Experimental Factor Ontology (EFO) Information | |
Identifier | EFO_0004340 |
Description | An indicator of body density as determined by the relationship of BODY WEIGHT to BODY HEIGHT. BMI=weight (kg)/height squared (m2). BMI correlates with body fat (ADIPOSE TISSUE). Their relationship varies with age and gender. For adults, BMI falls into these categories: below 18.5 (underweight); 18.5-24.9 (normal); 25.0-29.9 (overweight); 30.0 and above (obese). (National Center for Health Statistics, Centers for Disease Control and Prevention) | Trait category |
Body measurement
|
Synonyms |
2 synonyms
|
Mapped terms |
5 mapped terms
|
Child trait(s) | overweight body mass index status |
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 | |
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 |
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 |
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 |
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 | |
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 |
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 |
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 |
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 | |
PGS001228 (GBE_INI21001) |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Body mass index | body mass index | 27,126 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001228/ScoringFiles/PGS001228.txt.gz |
PGS001825 (portability-PLR_278) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Overweight, obesity and other hyperalimentation | obesity, overweight body mass index status, overnutrition |
13,009 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001825/ScoringFiles/PGS001825.txt.gz |
PGS001943 (portability-PLR_log_BMI) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Body mass index (BMI) | body mass index | 110,153 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001943/ScoringFiles/PGS001943.txt.gz |
PGS002033 (portability-ldpred2_278) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Overweight, obesity and other hyperalimentation | obesity, overweight body mass index status, overnutrition |
846,292 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002033/ScoringFiles/PGS002033.txt.gz |
PGS002161 (portability-ldpred2_log_BMI) |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Body mass index (BMI) | body mass index | 990,022 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002161/ScoringFiles/PGS002161.txt.gz |
PGS002251 (PRS97_BMI) |
PGP000278 | Dashti HS et al. BMC Med (2022) |
Body mass index | body mass index | 97 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002251/ScoringFiles/PGS002251.txt.gz |
PGS002313 (body_BMIz.BOLT-LMM) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
BMI | body mass index | 1,109,311 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002313/ScoringFiles/PGS002313.txt.gz |
PGS002360 (body_BMIz.BOLT-LMM-BBJ) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
BMI | body mass index | 920,920 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002360/ScoringFiles/PGS002360.txt.gz |
PGS002385 (body_BMIz.P+T.0.0001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
BMI | body mass index | 15,518 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002385/ScoringFiles/PGS002385.txt.gz |
PGS002434 (body_BMIz.P+T.0.001) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
BMI | body mass index | 41,662 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002434/ScoringFiles/PGS002434.txt.gz |
PGS002483 (body_BMIz.P+T.0.01) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
BMI | body mass index | 162,598 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002483/ScoringFiles/PGS002483.txt.gz |
PGS002532 (body_BMIz.P+T.1e-06) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
BMI | body mass index | 4,284 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002532/ScoringFiles/PGS002532.txt.gz |
PGS002581 (body_BMIz.P+T.5e-08) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
BMI | body mass index | 2,385 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002581/ScoringFiles/PGS002581.txt.gz |
PGS002630 (body_BMIz.PolyFun-pred) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
BMI | body mass index | 620,484 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002630/ScoringFiles/PGS002630.txt.gz |
PGS002679 (body_BMIz.SBayesR) |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
BMI | body mass index | 987,879 | - |
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002679/ScoringFiles/PGS002679.txt.gz |
PGS Performance Metric ID (PPM) |
Evaluated Score |
PGS Sample Set ID (PSS) |
Performance Source | Trait |
PGS Effect Sizes (per SD change) |
Classification Metrics | Other Metrics | Covariates Included in the Model |
PGS Performance: Other Relevant Information |
---|---|---|---|---|---|---|---|---|---|
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 | — | R²: 0.08526 | First 10 genetic PCs | Beta is in units of kg/m^2 |
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). |
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). |
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). |
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). |
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 | — | — |
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). |
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). |
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). |
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] | — | — |
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] | — | — |
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] | — | — |
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] | — | — |
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) | — | — | R²: 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) | — | — | R²: 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) | — | — | R²: 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) | — | — | R²: 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) | — | — | R²: 0.1194 | Sex, age | — |
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) | — | — | R²: 0.0579 | 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) | — | — | R²: 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) | — | — | R²: 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) | — | — | R²: 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) | — | — | R²: 0.0591 | Sex, age | — |
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 | — | R²: 0.0676 | age, sex, 10 genetic PCs | — |
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. |
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. |
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. |
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 | — | — | — |
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 | — | — | — |
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 | — | — | — |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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 | — | — | — |
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 | — | — | — |
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. |
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. |
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. |
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. |
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 | — | R²: 0.032 | Sex, age | — |
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) | — | — | R²: 0.017 | Age at baseline, sex, enrollment channel, PCs(1-7), observation season, observation vendor | — |
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) | — | — | R²: 0.027 | 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 | — |
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 | — |
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 | — | R²: 0.162 R2 (linear regression BMI ~ 1 + PRS): 0.131 |
age, sex | — |
PPM008654 | PGS001228 (GBE_INI21001) |
PSS004911| African Ancestry| 6,398 individuals |
PGP000244 | Tanigawa Y et al. PLoS Genet (2022) |
Reported Trait: BMI | — | — | R²: 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. PLoS Genet (2022) |
Reported Trait: BMI | — | — | R²: 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. PLoS Genet (2022) |
Reported Trait: BMI | — | — | R²: 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. PLoS Genet (2022) |
Reported Trait: BMI | — | — | R²: 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. PLoS Genet (2022) |
Reported Trait: BMI | — | — | R²: 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 | — |
PPM009497 | PGS001825 (portability-PLR_278) |
PSS009295| European Ancestry| 20,000 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Overweight, obesity and other hyperalimentation | — | — | Partial Correlation (partial-r): 0.0547 [0.0409, 0.0686] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM009499 | PGS001825 (portability-PLR_278) |
PSS008623| European Ancestry| 6,660 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Overweight, obesity and other hyperalimentation | — | — | Partial Correlation (partial-r): 0.0516 [0.0276, 0.0756] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM009500 | PGS001825 (portability-PLR_278) |
PSS008398| Greater Middle Eastern Ancestry| 1,200 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Overweight, obesity and other hyperalimentation | — | — | Partial Correlation (partial-r): 0.0493 [-0.0078, 0.106] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM009501 | PGS001825 (portability-PLR_278) |
PSS008177| South Asian Ancestry| 6,331 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Overweight, obesity and other hyperalimentation | — | — | Partial Correlation (partial-r): 0.0334 [0.0087, 0.058] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM009502 | PGS001825 (portability-PLR_278) |
PSS007963| East Asian Ancestry| 1,810 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Overweight, obesity and other hyperalimentation | — | — | Partial Correlation (partial-r): -0.0049 [-0.0513, 0.0414] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM009503 | PGS001825 (portability-PLR_278) |
PSS007744| African Ancestry| 2,484 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Overweight, obesity and other hyperalimentation | — | — | Partial Correlation (partial-r): 0.0323 [-0.0072, 0.0717] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM009504 | PGS001825 (portability-PLR_278) |
PSS008848| African Ancestry| 3,924 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Overweight, obesity and other hyperalimentation | — | — | Partial Correlation (partial-r): 0.0208 [-0.0105, 0.0522] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM009498 | PGS001825 (portability-PLR_278) |
PSS009069| European Ancestry| 4,136 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Overweight, obesity and other hyperalimentation | — | — | Partial Correlation (partial-r): 0.0453 [0.0148, 0.0758] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM010419 | PGS001943 (portability-PLR_log_BMI) |
PSS009429| European Ancestry| 19,937 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Body mass index (BMI) | — | — | Partial Correlation (partial-r): 0.3698 [0.3577, 0.3817] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM010420 | PGS001943 (portability-PLR_log_BMI) |
PSS009203| European Ancestry| 4,124 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Body mass index (BMI) | — | — | Partial Correlation (partial-r): 0.3628 [0.336, 0.3891] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM010421 | PGS001943 (portability-PLR_log_BMI) |
PSS008757| European Ancestry| 6,622 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Body mass index (BMI) | — | — | Partial Correlation (partial-r): 0.3557 [0.3345, 0.3766] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM010422 | PGS001943 (portability-PLR_log_BMI) |
PSS008531| Greater Middle Eastern Ancestry| 1,178 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Body mass index (BMI) | — | — | Partial Correlation (partial-r): 0.3001 [0.2468, 0.3517] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM010423 | PGS001943 (portability-PLR_log_BMI) |
PSS008309| South Asian Ancestry| 6,145 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Body mass index (BMI) | — | — | Partial Correlation (partial-r): 0.3048 [0.2819, 0.3274] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM010424 | PGS001943 (portability-PLR_log_BMI) |
PSS008086| East Asian Ancestry| 1,799 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Body mass index (BMI) | — | — | Partial Correlation (partial-r): 0.2501 [0.206, 0.2931] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM010425 | PGS001943 (portability-PLR_log_BMI) |
PSS007873| African Ancestry| 2,444 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Body mass index (BMI) | — | — | Partial Correlation (partial-r): 0.1973 [0.1587, 0.2352] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM010426 | PGS001943 (portability-PLR_log_BMI) |
PSS008977| African Ancestry| 3,859 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Body mass index (BMI) | — | — | Partial Correlation (partial-r): 0.1571 [0.1261, 0.1878] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM011135 | PGS002033 (portability-ldpred2_278) |
PSS009295| European Ancestry| 20,000 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Overweight, obesity and other hyperalimentation | — | — | Partial Correlation (partial-r): 0.0789 [0.0651, 0.0927] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM011137 | PGS002033 (portability-ldpred2_278) |
PSS008623| European Ancestry| 6,660 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Overweight, obesity and other hyperalimentation | — | — | Partial Correlation (partial-r): 0.0824 [0.0585, 0.1062] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM011138 | PGS002033 (portability-ldpred2_278) |
PSS008398| Greater Middle Eastern Ancestry| 1,200 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Overweight, obesity and other hyperalimentation | — | — | Partial Correlation (partial-r): 0.0801 [0.0232, 0.1366] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM011139 | PGS002033 (portability-ldpred2_278) |
PSS008177| South Asian Ancestry| 6,331 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Overweight, obesity and other hyperalimentation | — | — | Partial Correlation (partial-r): 0.0502 [0.0255, 0.0747] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM011140 | PGS002033 (portability-ldpred2_278) |
PSS007963| East Asian Ancestry| 1,810 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Overweight, obesity and other hyperalimentation | — | — | Partial Correlation (partial-r): 0.0109 [-0.0354, 0.0572] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM011141 | PGS002033 (portability-ldpred2_278) |
PSS007744| African Ancestry| 2,484 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Overweight, obesity and other hyperalimentation | — | — | Partial Correlation (partial-r): 0.0265 [-0.013, 0.0659] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM011142 | PGS002033 (portability-ldpred2_278) |
PSS008848| African Ancestry| 3,924 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Overweight, obesity and other hyperalimentation | — | — | Partial Correlation (partial-r): 0.0516 [0.0203, 0.0829] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM011136 | PGS002033 (portability-ldpred2_278) |
PSS009069| European Ancestry| 4,136 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Overweight, obesity and other hyperalimentation | — | — | Partial Correlation (partial-r): 0.0523 [0.0218, 0.0827] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM012135 | PGS002161 (portability-ldpred2_log_BMI) |
PSS009429| European Ancestry| 19,937 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Body mass index (BMI) | — | — | Partial Correlation (partial-r): 0.3664 [0.3544, 0.3784] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM012136 | PGS002161 (portability-ldpred2_log_BMI) |
PSS009203| European Ancestry| 4,124 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Body mass index (BMI) | — | — | Partial Correlation (partial-r): 0.3698 [0.3431, 0.3959] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM012137 | PGS002161 (portability-ldpred2_log_BMI) |
PSS008757| European Ancestry| 6,622 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Body mass index (BMI) | — | — | Partial Correlation (partial-r): 0.3595 [0.3384, 0.3804] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM012138 | PGS002161 (portability-ldpred2_log_BMI) |
PSS008531| Greater Middle Eastern Ancestry| 1,178 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Body mass index (BMI) | — | — | Partial Correlation (partial-r): 0.2932 [0.2396, 0.345] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM012139 | PGS002161 (portability-ldpred2_log_BMI) |
PSS008309| South Asian Ancestry| 6,145 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Body mass index (BMI) | — | — | Partial Correlation (partial-r): 0.308 [0.2851, 0.3305] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM012141 | PGS002161 (portability-ldpred2_log_BMI) |
PSS007873| African Ancestry| 2,444 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Body mass index (BMI) | — | — | Partial Correlation (partial-r): 0.2104 [0.1721, 0.2482] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM012142 | PGS002161 (portability-ldpred2_log_BMI) |
PSS008977| African Ancestry| 3,859 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Body mass index (BMI) | — | — | Partial Correlation (partial-r): 0.1573 [0.1263, 0.188] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM012140 | PGS002161 (portability-ldpred2_log_BMI) |
PSS008086| East Asian Ancestry| 1,799 individuals |
PGP000263 | Privé F et al. Am J Hum Genet (2022) |
Reported Trait: Body mass index (BMI) | — | — | Partial Correlation (partial-r): 0.2694 [0.2257, 0.3119] | sex, age, birth date, deprivation index, 16 PCs | — |
PPM012804 | PGS002251 (PRS97_BMI) |
PSS009565| European Ancestry| 33,511 individuals |
PGP000278 | Dashti HS et al. BMC Med (2022) |
Reported Trait: Body mass index | β: 0.83 [0.76, 0.9] | — | — | age, sex, genotyping array, and 5 PCs of ancestry | beta unit kg/m2 |
PPM013078 | PGS002313 (body_BMIz.BOLT-LMM) |
PSS009695| African Ancestry| 6,399 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.032 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013127 | PGS002313 (body_BMIz.BOLT-LMM) |
PSS009696| East Asian Ancestry| 916 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.06 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013176 | PGS002313 (body_BMIz.BOLT-LMM) |
PSS009697| European Ancestry| 43,328 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.1326 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013225 | PGS002313 (body_BMIz.BOLT-LMM) |
PSS009698| South Asian Ancestry| 7,911 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.082 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013272 | PGS002360 (body_BMIz.BOLT-LMM-BBJ) |
PSS009695| African Ancestry| 6,399 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0005 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013295 | PGS002360 (body_BMIz.BOLT-LMM-BBJ) |
PSS009696| East Asian Ancestry| 916 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0397 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013318 | PGS002360 (body_BMIz.BOLT-LMM-BBJ) |
PSS009697| European Ancestry| 43,328 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0056 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013341 | PGS002360 (body_BMIz.BOLT-LMM-BBJ) |
PSS009698| South Asian Ancestry| 7,911 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0109 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013366 | PGS002385 (body_BMIz.P+T.0.0001) |
PSS009695| African Ancestry| 6,399 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0003 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013415 | PGS002385 (body_BMIz.P+T.0.0001) |
PSS009696| East Asian Ancestry| 916 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0285 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013464 | PGS002385 (body_BMIz.P+T.0.0001) |
PSS009697| European Ancestry| 43,328 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0584 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013513 | PGS002385 (body_BMIz.P+T.0.0001) |
PSS009698| South Asian Ancestry| 7,911 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0341 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013611 | PGS002434 (body_BMIz.P+T.0.001) |
PSS009696| East Asian Ancestry| 916 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0236 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013660 | PGS002434 (body_BMIz.P+T.0.001) |
PSS009697| European Ancestry| 43,328 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0592 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013709 | PGS002434 (body_BMIz.P+T.0.001) |
PSS009698| South Asian Ancestry| 7,911 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0215 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013562 | PGS002434 (body_BMIz.P+T.0.001) |
PSS009695| African Ancestry| 6,399 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0004 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013758 | PGS002483 (body_BMIz.P+T.0.01) |
PSS009695| African Ancestry| 6,399 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0001 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013807 | PGS002483 (body_BMIz.P+T.0.01) |
PSS009696| East Asian Ancestry| 916 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.003 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013856 | PGS002483 (body_BMIz.P+T.0.01) |
PSS009697| European Ancestry| 43,328 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0466 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013905 | PGS002483 (body_BMIz.P+T.0.01) |
PSS009698| South Asian Ancestry| 7,911 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0111 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM013954 | PGS002532 (body_BMIz.P+T.1e-06) |
PSS009695| African Ancestry| 6,399 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0092 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM014003 | PGS002532 (body_BMIz.P+T.1e-06) |
PSS009696| East Asian Ancestry| 916 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0149 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM014052 | PGS002532 (body_BMIz.P+T.1e-06) |
PSS009697| European Ancestry| 43,328 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0401 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM014101 | PGS002532 (body_BMIz.P+T.1e-06) |
PSS009698| South Asian Ancestry| 7,911 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0269 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM014199 | PGS002581 (body_BMIz.P+T.5e-08) |
PSS009696| East Asian Ancestry| 916 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0084 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM014150 | PGS002581 (body_BMIz.P+T.5e-08) |
PSS009695| African Ancestry| 6,399 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0077 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM014248 | PGS002581 (body_BMIz.P+T.5e-08) |
PSS009697| European Ancestry| 43,328 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0323 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM014297 | PGS002581 (body_BMIz.P+T.5e-08) |
PSS009698| South Asian Ancestry| 7,911 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0229 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM014346 | PGS002630 (body_BMIz.PolyFun-pred) |
PSS009695| African Ancestry| 6,399 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0345 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | See body_BMIz.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
PPM014395 | PGS002630 (body_BMIz.PolyFun-pred) |
PSS009696| East Asian Ancestry| 916 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0624 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | See body_BMIz.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
PPM014444 | PGS002630 (body_BMIz.PolyFun-pred) |
PSS009697| European Ancestry| 43,328 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.1307 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | See body_BMIz.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
PPM014493 | PGS002630 (body_BMIz.PolyFun-pred) |
PSS009698| South Asian Ancestry| 7,911 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model when combined with BOLT-LMM vs. covariates alone): 0.0832 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | See body_BMIz.mixweights file at http://data.broadinstitute.org/alkesgroup/polypred_results for combination weights |
PPM014591 | PGS002679 (body_BMIz.SBayesR) |
PSS009696| East Asian Ancestry| 916 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0587 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM014542 | PGS002679 (body_BMIz.SBayesR) |
PSS009695| African Ancestry| 6,399 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0317 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM014640 | PGS002679 (body_BMIz.SBayesR) |
PSS009697| European Ancestry| 43,328 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.1271 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
PPM014689 | PGS002679 (body_BMIz.SBayesR) |
PSS009698| South Asian Ancestry| 7,911 individuals |
PGP000332 | Weissbrod O et al. Nat Genet (2022) |
Reported Trait: BMI | — | — | Incremental R2 (full model vs. covariates alone): 0.0823 | age, sex, age‚age*sex, assessment center, genotyping array, 10 PCs | — |
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 |
---|---|---|---|---|---|---|---|---|
PSS000991 | Cases were individuals defined as obese (BMI ≥ 30kg/m2) | — | [ ,
48.23 % Male samples |
— | European (French-Canadian) |
— | FAS | — |
PSS000992 | Cases were individuals defined as obese (BMI ≥ 30kg/m2) | — | [ ,
43.5 % Male samples |
— | European (French-Canadian) |
— | QFS | — |
PSS008177 | — | — | 6,331 individuals | — | South Asian | India (South Asia) | UKB | — |
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). | — | [
|
— | 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). | — | [
|
— | 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 | — |
PSS009203 | — | — | 4,124 individuals | — | European | Poland (NE Europe) | UKB | — |
PSS008757 | — | — | 6,622 individuals | — | European | Italy (South Europe) | UKB | — |
PSS007744 | — | — | 2,484 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
PSS000037 | — | — | 288,016 individuals, 45.0 % Male samples |
— | European | — | UKB | — |
PSS009295 | — | — | 20,000 individuals | — | European | UK (+ Ireland) | UKB | — |
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 | — |
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 |
PSS008309 | — | — | 6,145 individuals | — | South Asian | India (South Asia) | UKB | — |
PSS008848 | — | — | 3,924 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
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. | — | [
|
— | 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. | — | [
|
— | 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. | — | [
|
— | 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. | — | [
|
— | European | — | HUNT | — |
PSS004911 | — | — | 6,398 individuals | — | African unspecified | — | UKB | — |
PSS004912 | — | — | 1,696 individuals | — | East Asian | — | UKB | — |
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) |
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. | — | [
|
— | 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. | — | [
|
— | 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. | — | [
|
— | 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. | — | [
|
— | 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. | — | [
|
— | 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. | — | [
|
— | 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. | — | [ ,
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. | — | [ ,
0.0 % Male samples |
Mean = 42.6 years Sd = 12.8 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. | — | [ ,
0.0 % Male samples |
Mean = 58.9 years Sd = 11.9 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. | — | [ ,
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. | — | [ ,
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. | — | [ ,
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. | — | [ ,
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. | — | [ ,
100.0 % Male samples |
Mean = 60.2 years Sd = 11.5 years |
European | — | HUNT | number o cases = number of observations (more than 1 pp) |
PSS007873 | — | — | 2,444 individuals | — | African American or Afro-Caribbean | Carribean | UKB | — |
PSS008398 | — | — | 1,200 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
PSS009429 | — | — | 19,937 individuals | — | European | UK (+ Ireland) | UKB | — |
PSS000911 | — | — | 13,989 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) (Qatari) |
— | QBB | — |
PSS008977 | — | — | 3,859 individuals | — | African unspecified | Nigeria (West Africa) | UKB | — |
PSS009695 | — | — | 6,399 individuals | — | African unspecified | — | UKB | — |
PSS009696 | — | — | 916 individuals | — | East Asian | — | UKB | — |
PSS009697 | — | — | 43,328 individuals | — | European | Non-British European | UKB | — |
PSS009698 | — | — | 7,911 individuals | — | South Asian | — | UKB | — |
PSS007963 | — | — | 1,810 individuals | — | East Asian | China (East Asia) | UKB | — |
PSS001425 | — | — | [
|
— | European | — | AGDS | — |
PSS001426 | — | — | [
|
— | European | — | AGDS | — |
PSS001427 | — | — | [
|
— | European | — | AGDS | — |
PSS001428 | — | — | [
|
— | European | — | AGDS | — |
PSS001429 | — | — | [
|
— | European | — | AGDS | — |
PSS001430 | — | — | [
|
— | European | — | AGDS | — |
PSS001431 | — | — | [
|
— | European | — | AGDS | — |
PSS001432 | — | — | [
|
— | European | — | AGDS | — |
PSS001433 | — | — | [
|
— | European | — | AGDS | — |
PSS001434 | — | — | [
|
— | European | — | AGDS | — |
PSS008531 | — | — | 1,178 individuals | — | Greater Middle Eastern (Middle Eastern, North African or Persian) | Iran (Middle East) | UKB | — |
PSS001456 | — | — | 5,179 individuals | — | European (Danish) |
— | Inter99 | Independent subset of Inter99, doesn't overlap with score development samples |
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. |
PSS009069 | — | — | 4,136 individuals | — | European | Poland (NE Europe) | UKB | — |
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 | — | [
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— | European | Swedish | ANDIS | — |
PSS001085 | Moderate Obesity-related Diabetes (MOD) vs. controls | — | [
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— | European | Swedish | ANDIS | — |
PSS001086 | Severe Autoimmune Diabetes (SAID) vs. controls | — | [
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— | European | Swedish | ANDIS | — |
PSS001087 | Severe Insulin-Deficient Diabetes (SIDD) vs. controls | — | [
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— | European | Swedish | ANDIS | — |
PSS001088 | Severe Insulin-Resistant Diabetes (SIRD) vs. controls | — | [
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— | European | Swedish | ANDIS | — |
PSS009565 | Clinically measured BMI | — | 33,511 individuals, 46.9 % Male samples |
Mean = 60.2 years Sd = 16.9 years |
European | — | MGBB | — |
PSS008086 | — | — | 1,799 individuals | — | East Asian | China (East Asia) | UKB | — |
PSS008623 | — | — | 6,660 individuals | — | European | Italy (South Europe) | UKB | — |