Trait: hematologic disease

Experimental Factor Ontology (EFO) Information
Identifier EFO_0005803
Description A disease involving the hematopoietic system.
Trait category
Other disease
Synonyms 21 synonyms
  • Hematologic Diseases
  • blood disease
  • blood disorder
  • blood dyscrasia
  • disease of hematopoietic system
  • disease of the blood and blood-forming organs
  • disease or disorder of hematopoietic system
  • disorder of hematopoietic system
  • haematological system disease
  • haematological system disorder
  • hematologic and lymphocytic disorder
  • hematologic disease
  • hematologic disorder
  • hematological disease
  • hematological disorder
  • hematological system disease
  • hematological system disorder
  • hematopoietic disease
  • hematopoietic system disease
  • hematopoietic system disease or disorder
  • rare hematologic disease
Mapped terms 16 mapped terms
  • DOID:74
  • GTR:AN1320635
  • ICD10:D75
  • ICD10:D75.9
  • ICD9:280-289.99
  • ICD9:289.8
  • ICD9:289.9
  • MESH:D006402
  • MONDO:0005570
  • MeSH:D006402
  • NCIT:C26323
  • Orphanet:97992
  • SCTID:414022008
  • UMLS:C0018939
  • UMLS:CN206939
  • UMLS:CN882913
Child trait(s) 9 child traits

Associated Polygenic Score(s)

Filter PGS by Participant Ancestry
Individuals included in:
G - Source of Variant Associations (GWAS)
D - Score Development/Training
E - PGS Evaluation
List of ancestries includes:
Display options:
Ancestry legend
Multi-ancestry (including European)
Multi-ancestry (excluding European)
African
East Asian
South Asian
Additional Asian Ancestries
European
Greater Middle Eastern
Hispanic or Latin American
Additional Diverse Ancestries
Not Reported
Note: This table shows all PGS for "hematologic disease" and any child terms of this trait in the EFO hierarchy by default.
Polygenic Score ID & Name PGS Publication ID (PGP) Reported Trait Mapped Trait(s) (Ontology) Number of Variants Ancestry distribution Scoring File (FTP Link)
PGS000077
(CC_LL)
PGP000050 |
Graff RE et al. Nat Commun (2021)
Lymphocytic leukemia lymphoid leukemia 75
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000077/ScoringFiles/PGS000077.txt.gz
PGS000080
(CC_NHL)
PGP000050 |
Graff RE et al. Nat Commun (2021)
Non-Hodgkin's lymphoma non-Hodgkins lymphoma 19
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000080/ScoringFiles/PGS000080.txt.gz
PGS000196
(G-PROB_SLE)
PGP000081 |
Knevel R et al. Sci Transl Med (2020)
Systemic lupus eythematosus systemic lupus erythematosus 250
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000196/ScoringFiles/PGS000196.txt.gz
PGS000328
(GRS_SLE)
PGP000099 |
Reid S et al. Ann Rheum Dis (2019)
Systemic lupus erythematosus systemic lupus erythematosus 57
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000328/ScoringFiles/PGS000328.txt.gz
PGS000637
(PRSWEB_PHECODE201_20001-1052_PRS-CS_MGI_20200608)
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Hodgkin's disease Hodgkins lymphoma 1,047,511
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000637/ScoringFiles/PGS000637.txt.gz
PGS000638
(PRSWEB_PHECODE201_GWAS-Catalog-r2019-05-03-X201_P_5e-08_MGI_20200608)
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Hodgkin's disease Hodgkins lymphoma 16
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000638/ScoringFiles/PGS000638.txt.gz
PGS000639
(PRSWEB_PHECODE201_GWAS-Catalog-r2019-05-03-X201_PT_MGI_20200608)
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Hodgkin's disease Hodgkins lymphoma 20
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000639/ScoringFiles/PGS000639.txt.gz
PGS000640
(PRSWEB_PHECODE201_UKBB-SAIGE-HRC-X201_LASSOSUM_MGI_20200608)
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Hodgkin's disease Hodgkins lymphoma 1,119,335
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000640/ScoringFiles/PGS000640.txt.gz
PGS000641
(PRSWEB_PHECODE202.2_GWAS-Catalog-r2019-05-03-X202.2_P_5e-08_UKB_20200608)
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Non-Hodgkins lymphoma non-Hodgkins lymphoma 12
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000641/ScoringFiles/PGS000641.txt.gz
PGS000642
(PRSWEB_PHECODE202.2_GWAS-Catalog-r2019-05-03-X202.2_PT_UKB_20200608)
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Non-Hodgkins lymphoma non-Hodgkins lymphoma 10
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000642/ScoringFiles/PGS000642.txt.gz
PGS000643
(PRSWEB_PHECODE202.21_C-FOLLICULAR-LYMPHOMA_PRS-CS_MGI_20200608)
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Nodular lymphoma follicular lymphoma 1,048,780
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000643/ScoringFiles/PGS000643.txt.gz
PGS000644
(PRSWEB_PHECODE202.21_C-FOLLICULAR-LYMPHOMA_LASSOSUM_MGI_20200608)
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Nodular lymphoma follicular lymphoma 2,209,179
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000644/ScoringFiles/PGS000644.txt.gz
PGS000645
(PRSWEB_PHECODE204.1_C-LYMPHOID-LEUKAEMIA_PT_MGI_20200608)
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Lymphoid leukemia lymphoid leukemia 6
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000645/ScoringFiles/PGS000645.txt.gz
PGS000646
(PRSWEB_PHECODE204.12_GWAS-Catalog-r2019-05-03-X204.12_P_5e-08_MGI_20200608)
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Lymphoid leukemia, chronic chronic lymphocytic leukemia 32
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000646/ScoringFiles/PGS000646.txt.gz
PGS000647
(PRSWEB_PHECODE204.12_GWAS-Catalog-r2019-05-03-X204.12_P_5e-08_UKB_20200608)
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Lymphoid leukemia, chronic chronic lymphocytic leukemia 32
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000647/ScoringFiles/PGS000647.txt.gz
PGS000648
(PRSWEB_PHECODE204.12_GWAS-Catalog-r2019-05-03-X204.12_PT_MGI_20200608)
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Lymphoid leukemia, chronic chronic lymphocytic leukemia 44
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000648/ScoringFiles/PGS000648.txt.gz
PGS000649
(PRSWEB_PHECODE204.12_GWAS-Catalog-r2019-05-03-X204.12_PT_UKB_20200608)
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Lymphoid leukemia, chronic chronic lymphocytic leukemia 27
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000649/ScoringFiles/PGS000649.txt.gz
PGS000650
(PRSWEB_PHECODE204.12_UKBB-SAIGE-HRC-X204.12_PT_MGI_20200608)
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Lymphoid leukemia, chronic chronic lymphocytic leukemia 6
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000650/ScoringFiles/PGS000650.txt.gz
PGS000651
(PRSWEB_PHECODE204.12_UKBB-SAIGE-HRC-X204.12_LASSOSUM_MGI_20200608)
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Lymphoid leukemia, chronic chronic lymphocytic leukemia 76
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000651/ScoringFiles/PGS000651.txt.gz
PGS000652
(PRSWEB_PHECODE204.4_C90_PT_MGI_20200608)
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Multiple myeloma multiple myeloma 27
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000652/ScoringFiles/PGS000652.txt.gz
PGS000653
(PRSWEB_PHECODE204.4_GWAS-Catalog-r2019-05-03-X204.4_P_5e-08_UKB_20200608)
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Multiple myeloma multiple myeloma 22
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000653/ScoringFiles/PGS000653.txt.gz
PGS000654
(PRSWEB_PHECODE204.4_GWAS-Catalog-r2019-05-03-X204.4_PT_UKB_20200608)
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Multiple myeloma multiple myeloma 21
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000654/ScoringFiles/PGS000654.txt.gz
PGS000754
(PRS_SLE)
PGP000160 |
Wang YF et al. Nat Commun (2021)
Systemic lupus erythrmatosus systemic lupus erythematosus 293,684
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000754/ScoringFiles/PGS000754.txt.gz
PGS000771
(GRS95_SLEmain)
PGP000178 |
Chen L et al. Hum Mol Genet (2020)
Systemic lupus erythematosus systemic lupus erythematosus 95
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000771/ScoringFiles/PGS000771.txt.gz
PGS000772
(GRS95_SLEgen)
PGP000178 |
Chen L et al. Hum Mol Genet (2020)
Systemic lupus erythematosus systemic lupus erythematosus 95
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000772/ScoringFiles/PGS000772.txt.gz
PGS000788
(CC_LL_IV)
PGP000186 |
Kachuri L et al. Nat Commun (2020)
Lymphocytic leukemiaaa lymphoid leukemia 75
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000788/ScoringFiles/PGS000788.txt.gz
PGS000791
(CC_NHL_IV)
PGP000186 |
Kachuri L et al. Nat Commun (2020)
Non-Hodgkin's lymphoma non-Hodgkins lymphoma 19
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000791/ScoringFiles/PGS000791.txt.gz
PGS000803
(wGRS41_SLE)
PGP000192 |
Kawai VK et al. Lupus (2021)
Systemic lupus erythematosus systemic lupus erythematosus 41
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000803/ScoringFiles/PGS000803.txt.gz
PGS000874
(PRS41_CLL)
PGP000220 |
Kleinstern G et al. Blood (2018)
Chronic lymphocytic leukemia chronic lymphocytic leukemia 41
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000874/ScoringFiles/PGS000874.txt.gz
PGS001033
(GBE_HC624)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Other coagulation defects (time-to-event) blood coagulation disease 1
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001033/ScoringFiles/PGS001033.txt.gz
PGS001136
(GBE_HC413)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Hematologic disease, genetic hematologic disease 30
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001136/ScoringFiles/PGS001136.txt.gz
PGS001305
(GBE_HC608)
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Vitamin b12 deficiency induced anemia (time-to-event) vitamin B12 deficiency,
anemia
121
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001305/ScoringFiles/PGS001305.txt.gz

Performance Metrics

Disclaimer: The performance metrics are displayed as reported by the source studies. It is important to note that metrics are not necessarily comparable with each other. For example, metrics depend on the sample characteristics (described by the PGS Catalog Sample Set [PSS] ID), phenotyping, and statistical modelling. Please refer to the source publication for additional guidance on performance.

PGS Performance
Metric ID (PPM)
Evaluated Score PGS Sample Set ID
(PSS)
Performance Source Trait PGS Effect Sizes
(per SD change)
Classification Metrics Other Metrics Covariates Included in the Model PGS Performance:
Other Relevant Information
PPM000579 PGS000196
(G-PROB_SLE)
PSS000319|
European Ancestry|
243 individuals
PGP000081 |
Knevel R et al. Sci Transl Med (2020)
Reported Trait: Systemic lupus erythematosus diagnosis in patient with arthritis AUROC: 0.61 [0.27, 0.86] (Setting III: Selecting patients presenting with inflammatory arthritis at their first visit)
PPM000197 PGS000077
(CC_LL)
PSS000116|
European Ancestry|
411,207 individuals
PGP000050 |
Graff RE et al. Nat Commun (2021)
Reported Trait: Lymphocytic leukemia OR: 1.42 [1.33, 1.51] Genotyping reagent kit (GERA cohort only), genotyping array (UK Biobank only), age, sex, 10 PCs. Results from meta-analysis of GERA and UKB
PPM000200 PGS000080
(CC_NHL)
PSS000119|
European Ancestry|
412,765 individuals
PGP000050 |
Graff RE et al. Nat Commun (2021)
Reported Trait: Non-Hodgkin's lymphoma OR: 1.25 [1.2, 1.3] Genotyping reagent kit (GERA cohort only), genotyping array (UK Biobank only), age, sex, 10 PCs. Results from meta-analysis of GERA and UKB
PPM000882 PGS000328
(GRS_SLE)
PSS000438|
European Ancestry|
15,383 individuals
PGP000099 |
Reid S et al. Ann Rheum Dis (2019)
Reported Trait: Systemic lupus erythematosus AUROC: 0.71 Odds Ratio (OR; highest vs. lowest quartile): 7.48 [6.73, 8.32]
PPM000880 PGS000328
(GRS_SLE)
PSS000436|
European Ancestry|
3,803 individuals
PGP000099 |
Reid S et al. Ann Rheum Dis (2019)
Reported Trait: Systemic lupus erythematosus AUROC: 0.78 Odds Ratio (OR; highest vs. lowest quartile): 12.32 [9.53, 15.71]
PPM001323 PGS000638
(PRSWEB_PHECODE201_GWAS-Catalog-r2019-05-03-X201_P_5e-08_MGI_20200608)
PSS000559|
European Ancestry|
775 individuals
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Reported Trait: Hodgkin's disease OR: 1.377 [1.08, 1.755]
β: 0.32 (0.124)
AUROC: 0.601 [0.535, 0.671] Nagelkerke's Pseudo-R²: 0.0193
Brier score: 0.0824
Odds Ratio (OR, top 1% vs. Rest): 1.62 [0.258, 10.1]
age, sex, batch PCs 1-4 Cancer PRSweb PheWAS Results: PRSWEB_PHECODE201_GWAS-Catalog-r2019-05-03-X201_P_5e-08_MGI_20200608
PPM001330 PGS000645
(PRSWEB_PHECODE204.1_C-LYMPHOID-LEUKAEMIA_PT_MGI_20200608)
PSS000562|
European Ancestry|
957 individuals
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Reported Trait: Lymphoid leukemia OR: 1.358 [1.113, 1.657]
β: 0.306 (0.102)
AUROC: 0.578 [0.517, 0.642] Nagelkerke's Pseudo-R²: 0.0193
Brier score: 0.0819
Odds Ratio (OR, top 1% vs. Rest): 3.69 [1.01, 13.4]
age, sex, batch PCs 1-4 Cancer PRSweb PheWAS Results: PRSWEB_PHECODE204.1_C-LYMPHOID-LEUKAEMIA_PT_MGI_20200608
PPM001333 PGS000648
(PRSWEB_PHECODE204.12_GWAS-Catalog-r2019-05-03-X204.12_PT_MGI_20200608)
PSS000561|
European Ancestry|
756 individuals
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Reported Trait: Lymphoid leukemia, chronic OR: 2.124 [1.648, 2.738]
β: 0.753 (0.13)
AUROC: 0.696 [0.621, 0.764] Nagelkerke's Pseudo-R²: 0.102
Brier score: 0.0776
Odds Ratio (OR, top 1% vs. Rest): 12.9 [4.45, 37.6]
age, sex, batch PCs 1-4 Cancer PRSweb PheWAS Results: PRSWEB_PHECODE204.12_GWAS-Catalog-r2019-05-03-X204.12_PT_MGI_20200608
PPM001336 PGS000651
(PRSWEB_PHECODE204.12_UKBB-SAIGE-HRC-X204.12_LASSOSUM_MGI_20200608)
PSS000561|
European Ancestry|
756 individuals
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Reported Trait: Lymphoid leukemia, chronic OR: 1.32 [1.041, 1.675]
β: 0.278 (0.121)
AUROC: 0.573 [0.503, 0.644] Nagelkerke's Pseudo-R²: 0.0145
Brier score: 0.0823
Odds Ratio (OR, top 1% vs. Rest): 4.84 [1.23, 19.0]
age, sex, batch PCs 1-4 Cancer PRSweb PheWAS Results: PRSWEB_PHECODE204.12_UKBB-SAIGE-HRC-X204.12_LASSOSUM_MGI_20200608
PPM001339 PGS000654
(PRSWEB_PHECODE204.4_GWAS-Catalog-r2019-05-03-X204.4_PT_UKB_20200608)
PSS000582|
European Ancestry|
2,738 individuals
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Reported Trait: Multiple myeloma OR: 1.316 [1.156, 1.499]
β: 0.275 (0.0662)
AUROC: 0.576 [0.536, 0.616] Nagelkerke's Pseudo-R²: 0.0137
Brier score: 0.0818
Odds Ratio (OR, top 1% vs. Rest): 2.2 [0.854, 5.66]
age, sex, batch PCs 1-4 Cancer PRSweb PheWAS Results: PRSWEB_PHECODE204.4_GWAS-Catalog-r2019-05-03-X204.4_PT_UKB_20200608
PPM001337 PGS000652
(PRSWEB_PHECODE204.4_C90_PT_MGI_20200608)
PSS000563|
European Ancestry|
908 individuals
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Reported Trait: Multiple myeloma OR: 1.24 [1.005, 1.529]
β: 0.215 (0.107)
AUROC: 0.547 [0.479, 0.613] Nagelkerke's Pseudo-R²: 0.00945
Brier score: 0.0823
Odds Ratio (OR, top 1% vs. Rest): 2.6 [0.593, 11.4]
age, sex, batch PCs 1-4 Cancer PRSweb PheWAS Results: PRSWEB_PHECODE204.4_C90_PT_MGI_20200608
PPM001322 PGS000637
(PRSWEB_PHECODE201_20001-1052_PRS-CS_MGI_20200608)
PSS000559|
European Ancestry|
775 individuals
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Reported Trait: Hodgkin's disease OR: 1.3 [1.023, 1.651]
β: 0.262 (0.122)
AUROC: 0.574 [0.501, 0.642] Nagelkerke's Pseudo-R²: 0.013
Brier score: 0.0826
Odds Ratio (OR, top 1% vs. Rest): 1.67 [0.264, 10.5]
age, sex, batch PCs 1-4 Cancer PRSweb PheWAS Results: PRSWEB_PHECODE201_20001-1052_PRS-CS_MGI_20200608
PPM001324 PGS000639
(PRSWEB_PHECODE201_GWAS-Catalog-r2019-05-03-X201_PT_MGI_20200608)
PSS000559|
European Ancestry|
775 individuals
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Reported Trait: Hodgkin's disease OR: 1.476 [1.154, 1.889]
β: 0.39 (0.126)
AUROC: 0.62 [0.559, 0.688] Nagelkerke's Pseudo-R²: 0.0276
Brier score: 0.0821
Odds Ratio (OR, top 1% vs. Rest): 2.64 [0.572, 12.2]
age, sex, batch PCs 1-4 Cancer PRSweb PheWAS Results: PRSWEB_PHECODE201_GWAS-Catalog-r2019-05-03-X201_PT_MGI_20200608
PPM001325 PGS000640
(PRSWEB_PHECODE201_UKBB-SAIGE-HRC-X201_LASSOSUM_MGI_20200608)
PSS000559|
European Ancestry|
775 individuals
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Reported Trait: Hodgkin's disease OR: 1.292 [1.011, 1.651]
β: 0.256 (0.125)
AUROC: 0.569 [0.501, 0.634] Nagelkerke's Pseudo-R²: 0.0109
Brier score: 0.0828
Odds Ratio (OR, top 1% vs. Rest): 1.64 [0.261, 10.3]
age, sex, batch PCs 1-4 Cancer PRSweb PheWAS Results: PRSWEB_PHECODE201_UKBB-SAIGE-HRC-X201_LASSOSUM_MGI_20200608
PPM001326 PGS000641
(PRSWEB_PHECODE202.2_GWAS-Catalog-r2019-05-03-X202.2_P_5e-08_UKB_20200608)
PSS000580|
European Ancestry|
9,952 individuals
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Reported Trait: Non-Hodgkins lymphoma OR: 1.211 [1.133, 1.294]
β: 0.191 (0.034)
AUROC: 0.541 [0.521, 0.561] Nagelkerke's Pseudo-R²: 0.00682
Brier score: 0.0821
Odds Ratio (OR, top 1% vs. Rest): 2.05 [1.24, 3.4]
age, sex, batch PCs 1-4 Cancer PRSweb PheWAS Results: PRSWEB_PHECODE202.2_GWAS-Catalog-r2019-05-03-X202.2_P_5e-08_UKB_20200608
PPM001328 PGS000643
(PRSWEB_PHECODE202.21_C-FOLLICULAR-LYMPHOMA_PRS-CS_MGI_20200608)
PSS000560|
European Ancestry|
3,256 individuals
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Reported Trait: Nodular lymphoma OR: 1.133 [1.005, 1.277]
β: 0.125 (0.061)
AUROC: 0.532 [0.497, 0.568] Nagelkerke's Pseudo-R²: 0.00282
Brier score: 0.0825
Odds Ratio (OR, top 1% vs. Rest): 2.49 [1.1, 5.65]
age, sex, batch PCs 1-4 Cancer PRSweb PheWAS Results: PRSWEB_PHECODE202.21_C-FOLLICULAR-LYMPHOMA_PRS-CS_MGI_20200608
PPM001329 PGS000644
(PRSWEB_PHECODE202.21_C-FOLLICULAR-LYMPHOMA_LASSOSUM_MGI_20200608)
PSS000560|
European Ancestry|
3,256 individuals
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Reported Trait: Nodular lymphoma OR: 1.149 [1.021, 1.294]
β: 0.139 (0.0606)
AUROC: 0.538 [0.504, 0.573] Nagelkerke's Pseudo-R²: 0.00349
Brier score: 0.0825
Odds Ratio (OR, top 1% vs. Rest): 1.48 [0.538, 4.05]
age, sex, batch PCs 1-4 Cancer PRSweb PheWAS Results: PRSWEB_PHECODE202.21_C-FOLLICULAR-LYMPHOMA_LASSOSUM_MGI_20200608
PPM001331 PGS000646
(PRSWEB_PHECODE204.12_GWAS-Catalog-r2019-05-03-X204.12_P_5e-08_MGI_20200608)
PSS000561|
European Ancestry|
756 individuals
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Reported Trait: Lymphoid leukemia, chronic OR: 2.104 [1.628, 2.718]
β: 0.744 (0.131)
AUROC: 0.696 [0.628, 0.765] Nagelkerke's Pseudo-R²: 0.0973
Brier score: 0.0779
Odds Ratio (OR, top 1% vs. Rest): 11.3 [3.76, 33.9]
age, sex, batch PCs 1-4 Cancer PRSweb PheWAS Results: PRSWEB_PHECODE204.12_GWAS-Catalog-r2019-05-03-X204.12_P_5e-08_MGI_20200608
PPM001332 PGS000647
(PRSWEB_PHECODE204.12_GWAS-Catalog-r2019-05-03-X204.12_P_5e-08_UKB_20200608)
PSS000581|
European Ancestry|
2,758 individuals
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Reported Trait: Lymphoid leukemia, chronic OR: 1.874 [1.639, 2.144]
β: 0.628 (0.0685)
AUROC: 0.675 [0.64, 0.707] Nagelkerke's Pseudo-R²: 0.0689
Brier score: 0.0795
Odds Ratio (OR, top 1% vs. Rest): 4.11 [1.97, 8.6]
age, sex, batch PCs 1-4 Cancer PRSweb PheWAS Results: PRSWEB_PHECODE204.12_GWAS-Catalog-r2019-05-03-X204.12_P_5e-08_UKB_20200608
PPM001334 PGS000649
(PRSWEB_PHECODE204.12_GWAS-Catalog-r2019-05-03-X204.12_PT_UKB_20200608)
PSS000581|
European Ancestry|
2,758 individuals
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Reported Trait: Lymphoid leukemia, chronic OR: 1.85 [1.619, 2.114]
β: 0.615 (0.0681)
AUROC: 0.672 [0.637, 0.703] Nagelkerke's Pseudo-R²: 0.0665
Brier score: 0.0796
Odds Ratio (OR, top 1% vs. Rest): 2.52 [1.04, 6.08]
age, sex, batch PCs 1-4 Cancer PRSweb PheWAS Results: PRSWEB_PHECODE204.12_GWAS-Catalog-r2019-05-03-X204.12_PT_UKB_20200608
PPM001335 PGS000650
(PRSWEB_PHECODE204.12_UKBB-SAIGE-HRC-X204.12_PT_MGI_20200608)
PSS000561|
European Ancestry|
756 individuals
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Reported Trait: Lymphoid leukemia, chronic OR: 1.368 [1.097, 1.705]
β: 0.313 (0.113)
AUROC: 0.577 [0.511, 0.645] Nagelkerke's Pseudo-R²: 0.0205
Brier score: 0.0822
Odds Ratio (OR, top 1% vs. Rest): 2.0 [0.308, 13.0]
age, sex, batch PCs 1-4 Cancer PRSweb PheWAS Results: PRSWEB_PHECODE204.12_UKBB-SAIGE-HRC-X204.12_PT_MGI_20200608
PPM001338 PGS000653
(PRSWEB_PHECODE204.4_GWAS-Catalog-r2019-05-03-X204.4_P_5e-08_UKB_20200608)
PSS000582|
European Ancestry|
2,738 individuals
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Reported Trait: Multiple myeloma OR: 1.327 [1.165, 1.511]
β: 0.283 (0.0663)
AUROC: 0.577 [0.537, 0.617] Nagelkerke's Pseudo-R²: 0.0145
Brier score: 0.0818
Odds Ratio (OR, top 1% vs. Rest): 2.2 [0.855, 5.66]
age, sex, batch PCs 1-4 Cancer PRSweb PheWAS Results: PRSWEB_PHECODE204.4_GWAS-Catalog-r2019-05-03-X204.4_P_5e-08_UKB_20200608
PPM001327 PGS000642
(PRSWEB_PHECODE202.2_GWAS-Catalog-r2019-05-03-X202.2_PT_UKB_20200608)
PSS000580|
European Ancestry|
9,952 individuals
PGP000118 |
Fritsche LG et al. Am J Hum Genet (2020)
Reported Trait: Non-Hodgkins lymphoma OR: 1.239 [1.16, 1.324]
β: 0.214 (0.0337)
AUROC: 0.547 [0.527, 0.566] Nagelkerke's Pseudo-R²: 0.0087
Brier score: 0.082
Odds Ratio (OR, top 1% vs. Rest): 2.05 [1.24, 3.4]
age, sex, batch PCs 1-4 Cancer PRSweb PheWAS Results: PRSWEB_PHECODE202.2_GWAS-Catalog-r2019-05-03-X202.2_PT_UKB_20200608
PPM000883 PGS000328
(GRS_SLE)
PSS000436|
European Ancestry|
3,803 individuals
PGP000099 |
Reid S et al. Ann Rheum Dis (2019)
Reported Trait: Systemic Lupus damage score (SDI) OR: 1.13 [1.03, 1.24] Odds Ratio (OR; highest vs. lowest quartile): 1.47 [1.06, 2.04]
PPM000881 PGS000328
(GRS_SLE)
PSS000437|
European Ancestry|
1,001 individuals
PGP000099 |
Reid S et al. Ann Rheum Dis (2019)
Reported Trait: Systemic lupus erythematosus (onset before age 20) AUROC: 0.83
PPM000885 PGS000328
(GRS_SLE)
PSS000437|
European Ancestry|
1,001 individuals
PGP000099 |
Reid S et al. Ann Rheum Dis (2019)
Reported Trait: Nephritis in systemic lupus erythematosus patients Hazard Ratio (HR; highest vs. lowest quartile): 2.53 [1.72, 3.71]
PPM000884 PGS000328
(GRS_SLE)
PSS000436|
European Ancestry|
3,803 individuals
PGP000099 |
Reid S et al. Ann Rheum Dis (2019)
Reported Trait: Systemic lupus erythematosus (age-at-onset) Hazard Ratio (HR; highest vs. lowest quartile): 1.47 [1.22, 1.75]
PPM002100 PGS000803
(wGRS41_SLE)
PSS001038|
European Ancestry|
47,904 individuals
PGP000192 |
Kawai VK et al. Lupus (2021)
Reported Trait: Lupus (localised and systemic) OR: 1.73 [1.62, 1.85]
β: 0.546 (0.034)
PCs(1-5), median age in the electronic health record, sex
PPM002101 PGS000803
(wGRS41_SLE)
PSS001043|
European Ancestry|
18,722 individuals
PGP000192 |
Kawai VK et al. Lupus (2021)
Reported Trait: Lupus (localised and systemic) OR: 1.82 [1.66, 2.0] PCs(1-5), median age in the electronic health record, sex
PPM002102 PGS000803
(wGRS41_SLE)
PSS001035|
European Ancestry|
47,917 individuals
PGP000192 |
Kawai VK et al. Lupus (2021)
Reported Trait: Systemic lupus erythematosus OR: 1.71 [1.6, 1.82]
β: 0.534 (0.034)
PCs(1-5), median age in the electronic health record, sex
PPM002103 PGS000803
(wGRS41_SLE)
PSS001040|
European Ancestry|
18,698 individuals
PGP000192 |
Kawai VK et al. Lupus (2021)
Reported Trait: Systemic lupus erythematosus OR: 1.86 [1.69, 2.04] PCs(1-5), median age in the electronic health record, sex
PPM002104 PGS000803
(wGRS41_SLE)
PSS001037|
European Ancestry|
50,429 individuals
PGP000192 |
Kawai VK et al. Lupus (2021)
Reported Trait: Erythematous conditions OR: 1.28 [1.22, 1.34]
β: 0.246 (0.024)
PCs(1-5), median age in the electronic health record, sex
PPM002105 PGS000803
(wGRS41_SLE)
PSS001042|
European Ancestry|
21,474 individuals
PGP000192 |
Kawai VK et al. Lupus (2021)
Reported Trait: Erythematous conditions OR: 1.08 [1.04, 1.13] PCs(1-5), median age in the electronic health record, sex
PPM002106 PGS000803
(wGRS41_SLE)
PSS001034|
European Ancestry|
47,321 individuals
PGP000192 |
Kawai VK et al. Lupus (2021)
Reported Trait: Cutaneous lupus erythematosus OR: 1.79 [1.54, 2.08]
β: 0.582 (0.078)
PCs(1-5), median age in the electronic health record, sex
PPM002107 PGS000803
(wGRS41_SLE)
PSS001039|
European Ancestry|
18,422 individuals
PGP000192 |
Kawai VK et al. Lupus (2021)
Reported Trait: Cutaneous lupus erythematosus OR: 2.02 [1.71, 2.4] PCs(1-5), median age in the electronic health record, sex
PPM002108 PGS000803
(wGRS41_SLE)
PSS001036|
European Ancestry|
40,528 individuals
PGP000192 |
Kawai VK et al. Lupus (2021)
Reported Trait: Type 1 diabetes OR: 1.11 [1.06, 1.17]
β: 0.108 (0.024)
PCs(1-5), median age in the electronic health record, sex
PPM002109 PGS000803
(wGRS41_SLE)
PSS001041|
European Ancestry|
19,191 individuals
PGP000192 |
Kawai VK et al. Lupus (2021)
Reported Trait: Type 1 diabetes OR: 1.11 [1.05, 1.18] PCs(1-5), median age in the electronic health record, sex
PPM002110 PGS000803
(wGRS41_SLE)
PSS001036|
European Ancestry|
40,528 individuals
PGP000192 |
Kawai VK et al. Lupus (2021)
Reported Trait: Type 1 diabetes with renal manifestations OR: 1.41 [1.26, 1.59]
β: 0.346 (0.06)
PCs(1-5), median age in the electronic health record, sex
PPM002111 PGS000803
(wGRS41_SLE)
PSS001041|
European Ancestry|
19,191 individuals
PGP000192 |
Kawai VK et al. Lupus (2021)
Reported Trait: Type 1 diabetes with renal manifestations OR: 1.38 [1.19, 1.6] PCs(1-5), median age in the electronic health record, sex
PPM002112 PGS000803
(wGRS41_SLE)
PSS001036|
European Ancestry|
40,528 individuals
PGP000192 |
Kawai VK et al. Lupus (2021)
Reported Trait: Type 1 diabetes with opthalmic manifestations OR: 1.32 [1.16, 1.5]
β: 0.275 (0.065)
PCs(1-5), median age in the electronic health record, sex
PPM002113 PGS000803
(wGRS41_SLE)
PSS001041|
European Ancestry|
19,191 individuals
PGP000192 |
Kawai VK et al. Lupus (2021)
Reported Trait: Type 1 diabetes with opthalmic manifestations OR: 1.34 [1.18, 1.52] PCs(1-5), median age in the electronic health record, sex
PPM002114 PGS000803
(wGRS41_SLE)
PSS001036|
European Ancestry|
40,528 individuals
PGP000192 |
Kawai VK et al. Lupus (2021)
Reported Trait: Type 1 diabetes with neurological manifestations OR: 1.16 [1.06, 1.28]
β: 0.151 (0.047)
PCs(1-5), median age in the electronic health record, sex
PPM000573 PGS000196
(G-PROB_SLE)
PSS000318|
European Ancestry|
245 individuals
PGP000081 |
Knevel R et al. Sci Transl Med (2020)
Reported Trait: Systemic lupus erythematosus diagnosis in patient with arthritis AUROC: 0.79 [0.72, 0.85] (Setting II: Assigning patient diagnoses based on medical records)
PPM000567 PGS000196
(G-PROB_SLE)
PSS000324|
Multi-ancestry (including European)|
1,211 individuals
PGP000081 |
Knevel R et al. Sci Transl Med (2020)
Reported Trait: Systemic lupus erythematosus diagnosis in patient with arthritis AUROC: 0.74 [0.7, 0.78] (Setting I: Assigning patient diagnoses based on billing codes)
PPM001919 PGS000754
(PRS_SLE)
PSS000963|
East Asian Ancestry|
2,589 individuals
PGP000160 |
Wang YF et al. Nat Commun (2021)
Reported Trait: Systemic lupus erythematosus AUROC: 0.76 [0.74, 0.78]
PPM001920 PGS000754
(PRS_SLE)
PSS000960|
European Ancestry|
1,340 individuals
PGP000160 |
Wang YF et al. Nat Commun (2021)
Reported Trait: Systemic lupus erythematosus AUROC: 0.65
PPM001921 PGS000754
(PRS_SLE)
PSS000961|
European Ancestry|
7,733 individuals
PGP000160 |
Wang YF et al. Nat Commun (2021)
Reported Trait: Systemic lupus erythematosus AUROC: 0.65
PPM001922 PGS000754
(PRS_SLE)
PSS000962|
European Ancestry|
1,112 individuals
PGP000160 |
Wang YF et al. Nat Commun (2021)
Reported Trait: Systemic lupus erythematosus AUROC: 0.62
PPM001996 PGS000771
(GRS95_SLEmain)
PSS000994|
European Ancestry|
524 individuals
PGP000178 |
Chen L et al. Hum Mol Genet (2020)
Reported Trait: Renal disease age of onset AUROC: 0.576 [0.518, 0.634] Renal disease is used as a proxy for systemic lupus erythematosus severity
PPM001997 PGS000772
(GRS95_SLEgen)
PSS000993|
European Ancestry|
3,101 individuals
PGP000178 |
Chen L et al. Hum Mol Genet (2020)
Reported Trait: Renal disease Odds Ratio (OR, top 20% vs bottom 20%): 1.578 [1.25, 1.991] Renal disease is used as a proxy for systemic lupus erythematosus severity
PPM001998 PGS000771
(GRS95_SLEmain)
PSS000994|
European Ancestry|
524 individuals
PGP000178 |
Chen L et al. Hum Mol Genet (2020)
Reported Trait: Renal disease age of onset Odds Ratio (OR, top 20% vs bottom 20%): 3.155 [1.623, 6.133] Renal disease is used as a proxy for systemic lupus erythematosus severity
PPM002043 PGS000077
(CC_LL)
PSS001016|
European Ancestry|
391,338 individuals
PGP000186 |
Kachuri L et al. Nat Commun (2020)
|Ext.
Reported Trait: Incident Lymphocytic Leukemia HR: 1.45 [1.31, 1.61] AUROC: 0.719
C-index: 0.735 (0.016)
Age at assessment, sex, genotyping array, PCs(1-15) C-index calculated as a weighted average between 1 and 5 years and AUC at 5 years.
PPM002046 PGS000080
(CC_NHL)
PSS001019|
European Ancestry|
391,968 individuals
PGP000186 |
Kachuri L et al. Nat Commun (2020)
|Ext.
Reported Trait: Incident non-hodgkin's lymphoma HR: 1.16 [1.09, 1.24] AUROC: 0.677
C-index: 0.676 (0.01)
Age at assessment, sex, genotyping array, PCs(1-15) C-index calculated as a weighted average between 1 and 5 years and AUC at 5 years.
PPM002059 PGS000788
(CC_LL_IV)
PSS001016|
European Ancestry|
391,338 individuals
PGP000186 |
Kachuri L et al. Nat Commun (2020)
Reported Trait: Incident Lymphocytic Leukemia HR: 1.7 [1.53, 1.88] AUROC: 0.738
C-index: 0.756 (0.015)
: 0.415 Age at assessment, sex, genotyping array, PCs(1-15) C-index calculated as a weighted average between 1 and 5 years and AUC at 5 years.
PPM002062 PGS000791
(CC_NHL_IV)
PSS001019|
European Ancestry|
391,968 individuals
PGP000186 |
Kachuri L et al. Nat Commun (2020)
Reported Trait: Incident non-hodgkin's lymphoma HR: 1.15 [1.08, 1.22] AUROC: 0.677
C-index: 0.674 (0.01)
: 0.227 Age at assessment, sex, genotyping array, PCs(1-15) C-index calculated as a weighted average between 1 and 5 years and AUC at 5 years.
PPM002076 PGS000754
(PRS_SLE)
PSS001027|
Additional Asian Ancestries|
3,996 individuals
PGP000188 |
Tangtanatakul P et al. Arthritis Res Ther (2020)
|Ext.
Reported Trait: Systemic lupus erythematosus AUROC: 0.76
PPM002493 PGS000874
(PRS41_CLL)
PSS001123|
Multi-ancestry (including European)|
3,958 individuals
PGP000220 |
Kleinstern G et al. Blood (2018)
Reported Trait: Chronic lymphocytic leukemia OR: 2.49 [2.28, 2.8] C-index: 0.79 [0.78, 0.8] Age, sex, study, socioeconomic status (when available) Odds Ratio (OR, top 20% vs middle 20%) = 3.64 [2.94 - 4.51]
PPM002494 PGS000874
(PRS41_CLL)
PSS001123|
Multi-ancestry (including European)|
3,958 individuals
PGP000220 |
Kleinstern G et al. Blood (2018)
Reported Trait: Chronic lymphocytic leukemia in individuals with no family history of hematological cancers OR: 2.46 [2.19, 2.76] C-index: 0.791 [0.77, 0.81] Age, sex, study, socioeconomic status (when available) Odds Ratio (OR, top 20% vs middle 20%) = 3.29 [2.49 - 4.35]
PPM002495 PGS000874
(PRS41_CLL)
PSS001123|
Multi-ancestry (including European)|
3,958 individuals
PGP000220 |
Kleinstern G et al. Blood (2018)
Reported Trait: Chronic lymphocytic leukemia in individuals with a family history of hematological cancers OR: 3.79 [2.44, 5.87] C-index: 0.861 [0.82, 0.9] Age, sex, study, socioeconomic status (when available) Odds Ratio (OR, top 20% vs middle 20%) = 7.58 [2.74 - 21.0]
PPM002496 PGS000874
(PRS41_CLL)
PSS001121|
Ancestry Not Reported|
218 individuals
PGP000220 |
Kleinstern G et al. Blood (2018)
Reported Trait: Chronic lymphocytic leukemia OR: 2.44 [1.65, 3.62] C-index: 0.798 [0.74, 0.85] Age, sex, study, socioeconomic status (when available) Odds Ratio (OR, top 20% vs middle 20%) = 3.51 [1.39 - 8.86]
PPM002497 PGS000874
(PRS41_CLL)
PSS001122|
Ancestry Not Reported|
153 individuals
PGP000220 |
Kleinstern G et al. Blood (2018)
Reported Trait: Monoclonal B-cell lymphocytosis OR: 2.3 [1.44, 3.67] C-index: 0.773 [0.7, 0.85] Age, sex, study, socioeconomic status (when available) Odds Ratio (OR, top 20% vs middle 20%) = 4.36 [1.45 - 13.1]
PPM002498 PGS000874
(PRS41_CLL)
PSS001119|
Ancestry Not Reported|
1,468 individuals
PGP000220 |
Kleinstern G et al. Blood (2018)
Reported Trait: Chronic lymphocytic leukemia OR: 3.02 [2.49, 3.65] C-index: 0.779 [0.74, 0.81] Age, sex Odds Ratio (OR, top 20% vs middle 20%) = 4.47 [2.76 - 7.24]
PPM002499 PGS000874
(PRS41_CLL)
PSS001120|
Ancestry Not Reported|
1,362 individuals
PGP000220 |
Kleinstern G et al. Blood (2018)
Reported Trait: Monoclonal B-cell lymphocytosis OR: 2.81 [2.18, 3.61] C-index: 0.774 [0.73, 0.82] Age, sex Odds Ratio (OR, top 20% vs middle 20%) = 4.34 [2.21 - 8.50]
PPM002654 PGS000874
(PRS41_CLL)
PSS001173|
European Ancestry|
3,191 individuals
PGP000234 |
Kleinstern G et al. Leukemia (2021)
|Ext.
Reported Trait: Monoclonal B-cell lymphocytosis OR: 1.86 [1.67, 2.07] C-index: 0.72 [0.69, 0.73] Odds Ratio (OR, top 20% vs middle 20%): 2.38 [1.81, 3.13] Age, sex
PPM002655 PGS000874
(PRS41_CLL)
PSS001173|
European Ancestry|
3,191 individuals
PGP000234 |
Kleinstern G et al. Leukemia (2021)
|Ext.
Reported Trait: Monoclonal B-cell lymphocytosis OR: 1.15 [1.13, 1.18] C-index: 0.72 [0.7, 0.74] Age, sex An unweighted version of PRS41_CLL was used.
PPM002656 PGS000874
(PRS41_CLL)
PSS001173|
European Ancestry|
3,191 individuals
PGP000234 |
Kleinstern G et al. Leukemia (2021)
|Ext.
Reported Trait: Low-count monoclonal B-cell lymphocytosis OR: 1.75 [1.55, 1.98] C-index: 0.72 [0.7, 0.75] Odds Ratio (OR, top 20% vs middle 20%): 2.1 [1.53, 2.88] Age, sex
PPM002657 PGS000874
(PRS41_CLL)
PSS001173|
European Ancestry|
3,191 individuals
PGP000234 |
Kleinstern G et al. Leukemia (2021)
|Ext.
Reported Trait: Low-count monoclonal B-cell lymphocytosis OR: 1.14 [1.11, 1.17] C-index: 0.72 [0.7, 0.75] Age, sex An unweighted version of PRS41_CLL was used.
PPM002658 PGS000874
(PRS41_CLL)
PSS001173|
European Ancestry|
3,191 individuals
PGP000234 |
Kleinstern G et al. Leukemia (2021)
|Ext.
Reported Trait: High-count monoclonal B-cell lymphocytosis OR: 2.14 [1.8, 2.56] C-index: 0.73 [0.69, 0.77] Odds Ratio (OR, top 20% vs middle 20%): 3.13 [1.97, 4.98] Age, sex
PPM002659 PGS000874
(PRS41_CLL)
PSS001173|
European Ancestry|
3,191 individuals
PGP000234 |
Kleinstern G et al. Leukemia (2021)
|Ext.
Reported Trait: High-count monoclonal B-cell lymphocytosis OR: 1.19 [1.14, 1.23] C-index: 0.725 [0.69, 0.77] Age, sex An unweighted version of PRS41_CLL was used.
PPM002660 PGS000874
(PRS41_CLL)
PSS001172|
European Ancestry|
3,327 individuals
PGP000234 |
Kleinstern G et al. Leukemia (2021)
|Ext.
Reported Trait: Chronic lymphocytic leukemia OR: 2.53 [2.27, 2.81] C-index: 0.77 [0.75, 0.79] Odds Ratio (OR, top 20% vs middle 20%): 3.49 [2.70, 4.51] Age, sex
PPM002661 PGS000874
(PRS41_CLL)
PSS001172|
European Ancestry|
3,327 individuals
PGP000234 |
Kleinstern G et al. Leukemia (2021)
|Ext.
Reported Trait: Chronic lymphocytic leukemia OR: 1.23 [1.2, 1.26] C-index: 0.775 [0.76, 0.79] Age, sex An unweighted version of PRS41_CLL was used.
PPM002662 PGS000874
(PRS41_CLL)
PSS001171|
African Ancestry|
408 individuals
PGP000234 |
Kleinstern G et al. Leukemia (2021)
|Ext.
Reported Trait: Chronic lymphocytic leukemia OR: 1.76 [1.34, 2.31] C-index: 0.62 [0.57, 0.68] Age, sex
PPM002663 PGS000874
(PRS41_CLL)
PSS001171|
African Ancestry|
408 individuals
PGP000234 |
Kleinstern G et al. Leukemia (2021)
|Ext.
Reported Trait: Chronic lymphocytic leukemia OR: 1.07 [1.01, 1.13] C-index: 0.57 [0.53, 0.64] Age, sex An unweighted version of PRS41_CLL was used.
PPM007928 PGS001033
(GBE_HC624)
PSS004546|
African Ancestry|
6,497 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: TTE other coagulation defects AUROC: 0.68816 [0.60114, 0.77518] : 0.03983
Incremental AUROC (full-covars): -0.00134
PGS R2 (no covariates): 0.00067
PGS AUROC (no covariates): 0.49799 [0.49722, 0.49876]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM007929 PGS001033
(GBE_HC624)
PSS004547|
East Asian Ancestry|
1,704 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: TTE other coagulation defects AUROC: 0.8546 [0.65205, 1.0] : 0.119
Incremental AUROC (full-covars): 0.0
PGS R2 (no covariates): 8e-05
PGS AUROC (no covariates): 0.49971 [0.49913, 0.50028]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM007930 PGS001033
(GBE_HC624)
PSS004548|
European Ancestry|
24,905 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: TTE other coagulation defects AUROC: 0.64742 [0.59655, 0.69828] : 0.03199
Incremental AUROC (full-covars): 0.06677
PGS R2 (no covariates): 0.0303
PGS AUROC (no covariates): 0.58367 [0.54735, 0.61999]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM007931 PGS001033
(GBE_HC624)
PSS004549|
South Asian Ancestry|
7,831 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: TTE other coagulation defects AUROC: 0.66881 [0.55798, 0.77963] : 0.02584
Incremental AUROC (full-covars): 0.02341
PGS R2 (no covariates): 0.00994
PGS AUROC (no covariates): 0.54143 [0.46669, 0.61618]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM007932 PGS001033
(GBE_HC624)
PSS004550|
European Ancestry|
67,425 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: TTE other coagulation defects AUROC: 0.65623 [0.61832, 0.69414] : 0.05179
Incremental AUROC (full-covars): 0.12049
PGS R2 (no covariates): 0.05781
PGS AUROC (no covariates): 0.63402 [0.60616, 0.66189]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM008408 PGS001136
(GBE_HC413)
PSS004481|
African Ancestry|
6,497 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Genetic haematological disorder AUROC: 0.70006 [0.42128, 0.97885] : 0.03928
Incremental AUROC (full-covars): -0.02126
PGS R2 (no covariates): 0.01794
PGS AUROC (no covariates): 0.33706 [0.08609, 0.58803]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM008409 PGS001136
(GBE_HC413)
PSS004483|
European Ancestry|
24,905 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Genetic haematological disorder AUROC: 0.74771 [0.64556, 0.84987] : 0.04759
Incremental AUROC (full-covars): 0.0501
PGS R2 (no covariates): 0.01517
PGS AUROC (no covariates): 0.6101 [0.48056, 0.73964]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM008410 PGS001136
(GBE_HC413)
PSS004484|
South Asian Ancestry|
7,831 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Genetic haematological disorder AUROC: 0.86122 [0.58992, 1.0] : 0.17517
Incremental AUROC (full-covars): -0.03046
PGS R2 (no covariates): 0.0343
PGS AUROC (no covariates): 0.45357 [0.0, 1.0]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM008411 PGS001136
(GBE_HC413)
PSS004485|
European Ancestry|
67,425 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: Genetic haematological disorder AUROC: 0.67815 [0.59919, 0.75712] : 0.04564
Incremental AUROC (full-covars): 0.21126
PGS R2 (no covariates): 0.0569
PGS AUROC (no covariates): 0.69867 [0.61574, 0.78161]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM009020 PGS001305
(GBE_HC608)
PSS004536|
African Ancestry|
6,497 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: TTE vitamin b12 deficiency anaemia AUROC: 0.66063 [0.54547, 0.77579] : 0.02699
Incremental AUROC (full-covars): 0.01762
PGS R2 (no covariates): 0.00536
PGS AUROC (no covariates): 0.5839 [0.43974, 0.72805]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM009021 PGS001305
(GBE_HC608)
PSS004538|
European Ancestry|
24,905 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: TTE vitamin b12 deficiency anaemia AUROC: 0.65118 [0.60827, 0.69409] : 0.02729
Incremental AUROC (full-covars): 0.02555
PGS R2 (no covariates): 0.00865
PGS AUROC (no covariates): 0.59612 [0.5552, 0.63704]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM009022 PGS001305
(GBE_HC608)
PSS004539|
South Asian Ancestry|
7,831 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: TTE vitamin b12 deficiency anaemia AUROC: 0.72387 [0.6834, 0.76434] : 0.06747
Incremental AUROC (full-covars): 0.01252
PGS R2 (no covariates): 0.00966
PGS AUROC (no covariates): 0.57469 [0.52685, 0.62252]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method
PPM009023 PGS001305
(GBE_HC608)
PSS004540|
European Ancestry|
67,425 individuals
PGP000244 |
Tanigawa Y et al. medRxiv (2021)
|Pre
Reported Trait: TTE vitamin b12 deficiency anaemia AUROC: 0.64482 [0.61972, 0.66993] : 0.02374
Incremental AUROC (full-covars): 0.02212
PGS R2 (no covariates): 0.0068
PGS AUROC (no covariates): 0.57972 [0.55331, 0.60612]
age, sex, UKB array type, Genotype PCs Full Model & PGS R2 is estimated using Nagelkerke's method

Evaluated Samples

PGS Sample Set ID
(PSS)
Phenotype Definitions and Methods Participant Follow-up Time Sample Numbers Age of Study Participants Sample Ancestry Additional Ancestry Description Cohort(s) Additional Sample/Cohort Information
PSS004540
[
  • 447 cases
  • , 66,978 controls
]
European white British ancestry UKB Testing cohort (heldout set)
PSS001027 Cases were individuals with systemic lupus erythematosus (SLE). All cases were carefully recruited regarding the criteria from the American College of Rheumatology (ACR). Controls included healthy individuals and individuals who had unrelated diseases including: breast cancer, periodontitis, tuberculosis, drug-induced liver injury, epileptic encephalopathy, dengue hemorrhagic fever, thalassemia, and cardiomyopathy.
[
  • 826 cases
  • , 3,170 controls
]
,
40.31 % Male samples
South East Asian
(Thai)
NR Cases were recruited from King Chulalongkorn Memorial Hospital and the Rheumatology clinic at Ramathbodi hospital. Control data was provided by the Department of Medical Science, Min- istry of Public Health, Thailand.
PSS000559 PheCode:201; ICD9CM:201.00, 201.01, 201.02, 201.03, 201.04, 201.05, 201.06, 201.07, 201.08, 201.10, 201.11, 201.12, 201.13, 201.14, 201.15, 201.16, 201.17, 201.18, 201.20, 201.21, 201.22, 201.23, 201.24, 201.25, 201.26, 201.27, 201.28, 201.40, 201.41, 201.42, 201.43, 201.44, 201.45, 201.46, 201.47, 201.48, 201.50, 201.51, 201.52, 201.53, 201.54, 201.55, 201.56, 201.57, 201.58, 201.60, 201.61, 201.62, 201.63, 201.64, 201.65, 201.66, 201.67, 201.68, 201.70, 201.71, 201.72, 201.73, 201.74, 201.75, 201.76, 201.77, 201.78, 201.90, 201.91, 201.92, 201.93, 201.94, 201.95, 201.96, 201.97, 201.98, V10.72; ICD10CM:C81, C81.0, C81.00, C81.01, C81.02, C81.03, C81.04, C81.05, C81.06, C81.07, C81.08, C81.09, C81.1, C81.10, C81.11, C81.12, C81.13, C81.14, C81.15, C81.16, C81.17, C81.18, C81.19, C81.2, C81.20, C81.21, C81.22, C81.23, C81.24, C81.25, C81.26, C81.27, C81.28, C81.29, C81.3, C81.30, C81.31, C81.32, C81.33, C81.34, C81.35, C81.36, C81.37, C81.38, C81.39, C81.4, C81.40, C81.41, C81.42, C81.43, C81.44, C81.45, C81.46, C81.47, C81.48, C81.49, C81.7, C81.70, C81.71, C81.72, C81.73, C81.74, C81.75, C81.76, C81.77, C81.78, C81.79, C81.9, C81.90, C81.91, C81.92, C81.93, C81.94, C81.95, C81.96, C81.97, C81.98, C81.99
[
  • 71 cases
  • , 704 controls
]
European MGI
PSS000560 PheCode:202.21; ICD9CM:202.00, 202.01, 202.02, 202.03, 202.04, 202.05, 202.06, 202.07, 202.08; ICD10CM:C82, C82.0, C82.00, C82.01, C82.02, C82.03, C82.04, C82.05, C82.06, C82.07, C82.08, C82.09, C82.1, C82.10, C82.11, C82.12, C82.13, C82.14, C82.15, C82.16, C82.17, C82.18, C82.19, C82.2, C82.20, C82.21, C82.22, C82.23, C82.24, C82.25, C82.26, C82.27, C82.28, C82.29, C82.3, C82.30, C82.31, C82.32, C82.33, C82.34, C82.35, C82.36, C82.37, C82.38, C82.39, C82.4, C82.40, C82.41, C82.42, C82.43, C82.44, C82.45, C82.46, C82.47, C82.48, C82.49, C82.5, C82.50, C82.51, C82.52, C82.53, C82.54, C82.55, C82.56, C82.57, C82.58, C82.59, C82.6, C82.60, C82.61, C82.62, C82.63, C82.64, C82.65, C82.66, C82.67, C82.68, C82.69, C82.8, C82.80, C82.81, C82.82, C82.83, C82.84, C82.85, C82.86, C82.87, C82.88, C82.89, C82.9, C82.90, C82.91, C82.92, C82.93, C82.94, C82.95, C82.96, C82.97, C82.98, C82.99
[
  • 296 cases
  • , 2,960 controls
]
European MGI
PSS000561 PheCode:204.12; ICD9CM:204.10, 204.11, 204.12; ICD10CM:C91.1, C91.10, C91.11, C91.12
[
  • 69 cases
  • , 687 controls
]
European MGI
PSS000562 PheCode:204.1; ICD9CM:204.00, 204.01, 204.02, 204.10, 204.11, 204.12, 204.20, 204.21, 204.22, 204.80, 204.81, 204.82, 204.90, 204.91, 204.92, V10.61; ICD10CM:C91, C91.0, C91.00, C91.01, C91.02, C91.1, C91.10, C91.11, C91.12, C91.3, C91.30, C91.31, C91.32, C91.4, C91.40, C91.41, C91.42, C91.5, C91.50, C91.51, C91.52, C91.6, C91.60, C91.61, C91.62, C91.9, C91.90, C91.91, C91.92, C91.A, C91.A0, C91.A1, C91.A2, C91.Z, C91.Z0, C91.Z1, C91.Z2
[
  • 87 cases
  • , 870 controls
]
European MGI
PSS000563 PheCode:204.4; ICD9CM:203.00, 203.01, 203.02, 203.80, 203.81, 203.82; ICD10CM:C88.2, C88.3, C88.9, C90.0, C90.00, C90.01, C90.02, C90.2, C90.20, C90.21, C90.22, C90.30, C90.31, C90.32
[
  • 83 cases
  • , 825 controls
]
European MGI
PSS004546
[
  • 35 cases
  • , 6,462 controls
]
African unspecified UKB
PSS004547
[
  • 3 cases
  • , 1,701 controls
]
East Asian UKB
PSS004548
[
  • 123 cases
  • , 24,782 controls
]
European non-white British ancestry UKB
PSS004549
[
  • 18 cases
  • , 7,813 controls
]
South Asian UKB
PSS004550
[
  • 268 cases
  • , 67,157 controls
]
European white British ancestry UKB Testing cohort (heldout set)
PSS000318 Setting II: Based on ICD codes and review of medical records from Partners HealthCare Biobank; controls = other non-matching arthritis diseases Median = 8.0 years
[
  • 62 cases
  • , 183 controls
]
,
32.0 % Male samples
European PHB
PSS004536
[
  • 19 cases
  • , 6,478 controls
]
African unspecified UKB
PSS000580 PheCode:202.2; ICD9:200, 200.2, 200.8, 202.1, 202.2, 202.8, 202.9; ICD10:B21.1, C82.0, C82.1, C82.2, C82.7, C82.9, C83.0, C83.1, C83.2, C83.3, C83.4, C83.5, C83.6, C83.7, C83.8, C83.9, C84.0, C84.1, C84.2, C84.3, C84.4, C84.5, C85.0, C85.1, C85.7, C85.9, C96.7, C96.9, L41.2
[
  • 901 cases
  • , 9,051 controls
]
European UKB
PSS000581 PheCode:204.12; ICD9:204.1; ICD10:C91.1
[
  • 249 cases
  • , 2,509 controls
]
European UKB
PSS000582 PheCode:204.4; ICD9:203, 203.0, 203.8; ICD10:C88.1, C88.3, C88.9, C90.0, C90.2
[
  • 248 cases
  • , 2,490 controls
]
European UKB
PSS000960 Cases were individuals with systemic lupus erythematosus.
[
  • 910 cases
  • , 430 controls
]
European NR
PSS000961 Cases were individuals with systemic lupus erythematosus.
[
  • 2,354 cases
  • , 5,379 controls
]
European NR
PSS000962 Cases were individuals with systemic lupus erythematosus.
[
  • 406 cases
  • , 706 controls
]
European NR
PSS000963 Cases were individuals with systemic lupus erythematosus.
[
  • 1,604 cases
  • , 985 controls
]
East Asian
(Han Chinese)
NR
PSS004539
[
  • 138 cases
  • , 7,693 controls
]
South Asian UKB
PSS001034 Cases were individuals with cutaneous lupus erythematosus. Cases were identified by extracting clinical diagnoses from the electronic health record using the 9th and 10th International Statistical Classification of Diseases and Related Health Problems (ICD) Clinical Modification (CM) codes that mapped to the phenotype and transformed these ICD9/ICD10 codes into phecodes, which aggregate one or more related ICD codes into distinct diseases or traits. For each phenotype, cases were defined as individuals with 2 or more instances of the specific phecode in the electronic health record. Phecodes for lupus related disorders (systemic and cutaneous) include: 695.4, 695.41, 696.42.
[
  • 161 cases
  • , 47,160 controls
]
European BioVu
PSS001035 Cases were individuals with systemic lupus erythematosus. Cases were identified by extracting clinical diagnoses from the electronic health record using the 9th and 10th International Statistical Classification of Diseases and Related Health Problems (ICD) Clinical Modification (CM) codes that mapped to the phenotype and transformed these ICD9/ICD10 codes into phecodes, which aggregate one or more related ICD codes into distinct diseases or traits. For each phenotype, cases were defined as individuals with 2 or more instances of the specific phecode in the electronic health record. Phecodes for lupus related disorders (systemic and cutaneous) include: 695.4, 695.41, 696.42.
[
  • 880 cases
  • , 47,037 controls
]
European BioVu
PSS000319 Setting III: Based on ICD codes and final diagnosis in medical records from Partners HealthCare Biobank; controls = other non-matching arthritis diseases Median = 7.0 years
[
  • 7 cases
  • , 236 controls
]
,
32.0 % Male samples
European PHB
PSS001037 Cases were individuals with erythematosus conditions. Cases were identified by extracting clinical diagnoses from the electronic health record using the 9th and 10th International Statistical Classification of Diseases and Related Health Problems (ICD) Clinical Modification (CM) codes that mapped to the phenotype and transformed these ICD9/ICD10 codes into phecodes, which aggregate one or more related ICD codes into distinct diseases or traits. For each phenotype, cases were defined as individuals with 2 or more instances of the specific phecode in the electronic health record. Phecodes for lupus related disorders (systemic and cutaneous) include: 695.4, 695.41, 696.42.
[
  • 1,916 cases
  • , 48,513 controls
]
European BioVu
PSS001038 Cases were individuals with lupus (localised and systemic). Cases were identified by extracting clinical diagnoses from the electronic health record using the 9th and 10th International Statistical Classification of Diseases and Related Health Problems (ICD) Clinical Modification (CM) codes that mapped to the phenotype and transformed these ICD9/ICD10 codes into phecodes, which aggregate one or more related ICD codes into distinct diseases or traits. For each phenotype, cases were defined as individuals with 2 or more instances of the specific phecode in the electronic health record. Phecodes for lupus related disorders (systemic and cutaneous) include: 695.4, 695.41, 696.42.
[
  • 867 cases
  • , 47,037 controls
]
European BioVu
PSS001039 Cases were individuals with cutaneous lupus erythematosus. Cases were identified by extracting clinical diagnoses from the electronic health record using the 9th and 10th International Statistical Classification of Diseases and Related Health Problems (ICD) Clinical Modification (CM) codes that mapped to the phenotype and transformed these ICD9/ICD10 codes into phecodes, which aggregate one or more related ICD codes into distinct diseases or traits. For each phenotype, cases were defined as individuals with 2 or more instances of the specific phecode in the electronic health record. Phecodes for lupus related disorders (systemic and cutaneous) include: 695.4, 695.41, 696.42.
[
  • 120 cases
  • , 18,302 controls
]
European eMERGE
PSS001040 Cases were individuals with systemic lupus erythematosus. Cases were identified by extracting clinical diagnoses from the electronic health record using the 9th and 10th International Statistical Classification of Diseases and Related Health Problems (ICD) Clinical Modification (CM) codes that mapped to the phenotype and transformed these ICD9/ICD10 codes into phecodes, which aggregate one or more related ICD codes into distinct diseases or traits. For each phenotype, cases were defined as individuals with 2 or more instances of the specific phecode in the electronic health record. Phecodes for lupus related disorders (systemic and cutaneous) include: 695.4, 695.41, 696.42.
[
  • 393 cases
  • , 18,305 controls
]
European eMERGE
PSS001036 Cases were individuals with type 1 diabetes. Cases were identified by extracting clinical diagnoses from the electronic health record using the 9th and 10th International Statistical Classification of Diseases and Related Health Problems (ICD) Clinical Modification (CM) codes that mapped to the phenotype and transformed these ICD9/ICD10 codes into phecodes, which aggregate one or more related ICD codes into distinct diseases or traits. For each phenotype, cases were defined as individuals with 2 or more instances of the specific phecode in the electronic health record. Phecodes for type 1 diabetes include: 250.1. Of all the type 1 diabetes cases, 276 had renal manifestations, 240 had ophthalmic manifestations and 475 had neurological manifestations
[
  • 1,881 cases
  • , 38,647 controls
]
European BioVu
PSS001042 Cases were individuals with erythematosus conditions. Cases were identified by extracting clinical diagnoses from the electronic health record using the 9th and 10th International Statistical Classification of Diseases and Related Health Problems (ICD) Clinical Modification (CM) codes that mapped to the phenotype and transformed these ICD9/ICD10 codes into phecodes, which aggregate one or more related ICD codes into distinct diseases or traits. For each phenotype, cases were defined as individuals with 2 or more instances of the specific phecode in the electronic health record. Phecodes for lupus related disorders (systemic and cutaneous) include: 695.4, 695.41, 696.42.
[
  • 3,029 cases
  • , 18,445 controls
]
European eMERGE
PSS001043 Cases were individuals with lupus (localised and systemic). Cases were identified by extracting clinical diagnoses from the electronic health record using the 9th and 10th International Statistical Classification of Diseases and Related Health Problems (ICD) Clinical Modification (CM) codes that mapped to the phenotype and transformed these ICD9/ICD10 codes into phecodes, which aggregate one or more related ICD codes into distinct diseases or traits.For each phenotype, cases were defined as individuals with 2 or more instances of the specific phecode in the electronic health record.Phecodes for lupus related disorders (systemic and cutaneous) include: 695.4, 695.41, 696.42.
[
  • 418 cases
  • , 18,304 controls
]
European eMERGE
PSS001041 Cases were individuals with type 1 diabetes. Cases were identified by extracting clinical diagnoses from the electronic health record using the 9th and 10th International Statistical Classification of Diseases and Related Health Problems (ICD) Clinical Modification (CM) codes that mapped to the phenotype and transformed these ICD9/ICD10 codes into phecodes, which aggregate one or more related ICD codes into distinct diseases or traits. For each phenotype, cases were defined as individuals with 2 or more instances of the specific phecode in the electronic health record. Phecodes for type 1 diabetes include: 250.1. Of the type 1 diabetes cases 165 had renal manifestations, 230 had ophthalmic manifestations and 218 had neurological manifestations.
[
  • 1,156 cases
  • , 18,035 controls
]
European eMERGE
PSS000324 Setting I: Based on ICD codes and expert opinion (ACR2010 criteria), in eMERGE network EMR database from Stanaway 2018; controls = other non-matching arthritis diseases Median = 16.0 years
[
  • 133 cases
  • , 1,078 controls
]
,
43.0 % Male samples
European, African unspecified, Asian unspecified, NR Primarily European, African and Asian ancestry eMERGE
PSS004481
[
  • 5 cases
  • , 6,492 controls
]
African unspecified UKB
PSS004483
[
  • 20 cases
  • , 24,885 controls
]
European non-white British ancestry UKB
PSS004484
[
  • 2 cases
  • , 7,829 controls
]
South Asian UKB
PSS004485
[
  • 59 cases
  • , 67,366 controls
]
European white British ancestry UKB Testing cohort (heldout set)
PSS000993 All individuals had systemic lupus erythematosus. Cases are individuals that also have renal disease
[
  • 1,152 cases
  • , 1,949 controls
]
European NR
PSS000994 All individuals had systemic lupus erythematosus. Cases are individuals that also have renal disease
[
  • 146 cases
  • , 378 controls
]
European NR Cases and controls obtained by SLEGEN.
PSS001171 Cases were individuals with chronic lymphocytic leukemia (CLL). CLL diagnoses were made based on the 1996 NCI working group criteria and updated to the 2008 International Workshop CLL criteria wherever possible.
[
  • 173 cases
  • , 235 controls
]
,
66.91 % Male samples
African American or Afro-Caribbean MAYO Possibly significant sample overlap between this dataset and the dataset used to source PRS41_CLL. Additional cases were obtained from Duke University and Cornell
PSS001172 Cases were individuals with chronic lymphocytic leukemia (CLL). CLL diagnoses were made based on the 1996 NCI working group criteria and updated to the 2008 International Workshop CLL criteria wherever possible.
[
  • 696 cases
  • , 2,631 controls
]
,
45.36 % Male samples
European MAYO Possibly significant sample overlap between this dataset and the dataset used to source PRS41_CLL. Additional cases were obtained from Duke University and Cornell
PSS001173 Cases were individuals with monoclonal B-cell lymphocytosis (MBL) from two Mayo Clincs.Within the Mayo Clinic Biobank, MBL was screened for using a highly sensitive, 8-color (CD38, CD45, Kappa, Lambda, CD19, CD23, CD5 and CD20) flow-cytometry assay with the capacity to detect clonal B-cell counts to the 0.005% level (1/20,000 events), and for each individual, 500,000 PBMC events were typically captured. Of the 560 MBL cases, 396 had low-count MBL (LC-MBL) and 164 had high-count MBL (HC-MBL). Wiithin the Mayo Clinic Biobank only a subset of participants had a complete blood count. therefore the percent of clonal B-cells out of total B-cells was used to categorize participants as LC- and HC-MBL. Based on prior evidence, those MBL individuals with a percent clonal B-cell <85% were defined as LC-MBL and those with percent clonal B-cells ≥85% as HC-MBL. Within the Mayo Clinic Chronic lymphocytic leukemia (CLL) Resource, MBL was classified by LC-MBL or HC-MBL according to the B-cell clone size of below or above 0.5 × 109/L threshold, respectively.
[
  • 560 cases
  • , 2,631 controls
]
,
42.28 % Male samples
European MAYO Possibly significant sample overlap between this dataset and the dataset used to source PRS41_CLL.
PSS000436 The discovery cohort included 1001 patients from the University clinics in Uppsala, Linköping, Karolinska Institute (Stockholm), Lund, and from the four northern-most counties in Sweden. All subjects fulfilled ≥4 ACR-82 classification criteria for SLE and were of European descent.30 Clinical data were collected from the patients’ medical files, including SDI scores, the ACR-82 classification criteria, clinical antiphospholipid syndrome (APS) diagnosis, glomerular filtration rate, chronic kidney disease (CKD) stages, ESRD, renal biopsy data and CVE, defined as myocardial infarction, ischaemic cerebrovascular disease or venous thromboembolism (VTE). Control individuals were healthy blood donors from Uppsala (Uppsala Bioresource) and Lund or population based controls from Stockholm and the four northernmost counties of Sweden.
[
  • 1,001 cases
  • , 2,802 controls
]
European Karolinska, UHU The discovery cohort included 1001 patients from the University clinics in Uppsala, Linköping, Karolinska Institute (Stockholm), Lund, and from the four northern-most counties in Sweden
PSS000437 The discovery cohort included 1001 patients from the University clinics in Uppsala, Linköping, Karolinska Institute (Stockholm), Lund, and from the four northern-most counties in Sweden. All subjects fulfilled ≥4 ACR-82 classification criteria for SLE and were of European descent.30 Clinical data were collected from the patients’ medical files, including SDI scores, the ACR-82 classification criteria, clinical antiphospholipid syndrome (APS) diagnosis, glomerular filtration rate, chronic kidney disease (CKD) stages, ESRD, renal biopsy data and CVE, defined as myocardial infarction, ischaemic cerebrovascular disease or venous thromboembolism (VTE).
[
  • 1,001 cases
  • , 0 controls
]
European Karolinska, UHU The discovery cohort included 1001 patients from the University clinics in Uppsala, Linköping, Karolinska Institute (Stockholm), Lund, and from the four northern-most counties in Sweden
PSS000438
[
  • 5,524 cases
  • , 9,859 controls
]
European NR The replication cohort is described in Langefeld et al. (PMID:28714469)
PSS001119 Cases were individuals with chronic lymphocytic leukemia.
[
  • 201 cases
  • , 1,267 controls
]
Not reported MAYO Cases were obtained from the Genetic Epidemiology of CLL (GEC) Consortium
PSS001120 Cases were individuals with monoclonal B-cell lymphocytosis.
[
  • 95 cases
  • , 1,267 controls
]
Not reported MAYO Cases were obtained from the Genetic Epidemiology of CLL (GEC) Consortium
PSS001121 Cases were individuals with chronic lymphocytic leukemia.
[
  • 135 cases
  • , 83 controls
]
Not reported NR Cases and controls were obtained from the Genetic Epidemiology of CLL (GEC) Consortium
PSS001016 Individuals with at least one recorded incident diagnosis of a borderline, in situ, or malignant primary cancer were defined as cases.
[
  • 340 cases
  • , 390,998 controls
]
European UKB
PSS001123 Cases were individuals with chronic lymphocytic leukemia (CLL). Of the 3,958 individuals, 242 had a family history (FH) of hematological cancers, whereas 2,409 had no FH of hematological cancers. Of the 242 individuals with a FH, 112 had CLL. Of the 2,409 without a FH, 783 had CLL. FH was defined as a person self-reporting any hematological maligcancy among first-degree relatives. Hematological malignancies were defined as any non-Hodgkin lymphoma, Hodgkin lymphoma, multiple myeloma, or leukemia.
[
  • 1,499 cases
  • , 2,459 controls
]
,
60.81 % Male samples
European, NR 8 cohorts
  • BC
  • ,ENGELA
  • ,EpiLymph
  • ,MAYO
  • ,NCI-SEER
  • ,NSW
  • ,SCALE
  • ,UCSF2
Possible significant sample overlap between this dataset and the dataset used to source PRS41_CLL.
PSS001122 Cases were individuals with monoclonal B-cell lymphocytosis.
[
  • 95 cases
  • , 58 controls
]
Not reported NR Cases and controls were obtained from the Genetic Epidemiology of CLL (GEC) Consortium
PSS001019 Individuals with at least one recorded incident diagnosis of a borderline, in situ, or malignant primary cancer were defined as cases.
[
  • 970 cases
  • , 390,998 controls
]
European UKB
PSS000116 Cancer diagnoses were obtained from reigstry data in GERA, and ICD-9/10 codes mapped to ICD-O-3 codes in UK Biobank. Cancers for this phenotype were classified using the following SEER site recode(s): 35011 - 35013
[
  • 853 cases
  • , 410,354 controls
]
,
46.0 % Male samples
Mean = 58.0 years European GERA, UKB
PSS004538
[
  • 160 cases
  • , 24,745 controls
]
European non-white British ancestry UKB
PSS000119 Cancer diagnoses were obtained from reigstry data in GERA, and ICD-9/10 codes mapped to ICD-O-3 codes in UK Biobank. Cancers for this phenotype were classified using the following SEER site recode(s): 33041, and 33042
[
  • 2,411 cases
  • , 410,354 controls
]
,
46.0 % Male samples
Mean = 58.0 years European GERA, UKB