ID

45899

Descripción

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

Palabras clave

Versiones (1)
  1. 12/12/23 12/12/23 - Simon Heim
Titular de derechos de autor

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

Subido en

12 de diciembre de 2023

DOI

Para solicitar uno, por favor iniciar sesión.

Licencia

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

    Inglés

    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 formularios  
    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
    Descripción

    pht000631

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

    SUBJID

    Tipo de datos

    text

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

    Race

    Tipo de datos

    text

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

    Age

    Tipo de datos

    text

    Unidades de medida
    • 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
    Descripción

    Gender

    Tipo de datos

    text

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

    BMI

    Tipo de datos

    text

    Unidades de medida
    • kg/m2
    Alias
    UMLS CUI [1,1]
    C1305855
    kg/m2
    Fasting Blood Sugar
    Descripción

    FBS

    Tipo de datos

    text

    Unidades de medida
    • mg/dL
    Alias
    UMLS CUI [1,1]
    C0202045
    mg/dL
    Consent form(s) signed by participant
    Descripción

    Consent

    Tipo de datos

    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.
    Descripción

    Fasting status

    Tipo de datos

    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.
    Descripción

    pc1

    Tipo de datos

    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.
    Descripción

    pc2

    Tipo de datos

    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.
    Descripción

    pc3

    Tipo de datos

    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.
    Descripción

    pc4

    Tipo de datos

    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.
    Descripción

    pc5

    Tipo de datos

    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.
    Descripción

    pc6

    Tipo de datos

    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.
    Descripción

    pc7

    Tipo de datos

    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.
    Descripción

    pc8

    Tipo de datos

    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.
    Descripción

    pc9

    Tipo de datos

    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.
    Descripción

    pc10

    Tipo de datos

    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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    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 formularios
    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
    Tipo
    Description | Question | Decode (Coded Value)
    Tipo de datos
    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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