ID

45683

Description

Principal Investigator: Neil E. Caporaso, MD, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA MeSH: Leukemia, Lymphocytic, Chronic, B-Cell,Hodgkin Disease,Lymphoma, Non-Hodgkin,Waldenstrom Macroglobulinemia,Leukemia, Hairy Cell,Leukemia, Myeloid, Acute,Leukemia, Myelomonocytic, Juvenile https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001219 We have been conducting genetic studies on families at high risk of different hematologic malignancies, in order to define the related tumors in the families, define precursor and other related conditions, and map and identify susceptibility genes. We have focused mainly on four types of lymphoid malignancies: chronic lymphocytic leukemia (CLL), Hodgkin lymphoma (HL), non-Hodgkin lymphoma (NHL), and Waldenström macroglobulinemia (WM). A few families with a rare lymphoma subtype, hairy cell leukemia (HCL) are included. In addition, single large pedigrees with acute myeloid leukemia (AML), and juvenile myelomocytic leukemia (JMML) are included. Families are ascertained for having at least two patients with the same hematologic malignancy and are classified by the type of malignancy that predominates in the family. Multiple types of lymphoid malignancies are often found in the same family. Other data has shown that these conditions aggregate together in families. Verification of cancer diagnoses is obtained through medical records, pathology reports, and flow cytometry. Family members with precursor traits are also included, monoclonal B-cell lymphocytosis (MBL) in CLL families and IgM monoclonal gammopathy of undetermined significance (MGUS) in WM families.

Link

dbGaP study = phs001219

Keywords

  1. 4/26/23 4/26/23 - Simon Heim
Copyright Holder

Neil E. Caporaso, MD, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

Uploaded on

April 26, 2023

DOI

To request one please log in.

License

Creative Commons BY 4.0

Model comments :

You can comment on the data model here. Via the speech bubbles at the itemgroups and items you can add comments to those specificially.

Itemgroup comments for :

Item comments for :


No comments

In order to download data models you must be logged in. Please log in or register for free.

dbGaP phs001219 Detection of Genes Predisposing to Hematologic Malignancies

This subject phenotype table contains subject IDs, family IDs, sex, ages (n=4 variables; at blood withdrawal, at LPD disease 1 diagnosis, at LPD disease 2 diagnosis, and at LPD disease 3 diagnosis), and LPD disease diagnose (n=3 variables; first, second, and third).

pht007860
Description

pht007860

Alias
UMLS CUI [1,1]
C3846158
De-identified Subject ID
Description

SUBJECT_ID

Data type

string

Alias
UMLS CUI [1,1]
C4684638
UMLS CUI [1,2]
C2348585
De-identified Family ID
Description

FAMILY_ID

Data type

string

Alias
UMLS CUI [1,1]
C3669174
Sex of subject
Description

SEX

Data type

string

Alias
UMLS CUI [1,1]
C0079399
Age of subject when blood was drawn for study
Description

AGE_at_BLOOD_DRAWN

Data type

text

Measurement units
  • years
Alias
UMLS CUI [1,1]
C0001779
UMLS CUI [1,2]
C3166434
years
First LPD type of disease diagnosed
Description

LPD_DIAGNOSIS1

Data type

string

Alias
UMLS CUI [1,1]
C0024314
UMLS CUI [1,2]
C0332307
UMLS CUI [1,3]
C0205435
Age of subject when LPD disease 1 was diagnosed
Description

AGE_at_DIAGNOSIS1

Data type

text

Measurement units
  • years
Alias
UMLS CUI [1,1]
C0024314
UMLS CUI [1,2]
C1828181
UMLS CUI [1,3]
C0205435
years
Second LPD type of disease diagnosed
Description

LPD_DIAGNOSIS2

Data type

string

Alias
UMLS CUI [1,1]
C0024314
UMLS CUI [1,2]
C0332307
UMLS CUI [1,3]
C0205436
Age of subject when LPD disease 2 was diagnosed
Description

AGE_at_DIAGNOSIS2

Data type

text

Measurement units
  • years
Alias
UMLS CUI [1,1]
C0024314
UMLS CUI [1,2]
C1828181
UMLS CUI [1,3]
C0205436
years
Third LPD type of disease diagnosed
Description

LPD_DIAGNOSIS3

Data type

string

Alias
UMLS CUI [1,1]
C0024314
UMLS CUI [1,2]
C0332307
UMLS CUI [1,3]
C0205437
Age of subject when LPD disease 3 was diagnosed
Description

AGE_at_DIAGNOSIS3

Data type

text

Measurement units
  • years
Alias
UMLS CUI [1,1]
C0024314
UMLS CUI [1,2]
C1828181
UMLS CUI [1,3]
C0205437
years

Similar models

This subject phenotype table contains subject IDs, family IDs, sex, ages (n=4 variables; at blood withdrawal, at LPD disease 1 diagnosis, at LPD disease 2 diagnosis, and at LPD disease 3 diagnosis), and LPD disease diagnose (n=3 variables; first, second, and third).

Name
Type
Description | Question | Decode (Coded Value)
Data type
Alias
Item Group
pht007860
C3846158 (UMLS CUI [1,1])
SUBJECT_ID
Item
De-identified Subject ID
string
C4684638 (UMLS CUI [1,1])
C2348585 (UMLS CUI [1,2])
FAMILY_ID
Item
De-identified Family ID
string
C3669174 (UMLS CUI [1,1])
SEX
Item
Sex of subject
string
C0079399 (UMLS CUI [1,1])
AGE_at_BLOOD_DRAWN
Item
Age of subject when blood was drawn for study
text
C0001779 (UMLS CUI [1,1])
C3166434 (UMLS CUI [1,2])
LPD_DIAGNOSIS1
Item
First LPD type of disease diagnosed
string
C0024314 (UMLS CUI [1,1])
C0332307 (UMLS CUI [1,2])
C0205435 (UMLS CUI [1,3])
AGE_at_DIAGNOSIS1
Item
Age of subject when LPD disease 1 was diagnosed
text
C0024314 (UMLS CUI [1,1])
C1828181 (UMLS CUI [1,2])
C0205435 (UMLS CUI [1,3])
LPD_DIAGNOSIS2
Item
Second LPD type of disease diagnosed
string
C0024314 (UMLS CUI [1,1])
C0332307 (UMLS CUI [1,2])
C0205436 (UMLS CUI [1,3])
AGE_at_DIAGNOSIS2
Item
Age of subject when LPD disease 2 was diagnosed
text
C0024314 (UMLS CUI [1,1])
C1828181 (UMLS CUI [1,2])
C0205436 (UMLS CUI [1,3])
LPD_DIAGNOSIS3
Item
Third LPD type of disease diagnosed
string
C0024314 (UMLS CUI [1,1])
C0332307 (UMLS CUI [1,2])
C0205437 (UMLS CUI [1,3])
AGE_at_DIAGNOSIS3
Item
Age of subject when LPD disease 3 was diagnosed
text
C0024314 (UMLS CUI [1,1])
C1828181 (UMLS CUI [1,2])
C0205437 (UMLS CUI [1,3])

Please use this form for feedback, questions and suggestions for improvements.

Fields marked with * are required.

Do you need help on how to use the search function? Please watch the corresponding tutorial video for more details and learn how to use the search function most efficiently.

Watch Tutorial