ID
45400
Description
Principal Investigator: Steven Kittner, MD, University of Maryland School of Medicine, Baltimore, MD, USA MeSH: Cerebral infarction https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000615 The NINDS Stroke Genetics Network (SiGN) is a large international collaboration designed to detect genetic variants that predispose to subtypes of ischemic stroke. The study implements a genome wide association study (GWAS) methodology with all stroke cases undergoing phenotypic and stroke-subtype classification using the same web-based Causative Classification of Stroke (CCS) system, with data entered by trained and certified adjudicators at participating Research Centers (GRC's). SiGN includes ischemic stroke cases from 24 GRC's, 13 from the US and 11 from Europe. Each GRC has access to well-characterized ischemic stroke cases in which extensive phenotype data and high-quality DNA was available. Genome-wide data was available for many cases and for those without, new genome-wide genotyping, including exome chip genotyping of rare variants, was done through the Center for Inherited Diseases Research (CIDR). To maximize power for subtype analyses, genotyping resources were allocated almost exclusively to cases. With few exceptions, controls were drawn from studies with publicly available genome-wide data.
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Versions (2)
- 11/15/22 11/15/22 - Simon Heim
- 1/29/25 1/29/25 - Akane Nishihara
Copyright Holder
Steven Kittner, MD, University of Maryland School of Medicine, Baltimore, MD, USA
Uploaded on
November 15, 2022
DOI
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License
Creative Commons BY 4.0
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dbGaP phs000615 NINDS Stroke Genetics Network (SiGN)
Eligibility Criteria
- StudyEvent: SEV1
- Eligibility Criteria
- Subject - Consent Information
- Subject - Sample Mapping
- The dataset includes information about diagnostic criteria applied (e.g. TOAST, CCS classification assignment), medical history (e.g. number of strokes experienced, history of hypertension, diabetes mellitus, atrial fibrillation), smoking status, and general sociodemographic information.
- Sample - Attribute Information
Similar models
Eligibility Criteria
- StudyEvent: SEV1
- Eligibility Criteria
- Subject - Consent Information
- Subject - Sample Mapping
- The dataset includes information about diagnostic criteria applied (e.g. TOAST, CCS classification assignment), medical history (e.g. number of strokes experienced, history of hypertension, diabetes mellitus, atrial fibrillation), smoking status, and general sociodemographic information.
- Sample - Attribute Information
C0680251 (UMLS CUI [1,2])
C0948008 (UMLS CUI [1,2])
C0412675 (UMLS CUI [1,2])
C1541923 (UMLS CUI [1,2])
C1947920 (UMLS CUI [1,3])
C0518959 (UMLS CUI [1,4])
C0012739 (UMLS CUI [1,5])
C0238051 (UMLS CUI [1,6])
C0151945 (UMLS CUI [1,7])
C0272285 (UMLS CUI [1,8])
C2956641 (UMLS CUI [1,9])
C0162671 (UMLS CUI [1,10])
C0025289 (UMLS CUI [1,11])
C0340669 (UMLS CUI [1,12])
C0002895 (UMLS CUI [1,13])
C0020732 (UMLS CUI [1,14])