European Ancestry|
1,354 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Height (cm) : 0.1368 Sex, age
PPM000759 PGS000297
(GRS3290_Height)
PSS000377|
European Ancestry|
1,174 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Height (cm) : 0.1273 Sex, age
PPM000760 PGS000297
(GRS3290_Height)
PSS000378|
European Ancestry|
1,095 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Height (cm) : 0.1382 Sex, age
PPM000766 PGS000299
(GRS462_WHRadjBMI)
PSS000376|
European Ancestry|
1,354 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Waist-to-hip ratio : 0.0138 Sex, age, age^2, BMI
PPM000767 PGS000299
(GRS462_WHRadjBMI)
PSS000377|
European Ancestry|
1,174 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Waist-to-hip ratio : 0.0141 Sex, age, age^2, BMI
PPM000768 PGS000299
(GRS462_WHRadjBMI)
PSS000378|
European Ancestry|
1,095 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Waist-to-hip ratio : 0.0195 Sex, age, age^2, BMI
PPM000761 PGS000298
(GRS941_BMI)
PSS000374|
European Ancestry|
1,318 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Body mass index (kg/m2) : 0.0579 Sex, age
PPM000756 PGS000297
(GRS3290_Height)
PSS000374|
European Ancestry|
1,318 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Height (cm) : 0.1192 Sex, age
PPM000762 PGS000298
(GRS941_BMI)
PSS000375|
European Ancestry|
1,313 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Body mass index (kg/m2) : 0.0612 Sex, age
PPM000763 PGS000298
(GRS941_BMI)
PSS000376|
European Ancestry|
1,354 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Body mass index (kg/m2) : 0.0647 Sex, age
PPM000764 PGS000298
(GRS941_BMI)
PSS000377|
European Ancestry|
1,174 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Body mass index (kg/m2) : 0.0655 Sex, age
PPM000765 PGS000298
(GRS941_BMI)
PSS000378|
European Ancestry|
1,095 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Body mass index (kg/m2) : 0.0591 Sex, age
PPM001699 PGS000717
(PGS557_albs)
PSS000883|
European Ancestry|
62,541 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Obesity in individuals aged 30-69.9 AUROC: 0.6 [0.595, 0.605] Due to a lack of information in the HUNT dataset, only 546 of the 557 common variants used to build the adult score by Richardson et al were included. Additionally, 28 of the 546 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM000786 PGS000297
(GRS3290_Height)
PSS000369|
European Ancestry|
334 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Height (cm) : 0.087 Sex, age
PPM000787 PGS000297
(GRS3290_Height)
PSS000370|
European Ancestry|
329 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Height (cm) : 0.0755 Sex, age
PPM000788 PGS000297
(GRS3290_Height)
PSS000371|
European Ancestry|
288 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Height (cm) : 0.0887 Sex, age
PPM000789 PGS000297
(GRS3290_Height)
PSS000372|
European Ancestry|
265 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Height (cm) : 0.0986 Sex, age
PPM000790 PGS000297
(GRS3290_Height)
PSS000373|
European Ancestry|
245 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Height (cm) : 0.1158 Sex, age
PPM000796 PGS000299
(GRS462_WHRadjBMI)
PSS000371|
European Ancestry|
288 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Waist-to-hip ratio : 0.01 Sex, age, age^2, BMI
PPM000797 PGS000299
(GRS462_WHRadjBMI)
PSS000372|
European Ancestry|
265 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Waist-to-hip ratio : 0.017 Sex, age, age^2, BMI
PPM000798 PGS000299
(GRS462_WHRadjBMI)
PSS000373|
European Ancestry|
245 individuals
PGP000092 |
Xie T et al. Circ Genom Precis Med (2020)
Reported Trait: Waist-to-hip ratio : 0.0156 Sex, age, age^2, BMI
PPM000860 PGS000320
(PRS_BMI)
PSS000411|
European Ancestry|
104 individuals
PGP000096 |
Chami N et al. PLoS Med (2020)
Reported Trait: Body mass index (BMI; kg/m^2) in MC4R carriers Higher PRS in obese vs. normal weight MC4R mutation carriers (p-value): 1.70e-06 age, sex, 10 genetic PCs
PPM000859 PGS000320
(PRS_BMI)
PSS000412|
European Ancestry|
256,770 individuals
PGP000096 |
Chami N et al. PLoS Med (2020)
Reported Trait: Body mass index (BMI; kg/m^2) in MC4R non-carriers Higher PRS in obese vs. normal weight MC4R non-carriers (p-value): 2.00e-16 age, sex, 10 genetic PCs
PPM000858 PGS000320
(PRS_BMI)
PSS000413|
European Ancestry|
451,508 individuals
PGP000096 |
Chami N et al. PLoS Med (2020)
Reported Trait: Body mass index (BMI; kg/m^2) β: 1.29 : 0.0676 age, sex, 10 genetic PCs
PPM001768 PGS000297
(GRS3290_Height)
PSS000911|
Greater Middle Eastern Ancestry|
13,989 individuals
PGP000147 |
Thareja G et al. Nat Commun (2021)
|Ext.
Reported Trait: Height Pearson correlation coefficent (r): 0.15
PPM001769 PGS000027
(GPS_BMI)
PSS000911|
Greater Middle Eastern Ancestry|
13,989 individuals
PGP000147 |
Thareja G et al. Nat Commun (2021)
|Ext.
Reported Trait: Body mass index Pearson correlation coefficent (r): 0.22
PPM001633 PGS000716
(PGS295_elbs)
PSS000849|
European Ancestry|
5,898 individuals
PGP000132 |
Richardson TG et al. BMJ (2020)
Reported Trait: Childhood body mass index AUROC: 0.64
PPM001634 PGS000717
(PGS557_albs)
PSS000849|
European Ancestry|
5,898 individuals
PGP000132 |
Richardson TG et al. BMJ (2020)
Reported Trait: Childhood body mass index AUROC: 0.61
PPM001635 PGS000716
(PGS295_elbs)
PSS000847|
European Ancestry|
3,997 individuals
PGP000132 |
Richardson TG et al. BMJ (2020)
Reported Trait: Adolescent body mass index AUROC: 0.63
PPM001636 PGS000717
(PGS557_albs)
PSS000847|
European Ancestry|
3,997 individuals
PGP000132 |
Richardson TG et al. BMJ (2020)
Reported Trait: Adolescent body mass index AUROC: 0.63
PPM001637 PGS000716
(PGS295_elbs)
PSS000848|
European Ancestry|
2,199 individuals
PGP000132 |
Richardson TG et al. BMJ (2020)
Reported Trait: Adult body mass index AUROC: 0.57
PPM001638 PGS000717
(PGS557_albs)
PSS000848|
European Ancestry|
2,199 individuals
PGP000132 |
Richardson TG et al. BMJ (2020)
Reported Trait: Adult body mass index AUROC: 0.6
PPM001702 PGS000717
(PGS557_albs)
PSS000880|
European Ancestry|
12,179 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 18-23.9 AUROC: 0.595 [0.583, 0.607] Due to a lack of information in the HUNT dataset, only 546 of the 557 common variants used to build the adult score by Richardson et al were included. Additionally, 28 of the 546 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM001739 PGS000027
(GPS_BMI)
PSS000892|
European Ancestry|
17,740 individuals
PGP000141 |
Brandkvist M et al. PLoS Med (2020)
|Ext.
Reported Trait: Obesity between female siblings OR: 1.89 [1.74, 2.05] Sex, time of measurement, age, PCs (1-20), genotyping batch 2.07 million of the 2.1 milliion common variants (excluding those with insufficient quality of genotyping or imputation (r^2 < 0.8) in the HUNT cohort) were included in the polygenic score previously developed by Khera et al (2019).
PPM001703 PGS000717
(PGS557_albs)
PSS000882|
European Ancestry|
17,139 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 14-29.9 AUROC: 0.602 [0.593, 0.611] Due to a lack of information in the HUNT dataset, only 546 of the 557 common variants used to build the adult score by Richardson et al were included. Additionally, 28 of the 546 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM001704 PGS000717
(PGS557_albs)
PSS000884|
European Ancestry|
62,541 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 30-69.9 AUROC: 0.585 [0.58, 0.589] Due to a lack of information in the HUNT dataset, only 546 of the 557 common variants used to build the adult score by Richardson et al were included. Additionally, 28 of the 546 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM001705 PGS000716
(PGS295_elbs)
PSS000875|
European Ancestry|
3,124 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Obesity in individuals aged 12-15.9 AUROC: 0.739 [0.667, 0.811] Due to a lack of information in the HUNT dataset, only 277 of the 295 common variants used to build the childhood score by Richardson et al were included.
PPM001706 PGS000716
(PGS295_elbs)
PSS000877|
European Ancestry|
2,896 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Obesity in individuals aged 16-17.9 AUROC: 0.66 [0.566, 0.754] Due to a lack of information in the HUNT dataset, only 277 of the 295 common variants used to build the childhood score by Richardson et al were included.
PPM001737 PGS000027
(GPS_BMI)
PSS000891|
European Ancestry|
14,487 individuals
PGP000141 |
Brandkvist M et al. PLoS Med (2020)
|Ext.
Reported Trait: Obesity between male siblings OR: 2.0 [1.83, 2.19] Sex, time of measurement, age, PCs (1-20), genotyping batch 2.07 million of the 2.1 milliion common variants (excluding those with insufficient quality of genotyping or imputation (r^2 < 0.8) in the HUNT cohort) were included in the polygenic score previously developed by Khera et al (2019).
PPM001738 PGS000027
(GPS_BMI)
PSS000892|
European Ancestry|
17,740 individuals
PGP000141 |
Brandkvist M et al. PLoS Med (2020)
|Ext.
Reported Trait: Obesity within female siblings OR: 1.72 [1.53, 1.93] Sex, time of measurement, age, PCs (1-20), genotyping batch 2.07 million of the 2.1 milliion common variants (excluding those with insufficient quality of genotyping or imputation (r^2 < 0.8) in the HUNT cohort) were included in the polygenic score previously developed by Khera et al (2019).
PPM001721 PGS000717
(PGS557_albs)
PSS000880|
European Ancestry|
12,179 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 18-23.9 AUROC: 0.592 [0.58, 0.604] Due to a lack of information in the HUNT dataset, only 509 of the 557 common variants used to build the adult score by Richardson et al were included.
PPM001685 PGS000716
(PGS295_elbs)
PSS000875|
European Ancestry|
3,124 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Obesity in individuals aged 12-15.9 AUROC: 0.735 [0.663, 0.806] Due to a lack of information in the HUNT dataset, only 289 of the 295 common variants used to build the childhood score by Richardson et al were included. Additionally, 11 of the 289 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM001686 PGS000716
(PGS295_elbs)
PSS000877|
European Ancestry|
2,896 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Obesity in individuals aged 16-17.9 AUROC: 0.672 [0.577, 0.766] Due to a lack of information in the HUNT dataset, only 289 of the 295 common variants used to build the childhood score by Richardson et al were included. Additionally, 11 of the 289 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM001694 PGS000716
(PGS295_elbs)
PSS000884|
European Ancestry|
62,541 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 30-69.9 AUROC: 0.568 [0.563, 0.573] Due to a lack of information in the HUNT dataset, only 289 of the 295 common variants used to build the childhood score by Richardson et al were included. Additionally, 11 of the 289 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM001697 PGS000717
(PGS557_albs)
PSS000879|
European Ancestry|
12,179 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Obesity in individuals aged 18-23.9 AUROC: 0.629 [0.606, 0.652] Due to a lack of information in the HUNT dataset, only 546 of the 557 common variants used to build the adult score by Richardson et al were included. Additionally, 28 of the 546 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM001698 PGS000717
(PGS557_albs)
PSS000881|
European Ancestry|
17,139 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Obesity in individuals aged 24-29.9 AUROC: 0.627 [0.611, 0.643] Due to a lack of information in the HUNT dataset, only 546 of the 557 common variants used to build the adult score by Richardson et al were included. Additionally, 28 of the 546 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM001700 PGS000717
(PGS557_albs)
PSS000876|
European Ancestry|
3,124 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 12-15.9 AUROC: 0.591 [0.559, 0.623] Due to a lack of information in the HUNT dataset, only 546 of the 557 common variants used to build the adult score by Richardson et al were included. Additionally, 28 of the 546 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM001701 PGS000717
(PGS557_albs)
PSS000878|
European Ancestry|
2,896 individuals
PGP000140 |
Brandkvist M et al. Hum Mol Genet (2020)
|Ext.
Reported Trait: Overweight in individuals aged 16-17.9 AUROC: 0.618 [0.583, 0.653] Due to a lack of information in the HUNT dataset, only 546 of the 557 common variants used to build the adult score by Richardson et al were included. Additionally, 28 of the 546 common variants were proxies. Proxy SNPs were accepted if they had an R^2 ≥0.8 as well as a D’ ≥0.95 using publicly available reference haplotypes of a European British in England and Scotland population from Phase 3 (Version 5) of the 1000 Genomes Project.
PPM001724 PGS000027
(GPS_BMI)
PSS000885|
European Ancestry|
19,588 individuals
PGP000141 |
Brandkvist M et al. PLoS Med (2020)
|Ext.
Reported Trait: Obesity within male siblings OR: 2.02 [1.66, 2.46] Sex, time of measurement, age, PCs (1-20), genotyping batch 2.07 million of the 2.1 milliion common variants (excluding those with insufficient quality of genotyping or imputation (r^2 < 0.8) in the HUNT cohort) were included in the polygenic score previously developed by Khera et al (2019).
PPM001729 PGS000027
(GPS_BMI)
PSS000887|
European Ancestry|
26,323 individuals
PGP000141 |
Brandkvist M et al. PLoS Med (2020)
|Ext.
Reported Trait: Obesity between male siblings OR: 2.15 [1.97, 2.34] Sex, time of measurement, age, PCs (1-20), genotyping batch 2.07 million of the 2.1 milliion common variants (excluding those with insufficient quality of genotyping or imputation (r^2 < 0.8) in the HUNT cohort) were included in the polygenic score previously developed by Khera et al (2019).
PPM001931 PGS000758
(LASSO_Height)
PSS000967|
European Ancestry|
941 individuals
PGP000163 |
Lu T et al. J Clin Endocrinol Metab (2021)
Reported Trait: Short stature in adulthood AUROC: 0.843 [0.796, 0.89] Area under the precision-recall curve (AUPRC): 0.284 [0.102, 0.5] Sex 33,783 SNPs were utilised from the 33,938 SNP score.
PPM001932 PGS000758
(LASSO_Height)
PSS000967|
European Ancestry|
941 individuals
PGP000163 |
Lu T et al. J Clin Endocrinol Metab (2021)
Reported Trait: Short stature in adulthood (females) OR: 0.62 [0.5, 0.75] AUROC: 0.861 [0.814, 0.907] Area under the precision-recall curve (AUPRC): 0.373 [0.087, 0.651] 33,783 SNPs were utilised from the 33,938 SNP score.
PPM001930 PGS000758
(LASSO_Height)
PSS000967|
European Ancestry|
941 individuals
PGP000163 |
Lu T et al. J Clin Endocrinol Metab (2021)
Reported Trait: Standing height in adulthood : 0.71 [0.679, 0.741] Sex 33,783 SNPs were utilised from the 33,938 SNP score.
PPM001933 PGS000758
(LASSO_Height)
PSS000967|
European Ancestry|
941 individuals
PGP000163 |
Lu T et al. J Clin Endocrinol Metab (2021)
Reported Trait: Short stature in adulthood (males) OR: 0.7 [0.57, 0.83] AUROC: 0.82 [0.731, 0.909] Area under the precision-recall curve (AUPRC): 0.181 [0.044, 0.518] 33,783 SNPs were utilised from the 33,938 SNP score.
PPM001929 PGS000758
(LASSO_Height)
PSS000969|
European Ancestry|
81,902 individuals
PGP000163 |
Lu T et al. J Clin Endocrinol Metab (2021)
Reported Trait: Standing height in adulthood : 0.711 [0.708, 0.714] Age, sex, recruitment center, genotyping array, PCs(1-20)
PPM001987 PGS000770
(PRS231)
PSS000992|
European Ancestry|
177 individuals
PGP000177 |
de Toro-Martín J et al. Front Genet (2019)
Reported Trait: Obesity Odds Ratio (OR_Trend): 1.19 [1.06, 1.33] Age, sex, random effect (family relatedness)
PPM001988 PGS000770
(PRS231)
PSS000991|
European Ancestry|
141 individuals
PGP000177 |
de Toro-Martín J et al. Front Genet (2019)
Reported Trait: Obesity Odds Ratio (OR_Trend): 1.12 [1.01, 1.25] Age, sex, random effect (family relatedness)
PPM001989 PGS000770
(PRS231)
PSS000992|
European Ancestry|
177 individuals
PGP000177 |
de Toro-Martín J et al. Front Genet (2019)
Reported Trait: Obesity prevalence AUROC: 0.661 Bootstrapping (n=1000)
PPM001990 PGS000770
(PRS231)
PSS000991|
European Ancestry|
141 individuals
PGP000177 |
de Toro-Martín J et al. Front Genet (2019)
Reported Trait: Obesity prevalence AUROC: 0.619 Bootstrapping (n=1000)
PPM001991 PGS000770
(PRS231)
PSS000992|
European Ancestry|
177 individuals
PGP000177 |
de Toro-Martín J et al. Front Genet (2019)
Reported Trait: Obesity prevalence AUROC: 0.127 [0.04, 0.22] Age, sex, bootstrapping (n=1000)
PPM001992 PGS000770
(PRS231)
PSS000992|
European Ancestry|
177 individuals
PGP000177 |
de Toro-Martín J et al. Front Genet (2019)
Reported Trait: Obesity prevalence Odds Ratio (OR, top 20% vs bottom 20%): 4.77 [1.54, 16.99] Age, sex, random effect (family relatedness)
PPM001993 PGS000770
(PRS231)
PSS000992|
European Ancestry|
177 individuals
PGP000177 |
de Toro-Martín J et al. Front Genet (2019)
Reported Trait: Obesity prevalence Odds Ratio (OR, middle 40-60% vs bottom 20%): 3.46 [1.11, 12.31] Age, sex, random effect (family relatedness)
PPM001994 PGS000770
(PRS231)
PSS000991|
European Ancestry|
141 individuals
PGP000177 |
de Toro-Martín J et al. Front Genet (2019)
Reported Trait: Obesity prevalence Odds Ratio (OR, middle 60-80% vs bottom 20%): 3.79 [1.07, 15.86] Age, sex, random effect (family relatedness)
PPM001995 PGS000770
(PRS231)
PSS000992|
European Ancestry|
177 individuals
PGP000177 |
de Toro-Martín J et al. Front Genet (2019)
Reported Trait: Body mass index β: 0.33 : 0.032 Sex, age
PPM002235 PGS000830
(BMI_PGS_F)
PSS001083|
Multi-ancestry (including European)|
2,531 individuals
PGP000210 |
Zubair N et al. Sci Rep (2019)
Reported Trait: Body mass index (female) : 0.027 Age at baseline, sex, enrollment channel, PCs(1-7), observation season, observation vendor
PPM002236 PGS000829
(BMI_PGS_M)
PSS001083|
Multi-ancestry (including European)|
2,531 individuals
PGP000210 |
Zubair N et al. Sci Rep (2019)
Reported Trait: Body mass index (male) : 0.017 Age at baseline, sex, enrollment channel, PCs(1-7), observation season, observation vendor
PPM002288 PGS000841
(BMI)
PSS001086|
European Ancestry|
3,194 individuals
PGP000211 |
Aly DM et al. Nat Genet (2021)
Reported Trait: Severe Autoimmune Diabetes OR: 1.14 [1.03, 1.26] PC1-10
PPM002289 PGS000841
(BMI)
PSS001087|
European Ancestry|
3,930 individuals
PGP000211 |
Aly DM et al. Nat Genet (2021)
Reported Trait: Severe Insulin-Deficient Diabetes OR: 1.15 [1.07, 1.23] PC1-10
PPM002290 PGS000841
(BMI)
PSS001088|
European Ancestry|
3,869 individuals
PGP000211 |
Aly DM et al. Nat Genet (2021)
Reported Trait: Severe Insulin-Resistant Diabetes OR: 1.23 [1.14, 1.32] PC1-10
PPM002291 PGS000841
(BMI)
PSS001085|
European Ancestry|
4,116 individuals
PGP000211 |
Aly DM et al. Nat Genet (2021)
Reported Trait: Moderate Obesity-related Diabetes OR: 1.29 [1.21, 1.38] PC1-10
PPM002292 PGS000841
(BMI)
PSS001084|
European Ancestry|
5,597 individuals
PGP000211 |
Aly DM et al. Nat Genet (2021)
Reported Trait: Moderate Age-Related Diabetes OR: 1.05 [0.99, 1.1] PC1-10
PPM002293 PGS000842
(WHR)
PSS001086|
European Ancestry|
3,194 individuals
PGP000211 |
Aly DM et al. Nat Genet (2021)
Reported Trait: Severe Autoimmune Diabetes OR: 1.13 [1.02, 1.25] PC1-10
PPM002295 PGS000842
(WHR)
PSS001088|
European Ancestry|
3,869 individuals
PGP000211 |
Aly DM et al. Nat Genet (2021)
Reported Trait: Severe Insulin-Resistant Diabetes OR: 1.14 [1.06, 1.22] PC1-10
PPM002296 PGS000842
(WHR)
PSS001085|
European Ancestry|
4,116 individuals
PGP000211 |
Aly DM et al. Nat Genet (2021)
Reported Trait: Moderate Obesity-related Diabetes OR: 1.1 [1.03, 1.18] PC1-10
PPM002297 PGS000842
(WHR)
PSS001084|
European Ancestry|
5,597 individuals
PGP000211 |
Aly DM et al. Nat Genet (2021)
Reported Trait: Moderate Age-Related Diabetes OR: 1.03 [0.98, 1.08] PC1-10
PPM002298 PGS000843
(WHRadjBMI)
PSS001086|
European Ancestry|
3,194 individuals
PGP000211 |
Aly DM et al. Nat Genet (2021)
Reported Trait: Severe Autoimmune Diabetes OR: 1.19 [1.08, 1.31] PC1-10
PPM002299 PGS000843
(WHRadjBMI)
PSS001087|
European Ancestry|
3,930 individuals
PGP000211 |
Aly DM et al. Nat Genet (2021)
Reported Trait: Severe Insulin-Deficient Diabetes OR: 1.09 [1.02, 1.17] PC1-10
PPM002300 PGS000843
(WHRadjBMI)
PSS001088|
European Ancestry|
3,869 individuals
PGP000211 |
Aly DM et al. Nat Genet (2021)
Reported Trait: Severe Insulin-Resistant Diabetes OR: 1.15 [1.07, 1.24] PC1-10
PPM002301 PGS000843
(WHRadjBMI)
PSS001085|
European Ancestry|
4,116 individuals
PGP000211 |
Aly DM et al. Nat Genet (2021)
Reported Trait: Moderate Obesity-related Diabetes OR: 1.03 [0.96, 1.1] PC1-10
PPM002302 PGS000843
(WHRadjBMI)
PSS001084|
European Ancestry|
5,597 individuals
PGP000211 |
Aly DM et al. Nat Genet (2021)
Reported Trait: Moderate Age-Related Diabetes OR: 1.05 [0.99, 1.1] PC1-10
PPM002930 PGS000910
(PRS_BMI)
PSS001433|
European Ancestry|
5,719 individuals
PGP000238 |
Campos AI et al. medRxiv (2021)
|Pre
Reported Trait: Weight gain in Sertraline takers OR: 1.27 [1.19, 1.35] Variance explained (Nagelkerke's R2*100): 1.57 sex, age at study enrollment, genetic PCs 1-20
PPM002931 PGS000910
(PRS_BMI)
PSS001429|
European Ancestry|
4,365 individuals
PGP000238 |
Campos AI et al. medRxiv (2021)
|Pre
Reported Trait: Weight gain in Escitalopram takers OR: 1.22 [1.14, 1.31] Variance explained (Nagelkerke's R2*100): 1.17 sex, age at study enrollment, genetic PCs 1-20
PPM002932 PGS000910
(PRS_BMI)
PSS001434|
European Ancestry|
3,967 individuals
PGP000238 |
Campos AI et al. medRxiv (2021)
|Pre
Reported Trait: Weight gain in Venlafaxine takers OR: 1.23 [1.14, 1.32] Variance explained (Nagelkerke's R2*100): 1.28 sex, age at study enrollment, genetic PCs 1-20
PPM002933 PGS000910
(PRS_BMI)
PSS001425|
European Ancestry|
1,657 individuals
PGP000238 |
Campos AI et al. medRxiv (2021)
|Pre
Reported Trait: Weight gain in Amitriptyline takers OR: 1.27 [1.13, 1.42] Variance explained (Nagelkerke's R2*100): 1.74 sex, age at study enrollment, genetic PCs 1-20
PPM002935 PGS000910
(PRS_BMI)
PSS001427|
European Ancestry|
2,524 individuals
PGP000238 |
Campos AI et al. medRxiv (2021)
|Pre
Reported Trait: Weight gain in Desvenlafaxine takers OR: 1.24 [1.13, 1.36] Variance explained (Nagelkerke's R2*100): 1.36 sex, age at study enrollment, genetic PCs 1-20
PPM002936 PGS000910
(PRS_BMI)
PSS001426|
European Ancestry|
2,585 individuals
PGP000238 |
Campos AI et al. medRxiv (2021)
|Pre
Reported Trait: Weight gain in Citalopram takers OR: 1.39 [1.26, 1.53] Variance explained (Nagelkerke's R2*100): 2.83 sex, age at study enrollment, genetic PCs 1-20
PPM002937 PGS000910
(PRS_BMI)
PSS001430|
European Ancestry|
3,670 individuals
PGP000238 |
Campos AI et al. medRxiv (2021)
|Pre
Reported Trait: Weight gain in Fluoxetine takers OR: 1.3 [1.2, 1.4] Variance explained (Nagelkerke's R2*100): 1.87 sex, age at study enrollment, genetic PCs 1-20
PPM002938 PGS000910
(PRS_BMI)
PSS001428|
European Ancestry|
1,995 individuals
PGP000238 |
Campos AI et al. medRxiv (2021)
|Pre
Reported Trait: Weight gain in Duloxetine takers OR: 1.21 [1.1, 1.34] Variance explained (Nagelkerke's R2*100): 1.26 sex, age at study enrollment, genetic PCs 1-20
PPM002934 PGS000910
(PRS_BMI)
PSS001431|
European Ancestry|
1,987 individuals
PGP000238 |
Campos AI et al. medRxiv (2021)
|Pre
Reported Trait: Weight gain in Mirtazapine takers OR: 1.21 [1.1, 1.33] Variance explained (Nagelkerke's R2*100): 1.23 sex, age at study enrollment, genetic PCs 1-20
PPM002939 PGS000910
(PRS_BMI)
PSS001432|
European Ancestry|
1,580 individuals
PGP000238 |
Campos AI et al. medRxiv (2021)
|Pre
Reported Trait: Weight gain in Paroxetine takers OR: 1.19 [1.06, 1.33] Variance explained (Nagelkerke's R2*100): 0.84 sex, age at study enrollment, genetic PCs 1-20
PPM002294 PGS000842
(WHR)
PSS001087|
European Ancestry|
3,930 individuals
PGP000211 |
Aly DM et al. Nat Genet (2021)
Reported Trait: Severe Insulin-Deficient Diabetes OR: 1.04 [0.97, 1.11] PC1-10
PPM002993 PGS000921
(PRS_BMI)
PSS001456|
European Ancestry|
5,179 individuals
PGP000243 |
Borisevich D et al. PLoS One (2021)
Reported Trait: Body mass index β: 1.7 kg/m2 : 0.162
R2 (linear regression BMI ~ 1 + PRS): 0.131
age, sex
PPM005230 PGS001405
(GBE_INI12144)
PSS004801|
African Ancestry|
253 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Height : 0.58747 [0.5721, 0.60284]
Incremental R2 (full-covars): 0.01915
PGS R2 (no covariates): 0.00938 [0.00471, 0.01404]
age, sex, UKB array type, Genotype PCs
PPM005231 PGS001405
(GBE_INI12144)
PSS004802|
East Asian Ancestry|
133 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Height : 0.509 [0.47581, 0.5422]
Incremental R2 (full-covars): 0.00258
PGS R2 (no covariates): 0.00401 [-0.00197, 0.00998]
age, sex, UKB array type, Genotype PCs
PPM005232 PGS001405
(GBE_INI12144)
PSS004803|
European Ancestry|
2,131 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Height : 0.59354 [0.58576, 0.60132]
Incremental R2 (full-covars): 0.04611
PGS R2 (no covariates): 0.05219 [0.04681, 0.05757]
age, sex, UKB array type, Genotype PCs
PPM005233 PGS001405
(GBE_INI12144)
PSS004804|
South Asian Ancestry|
414 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Height : 0.56418 [0.54969, 0.57868]
Incremental R2 (full-covars): 0.03739
PGS R2 (no covariates): 0.02079 [0.01454, 0.02704]
age, sex, UKB array type, Genotype PCs
PPM005234 PGS001405
(GBE_INI12144)
PSS004805|
European Ancestry|
6,641 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Height : 0.59871 [0.59403, 0.6034]
Incremental R2 (full-covars): 0.05016
PGS R2 (no covariates): 0.05141 [0.04816, 0.05466]
age, sex, UKB array type, Genotype PCs
PPM008265 PGS001105
(GBE_INI23127)
PSS005162|
East Asian Ancestry|
1,678 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Trunk fat % : 0.13249 [0.10257, 0.16242]
Incremental R2 (full-covars): 0.02072
PGS R2 (no covariates): 0.03925 [0.02121, 0.05729]
age, sex, UKB array type, Genotype PCs
PPM007793 PGS001006
(GBE_BIN_FC1002306)
PSS003760|
African Ancestry|
6,079 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Weight change compared with 1 year ago AUROC: 0.62073 [0.60627, 0.63518] PGS R2 (no covariates): 0.00038
: 0.05769
Incremental AUROC (full-covars): -1e-05
PGS AUROC (no covariates): 0.50745 [0.49252, 0.52238]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM007794 PGS001006
(GBE_BIN_FC1002306)
PSS003761|
East Asian Ancestry|
1,602 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Weight change compared with 1 year ago AUROC: 0.59942 [0.57137, 0.62747] : 0.04761
Incremental AUROC (full-covars): 0.00968
PGS R2 (no covariates): 0.00719
PGS AUROC (no covariates): 0.53616 [0.5075, 0.56482]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM007795 PGS001006
(GBE_BIN_FC1002306)
PSS003762|
European Ancestry|
24,477 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Weight change compared with 1 year ago AUROC: 0.57635 [0.56917, 0.58352] : 0.02324
Incremental AUROC (full-covars): 0.00533
PGS R2 (no covariates): 0.00365
PGS AUROC (no covariates): 0.5289 [0.52165, 0.53616]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM007796 PGS001006
(GBE_BIN_FC1002306)
PSS003763|
South Asian Ancestry|
7,281 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Weight change compared with 1 year ago AUROC: 0.60517 [0.59225, 0.61809] : 0.04456
Incremental AUROC (full-covars): -0.00065
PGS R2 (no covariates): 0.00024
PGS AUROC (no covariates): 0.50809 [0.49482, 0.52135]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM007797 PGS001006
(GBE_BIN_FC1002306)
PSS003764|
European Ancestry|
66,390 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Weight change compared with 1 year ago AUROC: 0.58453 [0.5802, 0.58886] : 0.02896
Incremental AUROC (full-covars): 0.00463
PGS R2 (no covariates): 0.00376
PGS AUROC (no covariates): 0.5295 [0.5251, 0.5339]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM008266 PGS001105
(GBE_INI23127)
PSS005163|
European Ancestry|
24,452 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Trunk fat % : 0.23391 [0.22471, 0.24311]
Incremental R2 (full-covars): 0.07149
PGS R2 (no covariates): 0.07404 [0.06778, 0.0803]
age, sex, UKB array type, Genotype PCs
PPM008256 PGS001103
(GBE_INI23115)
PSS005108|
European Ancestry|
24,461 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg fat % (L) : 0.76237 [0.75721, 0.76752]
Incremental R2 (full-covars): 0.02314
PGS R2 (no covariates): 0.01668 [0.01353, 0.01984]
age, sex, UKB array type, Genotype PCs
PPM008269 PGS001106
(GBE_INI23119)
PSS005121|
African Ancestry|
6,302 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm fat % (R) : 0.61876 [0.60418, 0.63333]
Incremental R2 (full-covars): 0.0012
PGS R2 (no covariates): 0.00365 [0.00072, 0.00657]
age, sex, UKB array type, Genotype PCs
PPM008270 PGS001106
(GBE_INI23119)
PSS005122|
East Asian Ancestry|
1,678 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm fat % (R) : 0.46296 [0.42833, 0.49759]
Incremental R2 (full-covars): 0.00917
PGS R2 (no covariates): 0.03415 [0.01724, 0.05107]
age, sex, UKB array type, Genotype PCs
PPM008244 PGS001101
(GBE_INI23099)
PSS005036|
African Ancestry|
6,300 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Body fat % : 0.58609 [0.57069, 0.6015]
Incremental R2 (full-covars): -0.00196
PGS R2 (no covariates): 0.00632 [0.00248, 0.01016]
age, sex, UKB array type, Genotype PCs
PPM008245 PGS001101
(GBE_INI23099)
PSS005037|
East Asian Ancestry|
1,677 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Body fat % : 0.43955 [0.40434, 0.47477]
Incremental R2 (full-covars): 0.00787
PGS R2 (no covariates): 0.03564 [0.01839, 0.05289]
age, sex, UKB array type, Genotype PCs
PPM008246 PGS001101
(GBE_INI23099)
PSS005038|
European Ancestry|
24,454 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Body fat % : 0.46731 [0.45827, 0.47636]
Incremental R2 (full-covars): 0.0533
PGS R2 (no covariates): 0.04958 [0.04432, 0.05483]
age, sex, UKB array type, Genotype PCs
PPM008247 PGS001101
(GBE_INI23099)
PSS005039|
South Asian Ancestry|
7,645 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Body fat % : 0.56822 [0.5538, 0.58263]
Incremental R2 (full-covars): 0.01844
PGS R2 (no covariates): 0.0265 [0.01948, 0.03351]
age, sex, UKB array type, Genotype PCs
PPM008248 PGS001101
(GBE_INI23099)
PSS005040|
European Ancestry|
66,227 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Body fat % : 0.51245 [0.50718, 0.51771]
Incremental R2 (full-covars): 0.05409
PGS R2 (no covariates): 0.05282 [0.04954, 0.05611]
age, sex, UKB array type, Genotype PCs
PPM008249 PGS001102
(GBE_INI23123)
PSS005141|
African Ancestry|
6,301 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm fat % (L) : 0.60446 [0.58951, 0.61941]
Incremental R2 (full-covars): -3e-05
PGS R2 (no covariates): 0.00397 [0.00092, 0.00702]
age, sex, UKB array type, Genotype PCs
PPM008250 PGS001102
(GBE_INI23123)
PSS005142|
East Asian Ancestry|
1,678 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm fat % (L) : 0.47666 [0.44242, 0.5109]
Incremental R2 (full-covars): 0.00986
PGS R2 (no covariates): 0.03708 [0.01951, 0.05465]
age, sex, UKB array type, Genotype PCs
PPM008251 PGS001102
(GBE_INI23123)
PSS005143|
European Ancestry|
24,458 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm fat % (L) : 0.46034 [0.45125, 0.46943]
Incremental R2 (full-covars): 0.05013
PGS R2 (no covariates): 0.04532 [0.04027, 0.05037]
age, sex, UKB array type, Genotype PCs
PPM008252 PGS001102
(GBE_INI23123)
PSS005144|
South Asian Ancestry|
7,648 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm fat % (L) : 0.60959 [0.59609, 0.62308]
Incremental R2 (full-covars): 0.02061
PGS R2 (no covariates): 0.02621 [0.01923, 0.03318]
age, sex, UKB array type, Genotype PCs
PPM008253 PGS001102
(GBE_INI23123)
PSS005145|
European Ancestry|
66,244 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm fat % (L) : 0.51263 [0.50736, 0.5179]
Incremental R2 (full-covars): 0.04899
PGS R2 (no covariates): 0.04847 [0.04531, 0.05163]
age, sex, UKB array type, Genotype PCs
PPM008254 PGS001103
(GBE_INI23115)
PSS005106|
African Ancestry|
6,304 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg fat % (L) : 0.80234 [0.79373, 0.81095]
Incremental R2 (full-covars): -0.00147
PGS R2 (no covariates): 0.0021 [-0.00012, 0.00432]
age, sex, UKB array type, Genotype PCs
PPM008255 PGS001103
(GBE_INI23115)
PSS005107|
East Asian Ancestry|
1,679 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg fat % (L) : 0.8031 [0.78638, 0.81982]
Incremental R2 (full-covars): 2e-05
PGS R2 (no covariates): 0.01698 [0.00484, 0.02912]
age, sex, UKB array type, Genotype PCs
PPM008257 PGS001103
(GBE_INI23115)
PSS005109|
South Asian Ancestry|
7,649 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg fat % (L) : 0.82056 [0.81337, 0.82776]
Incremental R2 (full-covars): 0.00571
PGS R2 (no covariates): 0.00973 [0.00541, 0.01406]
age, sex, UKB array type, Genotype PCs
PPM008258 PGS001103
(GBE_INI23115)
PSS005110|
European Ancestry|
66,256 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg fat % (L) : 0.78472 [0.78184, 0.7876]
Incremental R2 (full-covars): 0.02307
PGS R2 (no covariates): 0.02257 [0.02035, 0.02479]
age, sex, UKB array type, Genotype PCs
PPM008259 PGS001104
(GBE_INI23111)
PSS005086|
African Ancestry|
6,305 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg fat % (R) : 0.78127 [0.77187, 0.79066]
Incremental R2 (full-covars): -0.00315
PGS R2 (no covariates): 0.00173 [-0.00029, 0.00375]
age, sex, UKB array type, Genotype PCs
PPM008260 PGS001104
(GBE_INI23111)
PSS005087|
East Asian Ancestry|
1,680 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg fat % (R) : 0.78811 [0.77028, 0.80593]
Incremental R2 (full-covars): 0.00106
PGS R2 (no covariates): 0.01989 [0.00679, 0.03299]
age, sex, UKB array type, Genotype PCs
PPM008261 PGS001104
(GBE_INI23111)
PSS005088|
European Ancestry|
24,463 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg fat % (R) : 0.74105 [0.73551, 0.74658]
Incremental R2 (full-covars): 0.02472
PGS R2 (no covariates): 0.01874 [0.0154, 0.02207]
age, sex, UKB array type, Genotype PCs
PPM008262 PGS001104
(GBE_INI23111)
PSS005089|
South Asian Ancestry|
7,649 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg fat % (R) : 0.7979 [0.78991, 0.80589]
Incremental R2 (full-covars): 0.00504
PGS R2 (no covariates): 0.00957 [0.00528, 0.01386]
age, sex, UKB array type, Genotype PCs
PPM008263 PGS001104
(GBE_INI23111)
PSS005090|
European Ancestry|
66,260 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg fat % (R) : 0.7638 [0.76069, 0.76692]
Incremental R2 (full-covars): 0.02467
PGS R2 (no covariates): 0.02454 [0.02223, 0.02685]
age, sex, UKB array type, Genotype PCs
PPM008264 PGS001105
(GBE_INI23127)
PSS005161|
African Ancestry|
6,299 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Trunk fat % : 0.33345 [0.31474, 0.35216]
Incremental R2 (full-covars): -0.0012
PGS R2 (no covariates): 0.01046 [0.00554, 0.01538]
age, sex, UKB array type, Genotype PCs
PPM008267 PGS001105
(GBE_INI23127)
PSS005164|
South Asian Ancestry|
7,646 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Trunk fat % : 0.26434 [0.2476, 0.28109]
Incremental R2 (full-covars): 0.03313
PGS R2 (no covariates): 0.04146 [0.03282, 0.05011]
age, sex, UKB array type, Genotype PCs
PPM008268 PGS001105
(GBE_INI23127)
PSS005165|
European Ancestry|
66,224 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Trunk fat % : 0.26796 [0.26224, 0.27368]
Incremental R2 (full-covars): 0.07557
PGS R2 (no covariates): 0.07501 [0.07118, 0.07883]
age, sex, UKB array type, Genotype PCs
PPM008271 PGS001106
(GBE_INI23119)
PSS005123|
European Ancestry|
24,458 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm fat % (R) : 0.45741 [0.4483, 0.46653]
Incremental R2 (full-covars): 0.04769
PGS R2 (no covariates): 0.04324 [0.03829, 0.04818]
age, sex, UKB array type, Genotype PCs
PPM008272 PGS001106
(GBE_INI23119)
PSS005124|
South Asian Ancestry|
7,649 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm fat % (R) : 0.62976 [0.61675, 0.64277]
Incremental R2 (full-covars): 0.0194
PGS R2 (no covariates): 0.02347 [0.01685, 0.0301]
age, sex, UKB array type, Genotype PCs
PPM008273 PGS001106
(GBE_INI23119)
PSS005125|
European Ancestry|
66,253 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm fat % (R) : 0.5084 [0.50311, 0.51369]
Incremental R2 (full-covars): 0.04751
PGS R2 (no covariates): 0.04648 [0.04338, 0.04959]
age, sex, UKB array type, Genotype PCs
PPM008492 PGS001154
(GBE_INI23110)
PSS005081|
African Ancestry|
6,310 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of arm (L) : 0.40184 [0.38341, 0.42027]
Incremental R2 (full-covars): 0.00929
PGS R2 (no covariates): 0.01779 [0.01142, 0.02415]
age, sex, UKB array type, Genotype PCs
PPM008493 PGS001154
(GBE_INI23110)
PSS005082|
East Asian Ancestry|
1,681 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of arm (L) : 0.57437 [0.5438, 0.60494]
Incremental R2 (full-covars): 0.01873
PGS R2 (no covariates): 0.01902 [0.0062, 0.03184]
age, sex, UKB array type, Genotype PCs
PPM008494 PGS001154
(GBE_INI23110)
PSS005083|
European Ancestry|
24,474 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of arm (L) : 0.57497 [0.56697, 0.58297]
Incremental R2 (full-covars): 0.0528
PGS R2 (no covariates): 0.05187 [0.0465, 0.05723]
age, sex, UKB array type, Genotype PCs
PPM008495 PGS001154
(GBE_INI23110)
PSS005084|
South Asian Ancestry|
7,653 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of arm (L) : 0.53183 [0.51672, 0.54695]
Incremental R2 (full-covars): 0.03819
PGS R2 (no covariates): 0.03481 [0.02684, 0.04279]
age, sex, UKB array type, Genotype PCs
PPM008496 PGS001154
(GBE_INI23110)
PSS005085|
European Ancestry|
66,310 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of arm (L) : 0.58392 [0.57912, 0.58872]
Incremental R2 (full-covars): 0.05546
PGS R2 (no covariates): 0.05563 [0.05227, 0.05899]
age, sex, UKB array type, Genotype PCs
PPM008497 PGS001155
(GBE_INI23108)
PSS005071|
African Ancestry|
6,310 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of leg (L) : 0.09719 [0.08351, 0.11087]
Incremental R2 (full-covars): 0.01992
PGS R2 (no covariates): 0.0278 [0.01992, 0.03567]
age, sex, UKB array type, Genotype PCs
PPM008498 PGS001155
(GBE_INI23108)
PSS005072|
East Asian Ancestry|
1,681 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of leg (L) : 0.31023 [0.27382, 0.34664]
Incremental R2 (full-covars): 0.0341
PGS R2 (no covariates): 0.0409 [0.02252, 0.05928]
age, sex, UKB array type, Genotype PCs
PPM008499 PGS001155
(GBE_INI23108)
PSS005073|
European Ancestry|
24,474 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of leg (L) : 0.28199 [0.27252, 0.29146]
Incremental R2 (full-covars): 0.09762
PGS R2 (no covariates): 0.09753 [0.09054, 0.10453]
age, sex, UKB array type, Genotype PCs
PPM008500 PGS001155
(GBE_INI23108)
PSS005074|
South Asian Ancestry|
7,653 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of leg (L) : 0.20912 [0.1931, 0.22513]
Incremental R2 (full-covars): 0.07366
PGS R2 (no covariates): 0.07706 [0.06572, 0.0884]
age, sex, UKB array type, Genotype PCs
PPM008501 PGS001155
(GBE_INI23108)
PSS005075|
European Ancestry|
66,310 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of leg (L) : 0.284 [0.27824, 0.28976]
Incremental R2 (full-covars): 0.10535
PGS R2 (no covariates): 0.10691 [0.1025, 0.11132]
age, sex, UKB array type, Genotype PCs
PPM008502 PGS001156
(GBE_INI23109)
PSS005076|
African Ancestry|
6,310 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of arm (R) : 0.39048 [0.37197, 0.40899]
Incremental R2 (full-covars): 0.00926
PGS R2 (no covariates): 0.01716 [0.0109, 0.02342]
age, sex, UKB array type, Genotype PCs
PPM008503 PGS001156
(GBE_INI23109)
PSS005077|
East Asian Ancestry|
1,681 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of arm (R) : 0.54175 [0.50979, 0.57371]
Incremental R2 (full-covars): 0.0153
PGS R2 (no covariates): 0.02132 [0.00778, 0.03486]
age, sex, UKB array type, Genotype PCs
PPM008504 PGS001156
(GBE_INI23109)
PSS005078|
European Ancestry|
24,473 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of arm (R) : 0.57975 [0.5718, 0.58769]
Incremental R2 (full-covars): 0.05372
PGS R2 (no covariates): 0.0533 [0.04787, 0.05873]
age, sex, UKB array type, Genotype PCs
PPM008505 PGS001156
(GBE_INI23109)
PSS005079|
South Asian Ancestry|
7,653 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of arm (R) : 0.53386 [0.51878, 0.54894]
Incremental R2 (full-covars): 0.03597
PGS R2 (no covariates): 0.03083 [0.02329, 0.03836]
age, sex, UKB array type, Genotype PCs
PPM008506 PGS001156
(GBE_INI23109)
PSS005080|
European Ancestry|
66,307 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of arm (R) : 0.57547 [0.57061, 0.58033]
Incremental R2 (full-covars): 0.05379
PGS R2 (no covariates): 0.05414 [0.05082, 0.05746]
age, sex, UKB array type, Genotype PCs
PPM008507 PGS001157
(GBE_INI23107)
PSS005066|
African Ancestry|
6,310 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of leg (R) : 0.10262 [0.08864, 0.11659]
Incremental R2 (full-covars): 0.02186
PGS R2 (no covariates): 0.02948 [0.02138, 0.03758]
age, sex, UKB array type, Genotype PCs
PPM008508 PGS001157
(GBE_INI23107)
PSS005067|
East Asian Ancestry|
1,681 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of leg (R) : 0.29891 [0.26259, 0.33523]
Incremental R2 (full-covars): 0.03502
PGS R2 (no covariates): 0.04108 [0.02267, 0.0595]
age, sex, UKB array type, Genotype PCs
PPM008509 PGS001157
(GBE_INI23107)
PSS005068|
European Ancestry|
24,475 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of leg (R) : 0.28612 [0.27664, 0.2956]
Incremental R2 (full-covars): 0.09428
PGS R2 (no covariates): 0.09552 [0.08858, 0.10246]
age, sex, UKB array type, Genotype PCs
PPM008510 PGS001157
(GBE_INI23107)
PSS005069|
South Asian Ancestry|
7,653 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of leg (R) : 0.21686 [0.20071, 0.23301]
Incremental R2 (full-covars): 0.07215
PGS R2 (no covariates): 0.07533 [0.06409, 0.08656]
age, sex, UKB array type, Genotype PCs
PPM008511 PGS001157
(GBE_INI23107)
PSS005070|
European Ancestry|
66,310 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of leg (R) : 0.29573 [0.28995, 0.30151]
Incremental R2 (full-covars): 0.10489
PGS R2 (no covariates): 0.10727 [0.10286, 0.11169]
age, sex, UKB array type, Genotype PCs
PPM008512 PGS001158
(GBE_INI23118)
PSS005116|
African Ancestry|
6,303 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg pred. mass (L) : 0.54551 [0.52919, 0.56183]
Incremental R2 (full-covars): 0.0082
PGS R2 (no covariates): 0.01474 [0.00892, 0.02055]
age, sex, UKB array type, Genotype PCs
PPM008513 PGS001158
(GBE_INI23118)
PSS005117|
East Asian Ancestry|
1,679 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg pred. mass (L) Incremental R2 (full-covars): 0.00295
: 0.73419 [0.71261, 0.75577]
PGS R2 (no covariates): 0.01537 [0.0038, 0.02694]
age, sex, UKB array type, Genotype PCs
PPM008514 PGS001158
(GBE_INI23118)
PSS005118|
European Ancestry|
24,460 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg pred. mass (L) : 0.70945 [0.70337, 0.71553]
Incremental R2 (full-covars): 0.04284
PGS R2 (no covariates): 0.04473 [0.03971, 0.04975]
age, sex, UKB array type, Genotype PCs
PPM008515 PGS001158
(GBE_INI23118)
PSS005119|
South Asian Ancestry|
7,649 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg pred. mass (L) : 0.66073 [0.64852, 0.67294]
Incremental R2 (full-covars): 0.03274
PGS R2 (no covariates): 0.02543 [0.01855, 0.03231]
age, sex, UKB array type, Genotype PCs
PPM008516 PGS001158
(GBE_INI23118)
PSS005120|
European Ancestry|
66,253 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg pred. mass (L) : 0.7033 [0.69954, 0.70705]
Incremental R2 (full-covars): 0.0494
PGS R2 (no covariates): 0.04898 [0.0458, 0.05216]
age, sex, UKB array type, Genotype PCs
PPM008517 PGS001159
(GBE_INI23114)
PSS005101|
African Ancestry|
6,305 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg pred. mass (R) : 0.55037 [0.53416, 0.56658]
Incremental R2 (full-covars): 0.00902
PGS R2 (no covariates): 0.01506 [0.00918, 0.02093]
age, sex, UKB array type, Genotype PCs
PPM008518 PGS001159
(GBE_INI23114)
PSS005102|
East Asian Ancestry|
1,680 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg pred. mass (R) : 0.73877 [0.71749, 0.76005]
Incremental R2 (full-covars): 0.00137
PGS R2 (no covariates): 0.01373 [0.00278, 0.02468]
age, sex, UKB array type, Genotype PCs
PPM008519 PGS001159
(GBE_INI23114)
PSS005103|
European Ancestry|
24,461 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg pred. mass (R) : 0.71438 [0.70839, 0.72038]
Incremental R2 (full-covars): 0.04346
PGS R2 (no covariates): 0.04404 [0.03906, 0.04902]
age, sex, UKB array type, Genotype PCs
PPM008520 PGS001159
(GBE_INI23114)
PSS005104|
South Asian Ancestry|
7,649 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg pred. mass (R) : 0.66736 [0.65533, 0.67939]
Incremental R2 (full-covars): 0.03277
PGS R2 (no covariates): 0.02632 [0.01932, 0.03331]
age, sex, UKB array type, Genotype PCs
PPM008521 PGS001159
(GBE_INI23114)
PSS005105|
European Ancestry|
66,257 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Leg pred. mass (R) : 0.70913 [0.70543, 0.71283]
Incremental R2 (full-covars): 0.04958
PGS R2 (no covariates): 0.04812 [0.04497, 0.05127]
age, sex, UKB array type, Genotype PCs
PPM008522 PGS001160
(GBE_INI23130)
PSS005176|
African Ancestry|
6,298 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Trunk pred. mass : 0.60813 [0.59327, 0.62298]
Incremental R2 (full-covars): 0.01103
PGS R2 (no covariates): 0.01817 [0.01174, 0.0246]
age, sex, UKB array type, Genotype PCs
PPM008523 PGS001160
(GBE_INI23130)
PSS005177|
East Asian Ancestry|
1,677 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Trunk pred. mass : 0.74648 [0.72572, 0.76724]
Incremental R2 (full-covars): 0.01277
PGS R2 (no covariates): 0.02185 [0.00815, 0.03555]
age, sex, UKB array type, Genotype PCs
PPM008524 PGS001160
(GBE_INI23130)
PSS005178|
European Ancestry|
24,444 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Trunk pred. mass Incremental R2 (full-covars): 0.04598
: 0.76821 [0.76317, 0.77326]
PGS R2 (no covariates): 0.04582 [0.04074, 0.05089]
age, sex, UKB array type, Genotype PCs
PPM008525 PGS001160
(GBE_INI23130)
PSS005179|
South Asian Ancestry|
7,642 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Trunk pred. mass : 0.69886 [0.68771, 0.71]
Incremental R2 (full-covars): 0.03651
PGS R2 (no covariates): 0.03063 [0.02312, 0.03815]
age, sex, UKB array type, Genotype PCs
PPM008526 PGS001160
(GBE_INI23130)
PSS005180|
European Ancestry|
66,204 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Trunk pred. mass : 0.7731 [0.77009, 0.77611]
Incremental R2 (full-covars): 0.05059
PGS R2 (no covariates): 0.05155 [0.0483, 0.0548]
age, sex, UKB array type, Genotype PCs
PPM008527 PGS001161
(GBE_INI23106)
PSS005061|
African Ancestry|
6,310 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of whole body : 0.29554 [0.27692, 0.31415]
Incremental R2 (full-covars): 0.01724
PGS R2 (no covariates): 0.02715 [0.01936, 0.03494]
age, sex, UKB array type, Genotype PCs
PPM008528 PGS001161
(GBE_INI23106)
PSS005062|
East Asian Ancestry|
1,681 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of whole body : 0.51675 [0.48383, 0.54967]
Incremental R2 (full-covars): 0.02489
PGS R2 (no covariates): 0.02929 [0.01355, 0.04503]
age, sex, UKB array type, Genotype PCs
PPM008529 PGS001161
(GBE_INI23106)
PSS005063|
European Ancestry|
24,471 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of whole body : 0.50936 [0.50067, 0.51806]
Incremental R2 (full-covars): 0.07566
PGS R2 (no covariates): 0.07383 [0.06758, 0.08008]
age, sex, UKB array type, Genotype PCs
PPM008530 PGS001161
(GBE_INI23106)
PSS005064|
South Asian Ancestry|
7,651 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of whole body : 0.44125 [0.42482, 0.45769]
Incremental R2 (full-covars): 0.05615
PGS R2 (no covariates): 0.05272 [0.04309, 0.06235]
age, sex, UKB array type, Genotype PCs
PPM008531 PGS001161
(GBE_INI23106)
PSS005065|
European Ancestry|
66,304 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Impedance of whole body : 0.51927 [0.51404, 0.5245]
Incremental R2 (full-covars): 0.08047
PGS R2 (no covariates): 0.08089 [0.07694, 0.08483]
age, sex, UKB array type, Genotype PCs
PPM008532 PGS001162
(GBE_INI49)
PSS007361|
African Ancestry|
6,416 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Hip circumference : 0.07927 [0.06667, 0.09187]
Incremental R2 (full-covars): 0.01779
PGS R2 (no covariates): 0.02214 [0.01507, 0.02921]
age, sex, UKB array type, Genotype PCs
PPM008533 PGS001162
(GBE_INI49)
PSS007362|
East Asian Ancestry|
1,698 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Hip circumference : 0.12482 [0.09552, 0.15412]
Incremental R2 (full-covars): 0.03098
PGS R2 (no covariates): 0.04899 [0.02904, 0.06893]
age, sex, UKB array type, Genotype PCs
PPM008534 PGS001162
(GBE_INI49)
PSS007363|
European Ancestry|
24,833 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Hip circumference : 0.09925 [0.09221, 0.1063]
Incremental R2 (full-covars): 0.09042
PGS R2 (no covariates): 0.09318 [0.0863, 0.10005]
age, sex, UKB array type, Genotype PCs
PPM008535 PGS001162
(GBE_INI49)
PSS007364|
South Asian Ancestry|
7,755 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Hip circumference : 0.09505 [0.08269, 0.1074]
Incremental R2 (full-covars): 0.05495
PGS R2 (no covariates): 0.05341 [0.04373, 0.0631]
age, sex, UKB array type, Genotype PCs
PPM008536 PGS001162
(GBE_INI49)
PSS007365|
European Ancestry|
67,319 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Hip circumference : 0.09693 [0.09269, 0.10118]
Incremental R2 (full-covars): 0.09505
PGS R2 (no covariates): 0.09536 [0.09115, 0.09958]
age, sex, UKB array type, Genotype PCs
PPM008686 PGS001235
(GBE_INI23122)
PSS005138|
European Ancestry|
24,458 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm pred. mass (R) : 0.76826 [0.76321, 0.7733]
Incremental R2 (full-covars): 0.03558
PGS R2 (no covariates): 0.03693 [0.03233, 0.04152]
age, sex, UKB array type, Genotype PCs
PPM008644 PGS001226
(GBE_INI20022)
PSS004896|
African Ancestry|
1,708 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Birth weight : 0.04004 [0.0307, 0.04937]
Incremental R2 (full-covars): 0.00368
PGS R2 (no covariates): 0.00672 [0.00276, 0.01067]
age, sex, UKB array type, Genotype PCs
PPM008645 PGS001226
(GBE_INI20022)
PSS004897|
East Asian Ancestry|
609 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Birth weight : 0.04464 [0.02551, 0.06377]
Incremental R2 (full-covars): 0.01768
PGS R2 (no covariates): 0.02148 [0.00789, 0.03508]
age, sex, UKB array type, Genotype PCs
PPM008646 PGS001226
(GBE_INI20022)
PSS004898|
European Ancestry|
12,908 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Birth weight : 0.06034 [0.05461, 0.06608]
Incremental R2 (full-covars): 0.02543
PGS R2 (no covariates): 0.02732 [0.02333, 0.03131]
age, sex, UKB array type, Genotype PCs
PPM008647 PGS001226
(GBE_INI20022)
PSS004899|
South Asian Ancestry|
1,908 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Birth weight : 0.04888 [0.03957, 0.05819]
Incremental R2 (full-covars): 0.00482
PGS R2 (no covariates): 0.00796 [0.00404, 0.01188]
age, sex, UKB array type, Genotype PCs
PPM008648 PGS001226
(GBE_INI20022)
PSS004900|
European Ancestry|
39,107 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Birth weight : 0.05162 [0.04837, 0.05488]
Incremental R2 (full-covars): 0.02688
PGS R2 (no covariates): 0.02741 [0.02498, 0.02984]
age, sex, UKB array type, Genotype PCs
PPM008649 PGS001227
(GBE_INI48)
PSS007356|
African Ancestry|
6,418 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Waist circumference : 0.04902 [0.03878, 0.05925]
Incremental R2 (full-covars): 0.01275
PGS R2 (no covariates): 0.01765 [0.01131, 0.024]
age, sex, UKB array type, Genotype PCs
PPM008650 PGS001227
(GBE_INI48)
PSS007357|
East Asian Ancestry|
1,698 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Waist circumference : 0.31538 [0.27895, 0.35182]
Incremental R2 (full-covars): 0.00143
PGS R2 (no covariates): 0.02593 [0.01107, 0.0408]
age, sex, UKB array type, Genotype PCs
PPM008651 PGS001227
(GBE_INI48)
PSS007358|
European Ancestry|
24,833 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Waist circumference : 0.30589 [0.29635, 0.31542]
Incremental R2 (full-covars): 0.05708
PGS R2 (no covariates): 0.0673 [0.06129, 0.07331]
age, sex, UKB array type, Genotype PCs
PPM008652 PGS001227
(GBE_INI48)
PSS007359|
South Asian Ancestry|
7,756 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Waist circumference : 0.19567 [0.17992, 0.21142]
Incremental R2 (full-covars): 0.03363
PGS R2 (no covariates): 0.03558 [0.02753, 0.04364]
age, sex, UKB array type, Genotype PCs
PPM008653 PGS001227
(GBE_INI48)
PSS007360|
European Ancestry|
67,330 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Waist circumference : 0.29142 [0.28565, 0.2972]
Incremental R2 (full-covars): 0.06556
PGS R2 (no covariates): 0.0665 [0.06287, 0.07013]
age, sex, UKB array type, Genotype PCs
PPM008654 PGS001228
(GBE_INI21001)
PSS004911|
African Ancestry|
6,398 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: BMI : 0.06264 [0.05124, 0.07404]
Incremental R2 (full-covars): 0.01685
PGS R2 (no covariates): 0.02029 [0.0135, 0.02707]
age, sex, UKB array type, Genotype PCs
PPM008655 PGS001228
(GBE_INI21001)
PSS004912|
East Asian Ancestry|
1,696 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: BMI : 0.16636 [0.13414, 0.19858]
Incremental R2 (full-covars): 0.03119
PGS R2 (no covariates): 0.06222 [0.04005, 0.08439]
age, sex, UKB array type, Genotype PCs
PPM008656 PGS001228
(GBE_INI21001)
PSS004913|
European Ancestry|
24,795 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: BMI : 0.13175 [0.12392, 0.13958]
Incremental R2 (full-covars): 0.09741
PGS R2 (no covariates): 0.10338 [0.09622, 0.11054]
age, sex, UKB array type, Genotype PCs
PPM008657 PGS001228
(GBE_INI21001)
PSS004914|
South Asian Ancestry|
7,639 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: BMI : 0.0872 [0.07527, 0.09914]
Incremental R2 (full-covars): 0.06488
PGS R2 (no covariates): 0.0648 [0.05426, 0.07534]
age, sex, UKB array type, Genotype PCs
PPM008658 PGS001228
(GBE_INI21001)
PSS004915|
European Ancestry|
67,235 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: BMI : 0.12084 [0.11623, 0.12545]
Incremental R2 (full-covars): 0.10799
PGS R2 (no covariates): 0.11052 [0.10606, 0.11499]
age, sex, UKB array type, Genotype PCs
PPM008659 PGS001229
(GBE_INI50)
PSS007396|
African Ancestry|
6,407 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Standing height : 0.50012 [0.48294, 0.5173]
Incremental R2 (full-covars): 0.02391
PGS R2 (no covariates): 0.03665 [0.02769, 0.04561]
age, sex, UKB array type, Genotype PCs
PPM008660 PGS001229
(GBE_INI50)
PSS007397|
East Asian Ancestry|
1,697 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Standing height : 0.60758 [0.5786, 0.63657]
Incremental R2 (full-covars): 0.06552
PGS R2 (no covariates): 0.09127 [0.06525, 0.11728]
age, sex, UKB array type, Genotype PCs
PPM008661 PGS001229
(GBE_INI50)
PSS007398|
European Ancestry|
24,826 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Standing height : 0.7018 [0.69559, 0.708]
Incremental R2 (full-covars): 0.16478
PGS R2 (no covariates): 0.1861 [0.17738, 0.19482]
age, sex, UKB array type, Genotype PCs
PPM008662 PGS001229
(GBE_INI50)
PSS007399|
South Asian Ancestry|
7,650 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Standing height : 0.66523 [0.65314, 0.67732]
Incremental R2 (full-covars): 0.08156
PGS R2 (no covariates): 0.08889 [0.07686, 0.10092]
age, sex, UKB array type, Genotype PCs
PPM008663 PGS001229
(GBE_INI50)
PSS007400|
European Ancestry|
67,298 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Standing height : 0.71726 [0.71364, 0.72087]
Incremental R2 (full-covars): 0.17893
PGS R2 (no covariates): 0.17757 [0.17234, 0.1828]
age, sex, UKB array type, Genotype PCs
PPM008664 PGS001230
(GBE_INI21002)
PSS004916|
African Ancestry|
6,410 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Weight : 0.0617 [0.05037, 0.07303]
Incremental R2 (full-covars): 0.0214
PGS R2 (no covariates): 0.02742 [0.01959, 0.03524]
age, sex, UKB array type, Genotype PCs
PPM008665 PGS001230
(GBE_INI21002)
PSS004917|
East Asian Ancestry|
1,696 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Weight : 0.36447 [0.32811, 0.40083]
Incremental R2 (full-covars): 0.00956
PGS R2 (no covariates): 0.0426 [0.02388, 0.06133]
age, sex, UKB array type, Genotype PCs
PPM008666 PGS001230
(GBE_INI21002)
PSS004918|
European Ancestry|
24,804 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Weight : 0.33164 [0.32208, 0.3412]
Incremental R2 (full-covars): 0.09015
PGS R2 (no covariates): 0.09999 [0.09292, 0.10705]
age, sex, UKB array type, Genotype PCs
PPM008667 PGS001230
(GBE_INI21002)
PSS004919|
South Asian Ancestry|
7,746 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Weight : 0.2472 [0.23063, 0.26377]
Incremental R2 (full-covars): 0.06508
PGS R2 (no covariates): 0.0633 [0.05287, 0.07374]
age, sex, UKB array type, Genotype PCs
PPM008668 PGS001230
(GBE_INI21002)
PSS004920|
European Ancestry|
67,258 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Weight : 0.32007 [0.31426, 0.32588]
Incremental R2 (full-covars): 0.1043
PGS R2 (no covariates): 0.10578 [0.10139, 0.11017]
age, sex, UKB array type, Genotype PCs
PPM008679 PGS001234
(GBE_INI23126)
PSS005156|
African Ancestry|
6,299 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm pred. mass (L) : 0.60051 [0.58546, 0.61555]
Incremental R2 (full-covars): 0.00953
PGS R2 (no covariates): 0.01688 [0.01067, 0.02309]
age, sex, UKB array type, Genotype PCs
PPM008680 PGS001234
(GBE_INI23126)
PSS005157|
East Asian Ancestry|
1,678 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm pred. mass (L) : 0.75129 [0.73087, 0.77172]
Incremental R2 (full-covars): 0.00307
PGS R2 (no covariates): 0.01325 [0.00249, 0.02401]
age, sex, UKB array type, Genotype PCs
PPM008681 PGS001234
(GBE_INI23126)
PSS005158|
European Ancestry|
24,454 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm pred. mass (L) : 0.74421 [0.73873, 0.74969]
Incremental R2 (full-covars): 0.03857
PGS R2 (no covariates): 0.04031 [0.03552, 0.04509]
age, sex, UKB array type, Genotype PCs
PPM008682 PGS001234
(GBE_INI23126)
PSS005159|
South Asian Ancestry|
7,647 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm pred. mass (L) : 0.67369 [0.66183, 0.68555]
Incremental R2 (full-covars): 0.03112
PGS R2 (no covariates): 0.02284 [0.0163, 0.02938]
age, sex, UKB array type, Genotype PCs
PPM008683 PGS001234
(GBE_INI23126)
PSS005160|
European Ancestry|
66,231 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm pred. mass (L) : 0.72542 [0.72189, 0.72895]
Incremental R2 (full-covars): 0.04328
PGS R2 (no covariates): 0.04384 [0.04082, 0.04687]
age, sex, UKB array type, Genotype PCs
PPM008684 PGS001235
(GBE_INI23122)
PSS005136|
African Ancestry|
6,302 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm pred. mass (R) : 0.65534 [0.64178, 0.6689]
Incremental R2 (full-covars): 0.00854
PGS R2 (no covariates): 0.01485 [0.00901, 0.02068]
age, sex, UKB array type, Genotype PCs
PPM008685 PGS001235
(GBE_INI23122)
PSS005137|
East Asian Ancestry|
1,678 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm pred. mass (R) : 0.7677 [0.74842, 0.78699]
Incremental R2 (full-covars): 0.002
PGS R2 (no covariates): 0.0134 [0.00258, 0.02423]
age, sex, UKB array type, Genotype PCs
PPM008687 PGS001235
(GBE_INI23122)
PSS005139|
South Asian Ancestry|
7,648 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm pred. mass (R) : 0.7148 [0.70413, 0.72548]
Incremental R2 (full-covars): 0.02639
PGS R2 (no covariates): 0.01722 [0.01151, 0.02293]
age, sex, UKB array type, Genotype PCs
PPM008688 PGS001235
(GBE_INI23122)
PSS005140|
European Ancestry|
66,247 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Arm pred. mass (R) : 0.76233 [0.7592, 0.76546]
Incremental R2 (full-covars): 0.03907
PGS R2 (no covariates): 0.03932 [0.03645, 0.0422]
age, sex, UKB array type, Genotype 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
PSS005180 66,204 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS004913 24,795 individuals European non-white British ancestry UKB
PSS004914 7,639 individuals South Asian UKB
PSS004915 67,235 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS000991 Cases were individuals defined as obese (BMI ≥ 30kg/m2)
[
  • 39 cases
  • , 102 controls
]
,
48.23 % Male samples
European
(French-Canadian)
FAS
PSS000992 Cases were individuals defined as obese (BMI ≥ 30kg/m2)
[
  • 50 cases
  • , 127 controls
]
,
43.5 % Male samples
European
(French-Canadian)
QFS
PSS000411 59 MC4R mutations that had previously been reported to play a role in obesity were included in our analyses (Table S2) All phenotypic data used for analyses were collected at the baseline visit. We provide a brief description here; more details can be found in S1 Text and elsewhere [18–21]. BMI, calculated as weight (kg) divided by height squared (m2), was used to categorize individuals with underweight (BMI < 18.5 kg/m2), normal weight (18.5 kg/m2 ≤ BMI < 25 kg/m2), overweight (25 kg/m2 ≤ BMI < 30 kg/m2), or obesity (BMI ≥ 30 kg/m2).
[
  • 76 cases
  • , 28 controls
]
European UKB
PSS000412 59 MC4R mutations that had previously been reported to play a role in obesity were included in our analyses (Table S2) All phenotypic data used for analyses were collected at the baseline visit. We provide a brief description here; more details can be found in S1 Text and elsewhere [18–21]. BMI, calculated as weight (kg) divided by height squared (m2), was used to categorize individuals with underweight (BMI < 18.5 kg/m2), normal weight (18.5 kg/m2 ≤ BMI < 25 kg/m2), overweight (25 kg/m2 ≤ BMI < 30 kg/m2), or obesity (BMI ≥ 30 kg/m2).
[
  • 109,216 cases
  • , 147,554 controls
]
European UKB
PSS000413 All phenotypic data used for analyses were collected at the baseline visit. We provide a brief description here; more details can be found in S1 Text and elsewhere [18–21]. BMI, calculated as weight (kg) divided by height squared (m2), was used to categorize individuals with underweight (BMI < 18.5 kg/m2), normal weight (18.5 kg/m2 ≤ BMI < 25 kg/m2), overweight (25 kg/m2 ≤ BMI < 30 kg/m2), or obesity (BMI ≥ 30 kg/m2). 206,612 individuals,
100.0 % Male samples
Mean = 57.0 years
Sd = 8.1 years
European UKB
PSS000413 All phenotypic data used for analyses were collected at the baseline visit. We provide a brief description here; more details can be found in S1 Text and elsewhere [18–21]. BMI, calculated as weight (kg) divided by height squared (m2), was used to categorize individuals with underweight (BMI < 18.5 kg/m2), normal weight (18.5 kg/m2 ≤ BMI < 25 kg/m2), overweight (25 kg/m2 ≤ BMI < 30 kg/m2), or obesity (BMI ≥ 30 kg/m2). 244,896 individuals,
0.0 % Male samples
Mean = 57.0 years
Sd = 7.9 years
European UKB
PSS003760
[
  • 3,717 cases
  • , 2,362 controls
]
African unspecified UKB
PSS003761
[
  • 664 cases
  • , 938 controls
]
East Asian UKB
PSS003762
[
  • 10,994 cases
  • , 13,483 controls
]
European non-white British ancestry UKB
PSS003763
[
  • 3,505 cases
  • , 3,776 controls
]
South Asian UKB
PSS003764
[
  • 30,581 cases
  • , 35,809 controls
]
European white British ancestry UKB Testing cohort (heldout set)
PSS000037 288,016 individuals,
45.0 % Male samples
European UKB
PSS007356 6,418 individuals African unspecified UKB
PSS007357 1,698 individuals East Asian UKB
PSS007358 24,833 individuals European non-white British ancestry UKB
PSS007359 7,756 individuals South Asian UKB
PSS007360 67,330 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS004801 253 individuals African unspecified UKB
PSS004802 133 individuals East Asian UKB
PSS004803 2,131 individuals European non-white British ancestry UKB
PSS004804 414 individuals South Asian UKB
PSS004805 6,641 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS005101 6,305 individuals African unspecified UKB
PSS007361 6,416 individuals African unspecified UKB
PSS007362 1,698 individuals East Asian UKB
PSS007363 24,833 individuals European non-white British ancestry UKB
PSS007364 7,755 individuals South Asian UKB
PSS007365 67,319 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS000045 BMI difference between ages 21 and 45 1,699 individuals,
100.0 % Male samples
European HPFS
PSS000046 BMI difference between ages 45 and 65 1,634 individuals,
100.0 % Male samples
European HPFS
PSS000047 BMI difference between ages 65 and 80 2,020 individuals,
100.0 % Male samples
European HPFS
PSS000048 BMI difference between ages 18 and 45 5,956 individuals,
0.0 % Male samples
European NHS
PSS000049 BMI difference between age 18 and Menopause 6,705 individuals,
0.0 % Male samples
European NHS
PSS000050 BMI difference between ages 45 and 65 5,640 individuals,
0.0 % Male samples
European NHS
PSS000051 BMI difference between ages 65 and 80 2,942 individuals,
0.0 % Male samples
European NHS
PSS000052 BMI difference between Menopause and age 65 6,436 individuals,
0.0 % Male samples
European NHS
PSS005102 1,680 individuals East Asian UKB
PSS007396 6,407 individuals African unspecified UKB
PSS007397 1,697 individuals East Asian UKB
PSS007398 24,826 individuals European non-white British ancestry UKB
PSS007399 7,650 individuals South Asian UKB
PSS007400 67,298 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS000847 Measurs of BMI were dichotomised to classify individuals higher than th 85th centiles as overweight. 3,997 individuals Mean = 17.8 years European ALSPAC ALSPAC offspring
PSS000848 Measurs of BMI were dichotomised to classify individuals higher than th 85th centiles as overweight. 2,199 individuals,
0.0 % Male samples
Mean = 50.8 years European ALSPAC ALSPAC mothers
PSS000849 Measurs of BMI were dichotomised to classify individuals higher than th 85th centiles as overweight. 5,898 individuals Mean = 9.9 years European ALSPAC ALSPAC offspring
PSS005103 24,461 individuals European non-white British ancestry UKB
PSS005104 7,649 individuals South Asian UKB
PSS004896 1,708 individuals African unspecified UKB
PSS004897 609 individuals East Asian UKB
PSS004898 12,908 individuals European non-white British ancestry UKB
PSS004899 1,908 individuals South Asian UKB
PSS004900 39,107 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS000875 BMI was calculated as weight in kilograms per height in metres squared. Weight was measured to the nearest half kilogram wih participants wearing light cloes and no shoes and height was measured to the nearest centimeter. WHO defines overweight as BMI greater than or equal to 25 and obesity as BMI greater than or equal to 30. BMI strongly related to longitudinal growth. To defiine corresponding cut-offs for obesity and overweight for participants younger than 18 years, BMI z score was calulated using age and sex specific reference from the International Obesity Task Force.
[
  • 37 cases
  • , 3,087 controls
]
European HUNT
PSS000876 BMI was calculated as weight in kilograms per height in metres squared. Weight was measured to the nearest half kilogram wih participants wearing light cloes and no shoes and height was measured to the nearest centimeter. WHO defines overweight as BMI greater than or equal to 25 and obesity as BMI greater than or equal to 30. BMI strongly related to longitudinal growth. To defiine corresponding cut-offs for obesity and overweight for participants younger than 18 years, BMI z score was calulated using age and sex specific reference from the International Obesity Task Force.
[
  • 344 cases
  • , 2,780 controls
]
European HUNT
PSS000877 BMI was calculated as weight in kilograms per height in metres squared. Weight was measured to the nearest half kilogram wih participants wearing light cloes and no shoes and height was measured to the nearest centimeter. WHO defines overweight as BMI greater than or equal to 25 and obesity as BMI greater than or equal to 30. BMI strongly related to longitudinal growth. To defiine corresponding cut-offs for obesity and overweight for participants younger than 18 years, BMI z score was calulated using age and sex specific reference from the International Obesity Task Force.
[
  • 30 cases
  • , 2,866 controls
]
European HUNT
PSS000878 BMI was calculated as weight in kilograms per height in metres squared. Weight was measured to the nearest half kilogram wih participants wearing light cloes and no shoes and height was measured to the nearest centimeter. WHO defines overweight as BMI greater than or equal to 25 and obesity as BMI greater than or equal to 30. BMI strongly related to longitudinal growth. To defiine corresponding cut-offs for obesity and overweight for participants younger than 18 years, BMI z score was calulated using age and sex specific reference from the International Obesity Task Force.
[
  • 274 cases
  • , 2,622 controls
]
European HUNT
PSS000879 BMI was calculated as weight in kilograms per height in metres squared. Weight was measured to the nearest half kilogram wih participants wearing light cloes and no shoes and height was measured to the nearest centimeter. WHO defines overweight as BMI greater than or equal to 25 and obesity as BMI greater than or equal to 30. BMI strongly related to longitudinal growth.
[
  • 542 cases
  • , 11,637 controls
]
European HUNT
PSS000880 BMI was calculated as weight in kilograms per height in metres squared. Weight was measured to the nearest half kilogram wih participants wearing light cloes and no shoes and height was measured to the nearest centimeter. WHO defines overweight as BMI greater than or equal to 25 and obesity as BMI greater than or equal to 30. BMI strongly related to longitudinal growth.
[
  • 2,898 cases
  • , 9,281 controls
]
European HUNT
PSS000881 BMI was calculated as weight in kilograms per height in metres squared. Weight was measured to the nearest half kilogram wih participants wearing light cloes and no shoes and height was measured to the nearest centimeter. WHO defines overweight as BMI greater than or equal to 25 and obesity as BMI greater than or equal to 30. BMI strongly related to longitudinal growth.
[
  • 1,240 cases
  • , 15,899 controls
]
European HUNT
PSS000882 BMI was calculated as weight in kilograms per height in metres squared. Weight was measured to the nearest half kilogram wih participants wearing light cloes and no shoes and height was measured to the nearest centimeter. WHO defines overweight as BMI greater than or equal to 25 and obesity as BMI greater than or equal to 30. BMI strongly related to longitudinal growth.
[
  • 5,775 cases
  • , 11,364 controls
]
European HUNT
PSS000883 BMI was calculated as weight in kilograms per height in metres squared. Weight was measured to the nearest half kilogram wih participants wearing light cloes and no shoes and height was measured to the nearest centimeter. WHO defines overweight as BMI greater than or equal to 25 and obesity as BMI greater than or equal to 30. BMI strongly related to longitudinal growth.
[
  • 15,112 cases
  • , 47,429 controls
]
European HUNT
PSS000884 BMI was calculated as weight in kilograms per height in metres squared. Weight was measured to the nearest half kilogram wih participants wearing light cloes and no shoes and height was measured to the nearest centimeter. WHO defines overweight as BMI greater than or equal to 25 and obesity as BMI greater than or equal to 30. BMI strongly related to longitudinal growth.
[
  • 43,408 cases
  • , 19,133 controls
]
European HUNT
PSS000885 Body mass index (BMI) measurements were standardized with weight measured to the nearest half kilogram with the participants wearing light clothes and no shoes, and height was measured to the nearest centimeter. BMI was calculated using the formula weight in kilograms per meter squared. As defined by the World Health Organization (WHO), the authors rrefer to overweight as having BMI greater than or equal to 25 and obesity as having BMI greater than or equal to 30. Severe obesity was defined as having BMI greater than or equal to 35.
[
  • 19,588 cases
]
,
100.0 % Male samples
Mean = 42.4 years
Sd = 12.6 years
European HUNT number o cases = number of observations (more than 1 pp)
PSS000886 Body mass index (BMI) measurements were standardized with weight measured to the nearest half kilogram with the participants wearing light clothes and no shoes, and height was measured to the nearest centimeter. BMI was calculated using the formula weight in kilograms per meter squared. As defined by the World Health Organization (WHO), the authors rrefer to overweight as having BMI greater than or equal to 25 and obesity as having BMI greater than or equal to 30. Severe obesity was defined as having BMI greater than or equal to 35.
[
  • 21,499 cases
]
,
0.0 % Male samples
Mean = 42.6 years
Sd = 12.8 years
European HUNT number o cases = number of observations (more than 1 pp)
PSS000887 Body mass index (BMI) measurements were standardized with weight measured to the nearest half kilogram with the participants wearing light clothes and no shoes, and height was measured to the nearest centimeter. BMI was calculated using the formula weight in kilograms per meter squared. As defined by the World Health Organization (WHO), the authors rrefer to overweight as having BMI greater than or equal to 25 and obesity as having BMI greater than or equal to 30. Severe obesity was defined as having BMI greater than or equal to 35.
[
  • 26,336 cases
]
,
100.0 % Male samples
Mean = 47.6 years
Sd = 16.1 years
European HUNT number o cases = number of observations (more than 1 pp)
PSS000888 Body mass index (BMI) measurements were standardized with weight measured to the nearest half kilogram with the participants wearing light clothes and no shoes, and height was measured to the nearest centimeter. BMI was calculated using the formula weight in kilograms per meter squared. As defined by the World Health Organization (WHO), the authors rrefer to overweight as having BMI greater than or equal to 25 and obesity as having BMI greater than or equal to 30. Severe obesity was defined as having BMI greater than or equal to 35.
[
  • 29,545 cases
]
,
0.0 % Male samples
Mean = 47.1 years
Sd = 16.5 years
European HUNT number o cases = number of observations (more than 1 pp)
PSS000889 Body mass index (BMI) measurements were standardized with weight measured to the nearest half kilogram with the participants wearing light clothes and no shoes, and height was measured to the nearest centimeter. BMI was calculated using the formula weight in kilograms per meter squared. As defined by the World Health Organization (WHO), the authors rrefer to overweight as having BMI greater than or equal to 25 and obesity as having BMI greater than or equal to 30. Severe obesity was defined as having BMI greater than or equal to 35.
[
  • 21,192 cases
]
,
100.0 % Male samples
Mean = 52.3 years
Sd = 14.5 years
European HUNT number o cases = number of observations (more than 1 pp)
PSS000890 Body mass index (BMI) measurements were standardized with weight measured to the nearest half kilogram with the participants wearing light clothes and no shoes, and height was measured to the nearest centimeter. BMI was calculated using the formula weight in kilograms per meter squared. As defined by the World Health Organization (WHO), the authors rrefer to overweight as having BMI greater than or equal to 25 and obesity as having BMI greater than or equal to 30. Severe obesity was defined as having BMI greater than or equal to 35.
[
  • 25,108 cases
]
,
0.0 % Male samples
Mean = 51.2 years
Sd = 15.0 years
European HUNT number o cases = number of observations (more than 1 pp)
PSS000891 Body mass index (BMI) measurements were standardized with weight measured to the nearest half kilogram with the participants wearing light clothes and no shoes, and height was measured to the nearest centimeter. BMI was calculated using the formula weight in kilograms per meter squared. As defined by the World Health Organization (WHO), the authors rrefer to overweight as having BMI greater than or equal to 25 and obesity as having BMI greater than or equal to 30. Severe obesity was defined as having BMI greater than or equal to 35.
[
  • 14,487 cases
]
,
100.0 % Male samples
Mean = 60.2 years
Sd = 11.5 years
European HUNT number o cases = number of observations (more than 1 pp)
PSS000892 Body mass index (BMI) measurements were standardized with weight measured to the nearest half kilogram with the participants wearing light clothes and no shoes, and height was measured to the nearest centimeter. BMI was calculated using the formula weight in kilograms per meter squared. As defined by the World Health Organization (WHO), the authors rrefer to overweight as having BMI greater than or equal to 25 and obesity as having BMI greater than or equal to 30. Severe obesity was defined as having BMI greater than or equal to 35.
[
  • 17,740 cases
]
,
0.0 % Male samples
Mean = 58.9 years
Sd = 11.9 years
European HUNT number o cases = number of observations (more than 1 pp)
PSS004916 6,410 individuals African unspecified UKB
PSS004917 1,696 individuals East Asian UKB
PSS004918 24,804 individuals European non-white British ancestry UKB
PSS004919 7,746 individuals South Asian UKB
PSS004920 67,258 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS005070 66,310 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS000911 13,989 individuals Greater Middle Eastern (Middle Eastern, North African or Persian)
(Qatari)
QBB
PSS001425
[
  • 140 cases
  • , 1,517 controls
]
European AGDS
PSS001426
[
  • 367 cases
  • , 2,218 controls
]
European AGDS
PSS001427
[
  • 489 cases
  • , 2,035 controls
]
European AGDS
PSS001428
[
  • 382 cases
  • , 1,613 controls
]
European AGDS
PSS001429
[
  • 712 cases
  • , 3,653 controls
]
European AGDS
PSS001430
[
  • 583 cases
  • , 3,087 controls
]
European AGDS
PSS001431
[
  • 186 cases
  • , 1,801 controls
]
European AGDS
PSS001432
[
  • 236 cases
  • , 1,344 controls
]
European AGDS
PSS001433
[
  • 1,020 cases
  • , 4,699 controls
]
European AGDS
PSS001434
[
  • 794 cases
  • , 3,173 controls
]
European AGDS
PSS005036 6,300 individuals African unspecified UKB
PSS005037 1,677 individuals East Asian UKB
PSS005038 24,454 individuals European non-white British ancestry UKB
PSS005039 7,645 individuals South Asian UKB
PSS005040 66,227 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS005061 6,310 individuals African unspecified UKB
PSS005062 1,681 individuals East Asian UKB
PSS005063 24,471 individuals European non-white British ancestry UKB
PSS005064 7,651 individuals South Asian UKB
PSS005065 66,304 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS005066 6,310 individuals African unspecified UKB
PSS005067 1,681 individuals East Asian UKB
PSS005068 24,475 individuals European non-white British ancestry UKB
PSS005069 7,653 individuals South Asian UKB
PSS001456 5,179 individuals European
(Danish)
Inter99 Independent subset of Inter99, doesn't overlap with score development samples
PSS005071 6,310 individuals African unspecified UKB
PSS005072 1,681 individuals East Asian UKB
PSS005073 24,474 individuals European non-white British ancestry UKB
PSS005074 7,653 individuals South Asian UKB
PSS005075 66,310 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS005076 6,310 individuals African unspecified UKB
PSS005077 1,681 individuals East Asian UKB
PSS005078 24,473 individuals European non-white British ancestry UKB
PSS005079 7,653 individuals South Asian UKB
PSS005080 66,307 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS005081 6,310 individuals African unspecified UKB
PSS005082 1,681 individuals East Asian UKB
PSS001083 Of the 2,531 participants, 1,809 had longitudinal observations for total cholesterol (mg/dL), high density lipoprotein cholesterol (mg/dL) and trigycerides (mg/dL), 1,801 had longitudinal observations for low density lipoprotein cholesterol (mg/dL), 1,325 had longitudinal observations for waist circumference (inches), 2,355 had longitudinal observations for body mass index (kg/m^2) and 1,572 had longitudinal observations for homocysteine (μmol/L). 2,531 individuals,
40.0 % Male samples
Mean = 48.0 years
Sd = 12.0 years
European, Asian unspecified, Hispanic or Latin American, African unspecified, NR European = 1,999, Asian unspecified = 228, Hispanic or Latin American = 101, African unspecified = 51, Not reported = 152 NR Participants were obtained from the Scientific Wellness Program.
PSS005083 24,474 individuals European non-white British ancestry UKB
PSS005084 7,653 individuals South Asian UKB
PSS005086 6,305 individuals African unspecified UKB
PSS005087 1,680 individuals East Asian UKB
PSS005088 24,463 individuals European non-white British ancestry UKB
PSS005089 7,649 individuals South Asian UKB
PSS005090 66,260 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS005085 66,310 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS000369 Individuals who have been referred to a child psychiatric outpatient clinic in the Northern Netherlands before the age of 11. 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). 334 individuals,
69.2 % Male samples
Mean = 11.1 years
Sd = 0.48 years
European TRAILS, TRAILSCC TRAILS Clinical Cohort
PSS000370 Individuals who have been referred to a child psychiatric outpatient clinic in the Northern Netherlands before the age of 11. 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). 329 individuals,
69.2 % Male samples
Mean = 12.81 years
Sd = 0.59 years
European TRAILS, TRAILSCC TRAILS Clinical Cohort
PSS000371 Individuals who have been referred to a child psychiatric outpatient clinic in the Northern Netherlands before the age of 11. 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). 288 individuals,
69.2 % Male samples
Mean = 15.83 years
Sd = 0.6 years
European TRAILS, TRAILSCC TRAILS Clinical Cohort
PSS000372 Individuals who have been referred to a child psychiatric outpatient clinic in the Northern Netherlands before the age of 11. 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). 265 individuals,
69.2 % Male samples
Mean = 19.2 years
Sd = 0.66 years
European TRAILS, TRAILSCC TRAILS Clinical Cohort
PSS000373 Individuals who have been referred to a child psychiatric outpatient clinic in the Northern Netherlands before the age of 11. 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). 245 individuals,
69.2 % Male samples
Mean = 22.04 years
Sd = 0.69 years
European TRAILS, TRAILSCC TRAILS Clinical Cohort
PSS000374 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). 1,318 individuals,
47.6 % Male samples
Mean = 11.1 years European TRAILS
PSS000375 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). 1,313 individuals,
47.6 % Male samples
Mean = 13.5 years European TRAILS
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
PSS000377 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). 1,174 individuals,
47.6 % Male samples
Mean = 19.2 years European TRAILS
PSS000378 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). 1,095 individuals,
47.6 % Male samples
Mean = 22.4 years European TRAILS
PSS001084 Moderate Age-Related Diabetes (MARD) vs. controls
[
  • 2,853 cases
  • , 2,744 controls
]
European Swedish ANDIS
PSS001085 Moderate Obesity-related Diabetes (MOD) vs. controls
[
  • 1,372 cases
  • , 2,744 controls
]
European Swedish ANDIS
PSS001086 Severe Autoimmune Diabetes (SAID) vs. controls
[
  • 450 cases
  • , 2,744 controls
]
European Swedish ANDIS
PSS001087 Severe Insulin-Deficient Diabetes (SIDD) vs. controls
[
  • 1,186 cases
  • , 2,744 controls
]
European Swedish ANDIS
PSS001088 Severe Insulin-Resistant Diabetes (SIRD) vs. controls
[
  • 1,125 cases
  • , 2,744 controls
]
European Swedish ANDIS
PSS005108 24,461 individuals European non-white British ancestry UKB
PSS000967
[
  • 21 cases
  • , 920 controls
]
,
42.5 % Male samples
European ALSPAC
PSS005106 6,304 individuals African unspecified UKB
PSS000969 81,902 individuals,
45.9 % Male samples
European UKB This dataset is independent of UKB source/training and model selection datasets
PSS005107 1,679 individuals East Asian UKB
PSS005109 7,649 individuals South Asian UKB
PSS005110 66,256 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS005116 6,303 individuals African unspecified UKB
PSS005117 1,679 individuals East Asian UKB
PSS005121 6,302 individuals African unspecified UKB
PSS005122 1,678 individuals East Asian UKB
PSS005123 24,458 individuals European non-white British ancestry UKB
PSS005124 7,649 individuals South Asian UKB
PSS005125 66,253 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS005118 24,460 individuals European non-white British ancestry UKB
PSS005119 7,649 individuals South Asian UKB
PSS005120 66,253 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS005105 66,257 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS005136 6,302 individuals African unspecified UKB
PSS005137 1,678 individuals East Asian UKB
PSS005138 24,458 individuals European non-white British ancestry UKB
PSS005139 7,648 individuals South Asian UKB
PSS005140 66,247 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS005141 6,301 individuals African unspecified UKB
PSS005142 1,678 individuals East Asian UKB
PSS005143 24,458 individuals European non-white British ancestry UKB
PSS005144 7,648 individuals South Asian UKB
PSS005145 66,244 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS005156 6,299 individuals African unspecified UKB
PSS005157 1,678 individuals East Asian UKB
PSS005158 24,454 individuals European non-white British ancestry UKB
PSS005159 7,647 individuals South Asian UKB
PSS005160 66,231 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS005161 6,299 individuals African unspecified UKB
PSS005162 1,678 individuals East Asian UKB
PSS005163 24,452 individuals European non-white British ancestry UKB
PSS005164 7,646 individuals South Asian UKB
PSS005165 66,224 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS004912 1,696 individuals East Asian UKB
PSS004911 6,398 individuals African unspecified UKB
PSS005176 6,298 individuals African unspecified UKB
PSS005177 1,677 individuals East Asian UKB
PSS005178 24,444 individuals European non-white British ancestry UKB
PSS005179 7,642 individuals South Asian UKB