ID

45899

Description

Principal Investigator: Donald Bowden, PhD, Center for Human Genomics, Center for Diabetes Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA MeSH: Diabetes Mellitus, Type 2 https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001167 *The T2D Seq GWAS AA Cohort is utilized in the following dbGaP sub-studies.* To view genotypes, other molecular data, and derived variables collected in these sub-studies, please click on the following sub-studies below or in the "Sub-studies" box located on the right hand side of this top-level study page phs001167 T2D Seq GWAS AA Cohort.- phs000140 CIDR T2D Bowdens - phs001102 T2D GENES AA

Link

dbGaP study = phs001167

Keywords

Versions (1)
  1. 12/12/23 12/12/23 - Simon Heim
Copyright Holder

Donald Bowden, PhD, Center for Human Genomics, Center for Diabetes Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA

Uploaded on

December 12, 2023

DOI

To request one please log in.

License

Creative Commons BY 4.0
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dbGaP phs001167 Type 2 Diabetes in African Americans, GWAS and Exome Sequencing

English

Subject ID, race, age, gender, BMI, fasting blood sugar, consent form(s) signed by participant, participant is fasting when he has not had anything to eat or drink 12 hours prior to blood draw, pc1, pc2, pc3, pc4, pc5, pc6, pc7, pc8, pc9, and pc10 of participants without T2D and involved in the "A Whole Genome Association Search for Type 2 Diabetes Genes in African Americans" project.

8 forms  
  1. StudyEvent: SEV1
    1. Subject ID, race, age, gender, BMI, consent form(s) signed by participants, age of type 2 diabetes Mellitus diagnosis, age of end stage renal disease diagnosis, age of hypertension diagnosis, duration of type 2 diabetes Mellitus since diagnosis, individual has reported that he/she is on dialysis, individual has reported hypertension, individual has diabetic retinopathy reported by ophthalmologist or laser treatment with photocoagulation, HBA1C, pc1, pc2, pc3, pc4, pc5, pc6, pc7, pc8, pc9, and pc10 of participants with T2D and involved in the "A Whole Genome Association Search for Type 2 Diabetes Genes in African Americans" project.
    2. Subject ID, race, age, gender, BMI, fasting blood sugar, consent form(s) signed by participant, participant is fasting when he has not had anything to eat or drink 12 hours prior to blood draw, pc1, pc2, pc3, pc4, pc5, pc6, pc7, pc8, pc9, and pc10 of participants without T2D and involved in the "A Whole Genome Association Search for Type 2 Diabetes Genes in African Americans" project.
    3. Subject ID, family ID, mother ID, father ID, gender, and monozygotic tween ID of participants with of without T2D and involved in the "A Whole Genome Association Search for Type 2 Diabetes Genes in African Americans" project.
    4. Subject ID, consent group, subject source, source subject ID, and affection status of participants with or without T2D and involved in the "Type 2 Diabetes in African Americans, GWAS and Exome Sequencing" project.
    5. Subject ID, sample ID, sample source, source sample ID, sample use, if sample was used in analysis, and reason why sample was used in analysis of participants with or without T2D and involved in the "Type 2 Diabetes in African Americans, GWAS and Exome Sequencing" project.
    6. Subject ID, gender, age, T2D, diabetes age of diagnosis at time of study, height, weight, BMI, consortium name, and cohort name of participants with or without T2D and involved in the "Type 2 Diabetes Genetic Exploration by Next-generation Sequencing in Multi-Ethnic Samples (T2D-GENES) Project 1: Wake Forest African American Type 2 Diabetes" project.
    7. Sample ID, body site where sample was obtained, analyte type, histological type of sample, sequencing center, and tumor status of participants with or without T2D and involved in the "Type 2 Diabetes Genetic Exploration by Next-generation Sequencing in Multi-Ethnic Samples (T2D-GENES) Project 1: Wake Forest African American Type 2 Diabetes" project.
    8. Sample ID, analyte type, body site, and tumor status of participants with or without T2D and involved in the "A Whole Genome Association Search for Type 2 Diabetes Genes in African Americans" project.
pht000631
Description

pht000631

Alias
UMLS CUI [1,1]
C3846158
Subject ID
Description

SUBJID

Data type

text

Alias
UMLS CUI [1,1]
C2348585
Race, ethnicity of participant
Description

Race

Data type

text

Alias
UMLS CUI [1,1]
C5441552
Age at recruitment
Description

Age

Data type

text

Measurement units
  • Years
Alias
UMLS CUI [1,1]
C0001779
UMLS CUI [1,2]
C0242800
Years
Gender of participant has been confirmed with genotyping: 1 = Male, 2 = Female
Description

Gender

Data type

text

Alias
UMLS CUI [1,1]
C0079399
UMLS CUI [1,2]
C1285573
Body Mass Index
Description

BMI

Data type

text

Measurement units
  • kg/m2
Alias
UMLS CUI [1,1]
C1305855
kg/m2
Fasting Blood Sugar
Description

FBS

Data type

text

Measurement units
  • mg/dL
Alias
UMLS CUI [1,1]
C0202045
mg/dL
Consent form(s) signed by participant
Description

Consent

Data type

text

Alias
UMLS CUI [1,1]
C0009797
UMLS CUI [1,2]
C0742766
Participant is fasting when he has not had anything to eat or drink 12 hours prior to blood draw.
Description

Fasting status

Data type

text

Alias
UMLS CUI [1,1]
C4554048
UMLS CUI [1,2]
C0015663
UMLS CUI [1,3]
C4263302
UMLS CUI [1,4]
C3173371
UMLS CUI [1,5]
C1518422
UMLS CUI [1,6]
C0332168
UMLS CUI [1,7]
C0332152
UMLS CUI [1,8]
C0005834
Principal component 1: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
Description

pc1

Data type

text

Alias
UMLS CUI [1,1]
C1882460
UMLS CUI [1,2]
C0489829
UMLS CUI [1,3]
C0871424
UMLS CUI [1,4]
C1707511
UMLS CUI [1,5]
C1707520
UMLS CUI [2,1]
C0871342
UMLS CUI [2,2]
C0871161
UMLS CUI [2,3]
C1705241
UMLS CUI [2,4]
C0220825
UMLS CUI [2,5]
C0237589
UMLS CUI [3,1]
C1882460
UMLS CUI [3,2]
C1947906
UMLS CUI [3,3]
C1265611
UMLS CUI [3,4]
C2348152
UMLS CUI [3,5]
C4553182
UMLS CUI [3,6]
C0443228
UMLS CUI [3,7]
C1264664
UMLS CUI [4,1]
C0025663
UMLS CUI [4,2]
C1148554
UMLS CUI [4,3]
C0449961
UMLS CUI [4,4]
C0936012
UMLS CUI [4,5]
C1518422
UMLS CUI [4,6]
C0205375
UMLS CUI [4,7]
C0870442
Principal component 2: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
Description

pc2

Data type

text

Alias
UMLS CUI [1,1]
C1882460
UMLS CUI [1,2]
C0489829
UMLS CUI [1,3]
C0871424
UMLS CUI [1,4]
C1707511
UMLS CUI [1,5]
C1707520
UMLS CUI [2,1]
C0871342
UMLS CUI [2,2]
C0871161
UMLS CUI [2,3]
C1705241
UMLS CUI [2,4]
C0220825
UMLS CUI [2,5]
C0237589
UMLS CUI [3,1]
C1882460
UMLS CUI [3,2]
C1947906
UMLS CUI [3,3]
C1265611
UMLS CUI [3,4]
C2348152
UMLS CUI [3,5]
C4553182
UMLS CUI [3,6]
C0443228
UMLS CUI [3,7]
C1264664
UMLS CUI [4,1]
C0025663
UMLS CUI [4,2]
C1148554
UMLS CUI [4,3]
C0449961
UMLS CUI [4,4]
C0936012
UMLS CUI [4,5]
C1518422
UMLS CUI [4,6]
C0205375
UMLS CUI [4,7]
C0870442
Principal component 3: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
Description

pc3

Data type

text

Alias
UMLS CUI [1,1]
C1882460
UMLS CUI [1,2]
C0489829
UMLS CUI [1,3]
C0871424
UMLS CUI [1,4]
C1707511
UMLS CUI [1,5]
C1707520
UMLS CUI [2,1]
C0871342
UMLS CUI [2,2]
C0871161
UMLS CUI [2,3]
C1705241
UMLS CUI [2,4]
C0220825
UMLS CUI [2,5]
C0237589
UMLS CUI [3,1]
C1882460
UMLS CUI [3,2]
C1947906
UMLS CUI [3,3]
C1265611
UMLS CUI [3,4]
C2348152
UMLS CUI [3,5]
C4553182
UMLS CUI [3,6]
C0443228
UMLS CUI [3,7]
C1264664
UMLS CUI [4,1]
C0025663
UMLS CUI [4,2]
C1148554
UMLS CUI [4,3]
C0449961
UMLS CUI [4,4]
C0936012
UMLS CUI [4,5]
C1518422
UMLS CUI [4,6]
C0205375
UMLS CUI [4,7]
C0870442
Principal component 4: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
Description

pc4

Data type

text

Alias
UMLS CUI [1,1]
C1882460
UMLS CUI [1,2]
C0489829
UMLS CUI [1,3]
C0871424
UMLS CUI [1,4]
C1707511
UMLS CUI [1,5]
C1707520
UMLS CUI [2,1]
C0871342
UMLS CUI [2,2]
C0871161
UMLS CUI [2,3]
C1705241
UMLS CUI [2,4]
C0220825
UMLS CUI [2,5]
C0237589
UMLS CUI [3,1]
C1882460
UMLS CUI [3,2]
C1947906
UMLS CUI [3,3]
C1265611
UMLS CUI [3,4]
C2348152
UMLS CUI [3,5]
C4553182
UMLS CUI [3,6]
C0443228
UMLS CUI [3,7]
C1264664
UMLS CUI [4,1]
C0025663
UMLS CUI [4,2]
C1148554
UMLS CUI [4,3]
C0449961
UMLS CUI [4,4]
C0936012
UMLS CUI [4,5]
C1518422
UMLS CUI [4,6]
C0205375
UMLS CUI [4,7]
C0870442
Principal component 5: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
Description

pc5

Data type

text

Alias
UMLS CUI [1,1]
C1882460
UMLS CUI [1,2]
C0489829
UMLS CUI [1,3]
C0871424
UMLS CUI [1,4]
C1707511
UMLS CUI [1,5]
C1707520
UMLS CUI [2,1]
C0871342
UMLS CUI [2,2]
C0871161
UMLS CUI [2,3]
C1705241
UMLS CUI [2,4]
C0220825
UMLS CUI [2,5]
C0237589
UMLS CUI [3,1]
C1882460
UMLS CUI [3,2]
C1947906
UMLS CUI [3,3]
C1265611
UMLS CUI [3,4]
C2348152
UMLS CUI [3,5]
C4553182
UMLS CUI [3,6]
C0443228
UMLS CUI [3,7]
C1264664
UMLS CUI [4,1]
C0025663
UMLS CUI [4,2]
C1148554
UMLS CUI [4,3]
C0449961
UMLS CUI [4,4]
C0936012
UMLS CUI [4,5]
C1518422
UMLS CUI [4,6]
C0205375
UMLS CUI [4,7]
C0870442
Principal component 6: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
Description

pc6

Data type

text

Alias
UMLS CUI [1,1]
C1882460
UMLS CUI [1,2]
C0489829
UMLS CUI [1,3]
C0871424
UMLS CUI [1,4]
C1707511
UMLS CUI [1,5]
C1707520
UMLS CUI [2,1]
C0871342
UMLS CUI [2,2]
C0871161
UMLS CUI [2,3]
C1705241
UMLS CUI [2,4]
C0220825
UMLS CUI [2,5]
C0237589
UMLS CUI [3,1]
C1882460
UMLS CUI [3,2]
C1947906
UMLS CUI [3,3]
C1265611
UMLS CUI [3,4]
C2348152
UMLS CUI [3,5]
C4553182
UMLS CUI [3,6]
C0443228
UMLS CUI [3,7]
C1264664
UMLS CUI [4,1]
C0025663
UMLS CUI [4,2]
C1148554
UMLS CUI [4,3]
C0449961
UMLS CUI [4,4]
C0936012
UMLS CUI [4,5]
C1518422
UMLS CUI [4,6]
C0205375
UMLS CUI [4,7]
C0870442
Principal component 7: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
Description

pc7

Data type

text

Alias
UMLS CUI [1,1]
C1882460
UMLS CUI [1,2]
C0489829
UMLS CUI [1,3]
C0871424
UMLS CUI [1,4]
C1707511
UMLS CUI [1,5]
C1707520
UMLS CUI [2,1]
C0871342
UMLS CUI [2,2]
C0871161
UMLS CUI [2,3]
C1705241
UMLS CUI [2,4]
C0220825
UMLS CUI [2,5]
C0237589
UMLS CUI [3,1]
C1882460
UMLS CUI [3,2]
C1947906
UMLS CUI [3,3]
C1265611
UMLS CUI [3,4]
C2348152
UMLS CUI [3,5]
C4553182
UMLS CUI [3,6]
C0443228
UMLS CUI [3,7]
C1264664
UMLS CUI [4,1]
C0025663
UMLS CUI [4,2]
C1148554
UMLS CUI [4,3]
C0449961
UMLS CUI [4,4]
C0936012
UMLS CUI [4,5]
C1518422
UMLS CUI [4,6]
C0205375
UMLS CUI [4,7]
C0870442
Principal component 8: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
Description

pc8

Data type

text

Alias
UMLS CUI [1,1]
C1882460
UMLS CUI [1,2]
C0489829
UMLS CUI [1,3]
C0871424
UMLS CUI [1,4]
C1707511
UMLS CUI [1,5]
C1707520
UMLS CUI [2,1]
C0871342
UMLS CUI [2,2]
C0871161
UMLS CUI [2,3]
C1705241
UMLS CUI [2,4]
C0220825
UMLS CUI [2,5]
C0237589
UMLS CUI [3,1]
C1882460
UMLS CUI [3,2]
C1947906
UMLS CUI [3,3]
C1265611
UMLS CUI [3,4]
C2348152
UMLS CUI [3,5]
C4553182
UMLS CUI [3,6]
C0443228
UMLS CUI [3,7]
C1264664
UMLS CUI [4,1]
C0025663
UMLS CUI [4,2]
C1148554
UMLS CUI [4,3]
C0449961
UMLS CUI [4,4]
C0936012
UMLS CUI [4,5]
C1518422
UMLS CUI [4,6]
C0205375
UMLS CUI [4,7]
C0870442
Principal component 9: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
Description

pc9

Data type

text

Alias
UMLS CUI [1,1]
C1882460
UMLS CUI [1,2]
C0489829
UMLS CUI [1,3]
C0871424
UMLS CUI [1,4]
C1707511
UMLS CUI [1,5]
C1707520
UMLS CUI [2,1]
C0871342
UMLS CUI [2,2]
C0871161
UMLS CUI [2,3]
C1705241
UMLS CUI [2,4]
C0220825
UMLS CUI [2,5]
C0237589
UMLS CUI [3,1]
C1882460
UMLS CUI [3,2]
C1947906
UMLS CUI [3,3]
C1265611
UMLS CUI [3,4]
C2348152
UMLS CUI [3,5]
C4553182
UMLS CUI [3,6]
C0443228
UMLS CUI [3,7]
C1264664
UMLS CUI [4,1]
C0025663
UMLS CUI [4,2]
C1148554
UMLS CUI [4,3]
C0449961
UMLS CUI [4,4]
C0936012
UMLS CUI [4,5]
C1518422
UMLS CUI [4,6]
C0205375
UMLS CUI [4,7]
C0870442
Principal component 10: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
Description

pc10

Data type

text

Alias
UMLS CUI [1,1]
C1882460
UMLS CUI [1,2]
C0489829
UMLS CUI [1,3]
C0871424
UMLS CUI [1,4]
C1707511
UMLS CUI [1,5]
C1707520
UMLS CUI [2,1]
C0871342
UMLS CUI [2,2]
C0871161
UMLS CUI [2,3]
C1705241
UMLS CUI [2,4]
C0220825
UMLS CUI [2,5]
C0237589
UMLS CUI [3,1]
C1882460
UMLS CUI [3,2]
C1947906
UMLS CUI [3,3]
C1265611
UMLS CUI [3,4]
C2348152
UMLS CUI [3,5]
C4553182
UMLS CUI [3,6]
C0443228
UMLS CUI [3,7]
C1264664
UMLS CUI [4,1]
C0025663
UMLS CUI [4,2]
C1148554
UMLS CUI [4,3]
C0449961
UMLS CUI [4,4]
C0936012
UMLS CUI [4,5]
C1518422
UMLS CUI [4,6]
C0205375
UMLS CUI [4,7]
C0870442
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Similar models

Subject ID, race, age, gender, BMI, fasting blood sugar, consent form(s) signed by participant, participant is fasting when he has not had anything to eat or drink 12 hours prior to blood draw, pc1, pc2, pc3, pc4, pc5, pc6, pc7, pc8, pc9, and pc10 of participants without T2D and involved in the "A Whole Genome Association Search for Type 2 Diabetes Genes in African Americans" project.

8 forms
  1. StudyEvent: SEV1
    1. Subject ID, race, age, gender, BMI, consent form(s) signed by participants, age of type 2 diabetes Mellitus diagnosis, age of end stage renal disease diagnosis, age of hypertension diagnosis, duration of type 2 diabetes Mellitus since diagnosis, individual has reported that he/she is on dialysis, individual has reported hypertension, individual has diabetic retinopathy reported by ophthalmologist or laser treatment with photocoagulation, HBA1C, pc1, pc2, pc3, pc4, pc5, pc6, pc7, pc8, pc9, and pc10 of participants with T2D and involved in the "A Whole Genome Association Search for Type 2 Diabetes Genes in African Americans" project.
    2. Subject ID, race, age, gender, BMI, fasting blood sugar, consent form(s) signed by participant, participant is fasting when he has not had anything to eat or drink 12 hours prior to blood draw, pc1, pc2, pc3, pc4, pc5, pc6, pc7, pc8, pc9, and pc10 of participants without T2D and involved in the "A Whole Genome Association Search for Type 2 Diabetes Genes in African Americans" project.
    3. Subject ID, family ID, mother ID, father ID, gender, and monozygotic tween ID of participants with of without T2D and involved in the "A Whole Genome Association Search for Type 2 Diabetes Genes in African Americans" project.
    4. Subject ID, consent group, subject source, source subject ID, and affection status of participants with or without T2D and involved in the "Type 2 Diabetes in African Americans, GWAS and Exome Sequencing" project.
    5. Subject ID, sample ID, sample source, source sample ID, sample use, if sample was used in analysis, and reason why sample was used in analysis of participants with or without T2D and involved in the "Type 2 Diabetes in African Americans, GWAS and Exome Sequencing" project.
    6. Subject ID, gender, age, T2D, diabetes age of diagnosis at time of study, height, weight, BMI, consortium name, and cohort name of participants with or without T2D and involved in the "Type 2 Diabetes Genetic Exploration by Next-generation Sequencing in Multi-Ethnic Samples (T2D-GENES) Project 1: Wake Forest African American Type 2 Diabetes" project.
    7. Sample ID, body site where sample was obtained, analyte type, histological type of sample, sequencing center, and tumor status of participants with or without T2D and involved in the "Type 2 Diabetes Genetic Exploration by Next-generation Sequencing in Multi-Ethnic Samples (T2D-GENES) Project 1: Wake Forest African American Type 2 Diabetes" project.
    8. Sample ID, analyte type, body site, and tumor status of participants with or without T2D and involved in the "A Whole Genome Association Search for Type 2 Diabetes Genes in African Americans" project.
Name
Type
Description | Question | Decode (Coded Value)
Data type
Alias
Item Group
pht000631
C3846158 (UMLS CUI [1,1])
SUBJID
Item
Subject ID
text
C2348585 (UMLS CUI [1,1])
Item
Race, ethnicity of participant
text
C5441552 (UMLS CUI [1,1])
Race
Code List
Race, ethnicity of participant
CL Item
African American (AA)
Age
Item
Age at recruitment
text
C0001779 (UMLS CUI [1,1])
C0242800 (UMLS CUI [1,2])
Item
Gender of participant has been confirmed with genotyping: 1 = Male, 2 = Female
text
C0079399 (UMLS CUI [1,1])
C1285573 (UMLS CUI [1,2])
Gender
Code List
Gender of participant has been confirmed with genotyping: 1 = Male, 2 = Female
CL Item
Male (1)
C0086582 (UMLS CUI [1,1])
CL Item
Female (2)
C0086287 (UMLS CUI [1,1])
BMI
Item
Body Mass Index
text
C1305855 (UMLS CUI [1,1])
FBS
Item
Fasting Blood Sugar
text
C0202045 (UMLS CUI [1,1])
Item
Consent form(s) signed by participant
text
C0009797 (UMLS CUI [1,1])
C0742766 (UMLS CUI [1,2])
Consent
Code List
Consent form(s) signed by participant
CL Item
BG91-0129 (A)
C0009797 (UMLS CUI [1,1])
CL Item
BG92-0090 (B)
C0009797 (UMLS CUI [1,1])
CL Item
BG95-109 (C)
C0009797 (UMLS CUI [1,1])
CL Item
BG00-028 (D)
C0009797 (UMLS CUI [1,1])
CL Item
BG00-427 (E)
C0009797 (UMLS CUI [1,1])
CL Item
BG00-427, Reconsent form (E,H)
C0009797 (UMLS CUI [1,1])
CL Item
BG00-427, Deceased participant (E,X)
C0009797 (UMLS CUI [1,1])
C1555024 (UMLS CUI [1,2])
CL Item
BG00-427_002, Reconsent form (E-002,H)
C0009797 (UMLS CUI [1,1])
CL Item
BG00-427_004 (E-004)
C0009797 (UMLS CUI [1,1])
CL Item
BG00-427_001 (E_001)
C0009797 (UMLS CUI [1,1])
CL Item
BG00-427_002 (E_002)
C0009797 (UMLS CUI [1,1])
CL Item
BG00-427_002, Reconsent form (E_002,H)
C0009797 (UMLS CUI [1,1])
CL Item
BG00-427_003 (E_003)
C0009797 (UMLS CUI [1,1])
CL Item
BG00-427_004 (E_004)
C0009797 (UMLS CUI [1,1])
CL Item
BG00-427_005 (E_005)
C0009797 (UMLS CUI [1,1])
CL Item
BG01-059 (F)
C0009797 (UMLS CUI [1,1])
CL Item
BG01-299 (G)
C0009797 (UMLS CUI [1,1])
CL Item
BG01-299-001 (G_001)
C0009797 (UMLS CUI [1,1])
CL Item
BG01-299-002 (G_002)
C0009797 (UMLS CUI [1,1])
CL Item
Reconsent form (H)
C0009797 (UMLS CUI [1,1])
CL Item
Deceased participant (X)
C1555024 (UMLS CUI [1,1])
Item
Participant is fasting when he has not had anything to eat or drink 12 hours prior to blood draw.
text
C4554048 (UMLS CUI [1,1])
C0015663 (UMLS CUI [1,2])
C4263302 (UMLS CUI [1,3])
C3173371 (UMLS CUI [1,4])
C1518422 (UMLS CUI [1,5])
C0332168 (UMLS CUI [1,6])
C0332152 (UMLS CUI [1,7])
C0005834 (UMLS CUI [1,8])
Fasting status
Code List
Participant is fasting when he has not had anything to eat or drink 12 hours prior to blood draw.
CL Item
Unknown (.)
C0439673 (UMLS CUI [1,1])
CL Item
Fasting (fasting)
C0015663 (UMLS CUI [1,1])
CL Item
Not fasting (not fasting)
C0015663 (UMLS CUI [1,1])
C1272696 (UMLS CUI [1,2])
pc1
Item
Principal component 1: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
text
C1882460 (UMLS CUI [1,1])
C0489829 (UMLS CUI [1,2])
C0871424 (UMLS CUI [1,3])
C1707511 (UMLS CUI [1,4])
C1707520 (UMLS CUI [1,5])
C0871342 (UMLS CUI [2,1])
C0871161 (UMLS CUI [2,2])
C1705241 (UMLS CUI [2,3])
C0220825 (UMLS CUI [2,4])
C0237589 (UMLS CUI [2,5])
C1882460 (UMLS CUI [3,1])
C1947906 (UMLS CUI [3,2])
C1265611 (UMLS CUI [3,3])
C2348152 (UMLS CUI [3,4])
C4553182 (UMLS CUI [3,5])
C0443228 (UMLS CUI [3,6])
C1264664 (UMLS CUI [3,7])
C0025663 (UMLS CUI [4,1])
C1148554 (UMLS CUI [4,2])
C0449961 (UMLS CUI [4,3])
C0936012 (UMLS CUI [4,4])
C1518422 (UMLS CUI [4,5])
C0205375 (UMLS CUI [4,6])
C0870442 (UMLS CUI [4,7])
pc2
Item
Principal component 2: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
text
C1882460 (UMLS CUI [1,1])
C0489829 (UMLS CUI [1,2])
C0871424 (UMLS CUI [1,3])
C1707511 (UMLS CUI [1,4])
C1707520 (UMLS CUI [1,5])
C0871342 (UMLS CUI [2,1])
C0871161 (UMLS CUI [2,2])
C1705241 (UMLS CUI [2,3])
C0220825 (UMLS CUI [2,4])
C0237589 (UMLS CUI [2,5])
C1882460 (UMLS CUI [3,1])
C1947906 (UMLS CUI [3,2])
C1265611 (UMLS CUI [3,3])
C2348152 (UMLS CUI [3,4])
C4553182 (UMLS CUI [3,5])
C0443228 (UMLS CUI [3,6])
C1264664 (UMLS CUI [3,7])
C0025663 (UMLS CUI [4,1])
C1148554 (UMLS CUI [4,2])
C0449961 (UMLS CUI [4,3])
C0936012 (UMLS CUI [4,4])
C1518422 (UMLS CUI [4,5])
C0205375 (UMLS CUI [4,6])
C0870442 (UMLS CUI [4,7])
pc3
Item
Principal component 3: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
text
C1882460 (UMLS CUI [1,1])
C0489829 (UMLS CUI [1,2])
C0871424 (UMLS CUI [1,3])
C1707511 (UMLS CUI [1,4])
C1707520 (UMLS CUI [1,5])
C0871342 (UMLS CUI [2,1])
C0871161 (UMLS CUI [2,2])
C1705241 (UMLS CUI [2,3])
C0220825 (UMLS CUI [2,4])
C0237589 (UMLS CUI [2,5])
C1882460 (UMLS CUI [3,1])
C1947906 (UMLS CUI [3,2])
C1265611 (UMLS CUI [3,3])
C2348152 (UMLS CUI [3,4])
C4553182 (UMLS CUI [3,5])
C0443228 (UMLS CUI [3,6])
C1264664 (UMLS CUI [3,7])
C0025663 (UMLS CUI [4,1])
C1148554 (UMLS CUI [4,2])
C0449961 (UMLS CUI [4,3])
C0936012 (UMLS CUI [4,4])
C1518422 (UMLS CUI [4,5])
C0205375 (UMLS CUI [4,6])
C0870442 (UMLS CUI [4,7])
pc4
Item
Principal component 4: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
text
C1882460 (UMLS CUI [1,1])
C0489829 (UMLS CUI [1,2])
C0871424 (UMLS CUI [1,3])
C1707511 (UMLS CUI [1,4])
C1707520 (UMLS CUI [1,5])
C0871342 (UMLS CUI [2,1])
C0871161 (UMLS CUI [2,2])
C1705241 (UMLS CUI [2,3])
C0220825 (UMLS CUI [2,4])
C0237589 (UMLS CUI [2,5])
C1882460 (UMLS CUI [3,1])
C1947906 (UMLS CUI [3,2])
C1265611 (UMLS CUI [3,3])
C2348152 (UMLS CUI [3,4])
C4553182 (UMLS CUI [3,5])
C0443228 (UMLS CUI [3,6])
C1264664 (UMLS CUI [3,7])
C0025663 (UMLS CUI [4,1])
C1148554 (UMLS CUI [4,2])
C0449961 (UMLS CUI [4,3])
C0936012 (UMLS CUI [4,4])
C1518422 (UMLS CUI [4,5])
C0205375 (UMLS CUI [4,6])
C0870442 (UMLS CUI [4,7])
pc5
Item
Principal component 5: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
text
C1882460 (UMLS CUI [1,1])
C0489829 (UMLS CUI [1,2])
C0871424 (UMLS CUI [1,3])
C1707511 (UMLS CUI [1,4])
C1707520 (UMLS CUI [1,5])
C0871342 (UMLS CUI [2,1])
C0871161 (UMLS CUI [2,2])
C1705241 (UMLS CUI [2,3])
C0220825 (UMLS CUI [2,4])
C0237589 (UMLS CUI [2,5])
C1882460 (UMLS CUI [3,1])
C1947906 (UMLS CUI [3,2])
C1265611 (UMLS CUI [3,3])
C2348152 (UMLS CUI [3,4])
C4553182 (UMLS CUI [3,5])
C0443228 (UMLS CUI [3,6])
C1264664 (UMLS CUI [3,7])
C0025663 (UMLS CUI [4,1])
C1148554 (UMLS CUI [4,2])
C0449961 (UMLS CUI [4,3])
C0936012 (UMLS CUI [4,4])
C1518422 (UMLS CUI [4,5])
C0205375 (UMLS CUI [4,6])
C0870442 (UMLS CUI [4,7])
pc6
Item
Principal component 6: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
text
C1882460 (UMLS CUI [1,1])
C0489829 (UMLS CUI [1,2])
C0871424 (UMLS CUI [1,3])
C1707511 (UMLS CUI [1,4])
C1707520 (UMLS CUI [1,5])
C0871342 (UMLS CUI [2,1])
C0871161 (UMLS CUI [2,2])
C1705241 (UMLS CUI [2,3])
C0220825 (UMLS CUI [2,4])
C0237589 (UMLS CUI [2,5])
C1882460 (UMLS CUI [3,1])
C1947906 (UMLS CUI [3,2])
C1265611 (UMLS CUI [3,3])
C2348152 (UMLS CUI [3,4])
C4553182 (UMLS CUI [3,5])
C0443228 (UMLS CUI [3,6])
C1264664 (UMLS CUI [3,7])
C0025663 (UMLS CUI [4,1])
C1148554 (UMLS CUI [4,2])
C0449961 (UMLS CUI [4,3])
C0936012 (UMLS CUI [4,4])
C1518422 (UMLS CUI [4,5])
C0205375 (UMLS CUI [4,6])
C0870442 (UMLS CUI [4,7])
pc7
Item
Principal component 7: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
text
C1882460 (UMLS CUI [1,1])
C0489829 (UMLS CUI [1,2])
C0871424 (UMLS CUI [1,3])
C1707511 (UMLS CUI [1,4])
C1707520 (UMLS CUI [1,5])
C0871342 (UMLS CUI [2,1])
C0871161 (UMLS CUI [2,2])
C1705241 (UMLS CUI [2,3])
C0220825 (UMLS CUI [2,4])
C0237589 (UMLS CUI [2,5])
C1882460 (UMLS CUI [3,1])
C1947906 (UMLS CUI [3,2])
C1265611 (UMLS CUI [3,3])
C2348152 (UMLS CUI [3,4])
C4553182 (UMLS CUI [3,5])
C0443228 (UMLS CUI [3,6])
C1264664 (UMLS CUI [3,7])
C0025663 (UMLS CUI [4,1])
C1148554 (UMLS CUI [4,2])
C0449961 (UMLS CUI [4,3])
C0936012 (UMLS CUI [4,4])
C1518422 (UMLS CUI [4,5])
C0205375 (UMLS CUI [4,6])
C0870442 (UMLS CUI [4,7])
pc8
Item
Principal component 8: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
text
C1882460 (UMLS CUI [1,1])
C0489829 (UMLS CUI [1,2])
C0871424 (UMLS CUI [1,3])
C1707511 (UMLS CUI [1,4])
C1707520 (UMLS CUI [1,5])
C0871342 (UMLS CUI [2,1])
C0871161 (UMLS CUI [2,2])
C1705241 (UMLS CUI [2,3])
C0220825 (UMLS CUI [2,4])
C0237589 (UMLS CUI [2,5])
C1882460 (UMLS CUI [3,1])
C1947906 (UMLS CUI [3,2])
C1265611 (UMLS CUI [3,3])
C2348152 (UMLS CUI [3,4])
C4553182 (UMLS CUI [3,5])
C0443228 (UMLS CUI [3,6])
C1264664 (UMLS CUI [3,7])
C0025663 (UMLS CUI [4,1])
C1148554 (UMLS CUI [4,2])
C0449961 (UMLS CUI [4,3])
C0936012 (UMLS CUI [4,4])
C1518422 (UMLS CUI [4,5])
C0205375 (UMLS CUI [4,6])
C0870442 (UMLS CUI [4,7])
pc9
Item
Principal component 9: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
text
C1882460 (UMLS CUI [1,1])
C0489829 (UMLS CUI [1,2])
C0871424 (UMLS CUI [1,3])
C1707511 (UMLS CUI [1,4])
C1707520 (UMLS CUI [1,5])
C0871342 (UMLS CUI [2,1])
C0871161 (UMLS CUI [2,2])
C1705241 (UMLS CUI [2,3])
C0220825 (UMLS CUI [2,4])
C0237589 (UMLS CUI [2,5])
C1882460 (UMLS CUI [3,1])
C1947906 (UMLS CUI [3,2])
C1265611 (UMLS CUI [3,3])
C2348152 (UMLS CUI [3,4])
C4553182 (UMLS CUI [3,5])
C0443228 (UMLS CUI [3,6])
C1264664 (UMLS CUI [3,7])
C0025663 (UMLS CUI [4,1])
C1148554 (UMLS CUI [4,2])
C0449961 (UMLS CUI [4,3])
C0936012 (UMLS CUI [4,4])
C1518422 (UMLS CUI [4,5])
C0205375 (UMLS CUI [4,6])
C0870442 (UMLS CUI [4,7])
pc10
Item
Principal component 10: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.
text
C1882460 (UMLS CUI [1,1])
C0489829 (UMLS CUI [1,2])
C0871424 (UMLS CUI [1,3])
C1707511 (UMLS CUI [1,4])
C1707520 (UMLS CUI [1,5])
C0871342 (UMLS CUI [2,1])
C0871161 (UMLS CUI [2,2])
C1705241 (UMLS CUI [2,3])
C0220825 (UMLS CUI [2,4])
C0237589 (UMLS CUI [2,5])
C1882460 (UMLS CUI [3,1])
C1947906 (UMLS CUI [3,2])
C1265611 (UMLS CUI [3,3])
C2348152 (UMLS CUI [3,4])
C4553182 (UMLS CUI [3,5])
C0443228 (UMLS CUI [3,6])
C1264664 (UMLS CUI [3,7])
C0025663 (UMLS CUI [4,1])
C1148554 (UMLS CUI [4,2])
C0449961 (UMLS CUI [4,3])
C0936012 (UMLS CUI [4,4])
C1518422 (UMLS CUI [4,5])
C0205375 (UMLS CUI [4,6])
C0870442 (UMLS CUI [4,7])
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