ID

45055

Description

Principal Investigator: Michele M. Sale, PhD, University of Virginia, Charlottesville, VA, USA MeSH: Type 2 Diabetes Mellitus,Dyslipidemia https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000433 Recent genome-wide association studies (GWAS) have successfully identified genetic variants that influence diabetes risk in European populations, however most do not have a major impact on diabetes risk in populations of African descent. The African American (AA) population from the Sea Islands of coastal South Carolina and Georgia has high rates of type 2 diabetes, low levels of admixture, and in general, consume a diet rich in saturated fats. We postulate that this unique combination of ancestral and environmental factors results in a more consistent penetrance of diabetes risk alleles, as well as enrichment of risk alleles of African origin. The existing DNA samples and rich phenotypic data from the Sea Island Families Project comprise a unique resource for genetic studies of type 2 diabetes and related metabolic traits such as dyslipidemia. Our central hypothesis is that the increased risk for T2DM in AA compared with European American (EA) is due, in part, to susceptibility alleles of African origin, and that these alleles can be identified using a GWAS. The Specific Aims are to: 1) Identify genetic risk factors for type 2 diabetes utilizing DNA samples and data from the Sea Island Families Project, Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study recruited from SC, GA, NC, and AL; and a GWAS approach; 2) Identify genetic contributors to lipoprotein subclasses in African Americans using the lipoprotein subclass profile (particle size and concentration for multiple subclasses of VLDL, LDL, and HDL) assessed by NMR at LipoScience, Inc., and the GWAS data from Aim 1. The rationale for this project is that identification and validation of novel pathophysiological pathways and informed selection of candidate genes for diabetes risk will inform development of new, targeted prevention and treatment strategies in this underserved, high risk population.

Link

https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000433

Keywords

  1. 8/4/22 8/4/22 - Chiara Middel
  2. 10/12/22 10/12/22 - Adrian Schulz
Copyright Holder

Michele M. Sale, PhD, University of Virginia, Charlottesville, VA, USA

Uploaded on

August 4, 2022

DOI

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License

Creative Commons BY 4.0

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dbGaP phs000433 The Sea Islands Genetic Network (SIGNET)

The sample attributes data table includes sample type, body site where sample was extracted, sample analyte type and additional comments to indicate rela duplicate samples genotyped for QC.

pht002437
Description

pht002437

De-identified sample ID
Description

SAMPID

Data type

string

Alias
UMLS CUI [1,1]
C4684638
UMLS CUI [1,2]
C1299222
Body site where sample was collected
Description

BODY_SITE

Data type

string

Alias
UMLS CUI [1,1]
C1515974
UMLS CUI [1,2]
C0200345
Analyte type
Description

ANALYTE_TYPE

Data type

string

Alias
UMLS CUI [1,1]
C4744818
Tumor status
Description

IS_TUMOR

Data type

text

Alias
UMLS CUI [1,1]
C0475752

Similar models

The sample attributes data table includes sample type, body site where sample was extracted, sample analyte type and additional comments to indicate rela duplicate samples genotyped for QC.

Name
Type
Description | Question | Decode (Coded Value)
Data type
Alias
Item Group
pht002437
SAMPID
Item
De-identified sample ID
string
C4684638 (UMLS CUI [1,1])
C1299222 (UMLS CUI [1,2])
BODY_SITE
Item
Body site where sample was collected
string
C1515974 (UMLS CUI [1,1])
C0200345 (UMLS CUI [1,2])
ANALYTE_TYPE
Item
Analyte type
string
C4744818 (UMLS CUI [1,1])
Item
Tumor status
text
C0475752 (UMLS CUI [1,1])
Code List
Tumor status
CL Item
No (N)
CL Item
Yes (Y)

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