0 Ratings
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

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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, 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.

    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.
    pht000630
    Description

    pht000630

    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
    Body Mass Index
    Description

    BMI

    Data type

    text

    Measurement units
    • kg/m2
    Alias
    UMLS CUI [1,1]
    C1305855
    kg/m2
    Consent form(s) signed by participant
    Description

    Consent

    Data type

    text

    Alias
    UMLS CUI [1,1]
    C0009797
    UMLS CUI [1,2]
    C0742766
    Age of T2DM (Type 2 Diabetes Mellitus) diagnosis
    Description

    T2DM Age of Onset

    Data type

    text

    Measurement units
    • Years
    Alias
    UMLS CUI [1,1]
    C1828181
    UMLS CUI [1,2]
    C4014362
    Years
    Age of ESRD (End Stage Renal Disease) diagnosis, start of dialysis
    Description

    ESRD Age of Onset

    Data type

    text

    Measurement units
    • Years
    Alias
    UMLS CUI [1,1]
    C1828181
    UMLS CUI [1,2]
    C0022661
    UMLS CUI [1,3]
    C0011946
    UMLS CUI [1,4]
    C0439659
    Years
    Age of hypertension diagnosis
    Description

    HTN Age of Onset

    Data type

    text

    Measurement units
    • Years
    Alias
    UMLS CUI [1,1]
    C1828181
    UMLS CUI [1,2]
    C0020538
    Years
    Duration of T2DM (Type 2 Diabetes Mellitus) since diagnosis
    Description

    Duration T2DM

    Data type

    text

    Measurement units
    • Years
    Alias
    UMLS CUI [1,1]
    C0872031
    UMLS CUI [1,2]
    C4014362
    Years
    Individual has reported that he/she is on dialysis
    Description

    ESRD

    Data type

    text

    Alias
    UMLS CUI [1,1]
    C0681906
    UMLS CUI [1,2]
    C0011946
    Individual has self reported hypertension (yes, no, unknown)
    Description

    HTN

    Data type

    text

    Alias
    UMLS CUI [1,1]
    C0681906
    UMLS CUI [1,2]
    C0020538
    Individual has diabetic retinopathy reported by ophthalmologist or laser treatment with photocoagulation
    Description

    Retinopathy

    Data type

    text

    Alias
    UMLS CUI [1,1]
    C0011884
    UMLS CUI [1,2]
    C1704292
    UMLS CUI [1,3]
    C0700287
    UMLS CUI [2,1]
    C0011884
    UMLS CUI [2,2]
    C0441510
    Hemoglobin1AC, test
    Description

    HBA1C

    Data type

    text

    Measurement units
    • %
    Alias
    UMLS CUI [1,1]
    C0850989
    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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    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.

    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
    pht000630
    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])
    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])
    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
    BG91-0129, Deceased participant (A,X)
    C0009797 (UMLS CUI [1,1])
    C1555024 (UMLS CUI [1,2])
    CL Item
    BG92-0090 (B)
    C0009797 (UMLS CUI [1,1])
    CL Item
    BG92-0090, Deceased participant (B,X)
    C0009797 (UMLS CUI [1,1])
    C1555024 (UMLS CUI [1,2])
    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_001 (E_001)
    C0009797 (UMLS CUI [1,1])
    CL Item
    BG00-427_001, Reconsent form (E_001, H)
    C0009797 (UMLS CUI [1,1])
    CL Item
    BG00-427_001, Reconsent form (E_001,H)
    C0009797 (UMLS CUI [1,1])
    CL Item
    BG00-427_001, Deceased participant (E_001,X)
    C0009797 (UMLS CUI [1,1])
    C1555024 (UMLS CUI [1,2])
    CL Item
    BG00-427_002 (E_002)
    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
    BG01-299-001, Deceased participant (G_001,X)
    C0009797 (UMLS CUI [1,1])
    C1555024 (UMLS CUI [1,2])
    CL Item
    BG01-299-002 (G_002)
    C0009797 (UMLS CUI [1,1])
    CL Item
    BG01-299-002, Reconsent form (G_002, H)
    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])
    T2DM Age of Onset
    Item
    Age of T2DM (Type 2 Diabetes Mellitus) diagnosis
    text
    C1828181 (UMLS CUI [1,1])
    C4014362 (UMLS CUI [1,2])
    ESRD Age of Onset
    Item
    Age of ESRD (End Stage Renal Disease) diagnosis, start of dialysis
    text
    C1828181 (UMLS CUI [1,1])
    C0022661 (UMLS CUI [1,2])
    C0011946 (UMLS CUI [1,3])
    C0439659 (UMLS CUI [1,4])
    HTN Age of Onset
    Item
    Age of hypertension diagnosis
    text
    C1828181 (UMLS CUI [1,1])
    C0020538 (UMLS CUI [1,2])
    Duration T2DM
    Item
    Duration of T2DM (Type 2 Diabetes Mellitus) since diagnosis
    text
    C0872031 (UMLS CUI [1,1])
    C4014362 (UMLS CUI [1,2])
    Item
    Individual has reported that he/she is on dialysis
    text
    C0681906 (UMLS CUI [1,1])
    C0011946 (UMLS CUI [1,2])
    ESRD
    Code List
    Individual has reported that he/she is on dialysis
    CL Item
    Yes (ESRD)
    C1705108 (UMLS CUI [1,1])
    CL Item
    No (no)
    C1298908 (UMLS CUI [1,1])
    CL Item
    Yes (yes)
    C1705108 (UMLS CUI [1,1])
    Item
    Individual has self reported hypertension (yes, no, unknown)
    text
    C0681906 (UMLS CUI [1,1])
    C0020538 (UMLS CUI [1,2])
    HTN
    Code List
    Individual has self reported hypertension (yes, no, unknown)
    CL Item
    Yes (HTN)
    C1705108 (UMLS CUI [1,1])
    CL Item
    No (no)
    C1298908 (UMLS CUI [1,1])
    CL Item
    Unknown (unknown)
    C0439673 (UMLS CUI [1,1])
    CL Item
    Yes (yes)
    C1705108 (UMLS CUI [1,1])
    Retinopathy
    Item
    Individual has diabetic retinopathy reported by ophthalmologist or laser treatment with photocoagulation
    text
    C0011884 (UMLS CUI [1,1])
    C1704292 (UMLS CUI [1,2])
    C0700287 (UMLS CUI [1,3])
    C0011884 (UMLS CUI [2,1])
    C0441510 (UMLS CUI [2,2])
    Item
    Hemoglobin1AC, test
    text
    C0850989 (UMLS CUI [1,1])
    HBA1C
    Code List
    Hemoglobin1AC, test
    CL Item
    Unknown (.)
    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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