Dataset Information

Stroke Genetics Network (SiGN) study: design and rationale for a genome-wide association study of ischemic stroke subtypes.

ABSTRACT: BACKGROUND AND PURPOSE:Meta-analyses of extant genome-wide data illustrate the need to focus on subtypes of ischemic stroke for gene discovery. The National Institute of Neurological Disorders and Stroke SiGN (Stroke Genetics Network) contributes substantially to meta-analyses that focus on specific subtypes of stroke. METHODS:The National Institute of Neurological Disorders and Stroke SiGN includes ischemic stroke cases from 24 genetic research centers: 13 from the United States and 11 from Europe. Investigators harmonize ischemic stroke phenotyping using the Web-based causative classification of stroke system, with data entered by trained and certified adjudicators at participating genetic research centers. Through the Center for Inherited Diseases Research, the Network plans to genotype 10,296 carefully phenotyped stroke cases using genome-wide single nucleotide polymorphism arrays and adds to these another 4253 previously genotyped cases, for a total of 14,549 cases. To maximize power for subtype analyses, the study allocates genotyping resources almost exclusively to cases. Publicly available studies provide most of the control genotypes. Center for Inherited Diseases Research-generated genotypes and corresponding phenotypes will be shared with the scientific community through the US National Center for Biotechnology Information database of Genotypes and Phenotypes, and brain MRI studies will be centrally archived. CONCLUSIONS:The Stroke Genetics Network, with its emphasis on careful and standardized phenotyping of ischemic stroke and stroke subtypes, provides an unprecedented opportunity to uncover genetic determinants of ischemic stroke.

SUBMITTER: Meschia JF

PROVIDER: S-EPMC4056331 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Stroke Genetics Network (SiGN) study: design and rationale for a genome-wide association study of ischemic stroke subtypes.

Meschia James F JF Arnett Donna K DK Ay Hakan H Brown Robert D RD Benavente Oscar R OR Cole John W JW de Bakker Paul I W PI Dichgans Martin M Doheny Kimberly F KF Fornage Myriam M Grewal Raji P RP Gwinn Katrina K Jern Christina C Conde Jordi Jimenez JJ Johnson Julie A JA Jood Katarina K Laurie Cathy C CC Lee Jin-Moo JM Lindgren Arne A Markus Hugh S HS McArdle Patrick F PF McClure Leslie A LA Mitchell Braxton D BD Schmidt Reinhold R Rexrode Kathryn M KM Rich Stephen S SS Rosand Jonathan J Rothwell Peter M PM Rundek Tatjana T Sacco Ralph L RL Sharma Pankaj P Shuldiner Alan R AR Slowik Agnieszka A Wassertheil-Smoller Sylvia S Sudlow Cathie C Thijs Vincent N S VN Woo Daniel D Worrall Bradford B BB Wu Ona O Kittner Steven J SJ

Stroke 20130910 10

<h4>Background and purpose</h4>Meta-analyses of extant genome-wide data illustrate the need to focus on subtypes of ischemic stroke for gene discovery. The National Institute of Neurological Disorders and Stroke SiGN (Stroke Genetics Network) contributes substantially to meta-analyses that focus on specific subtypes of stroke.<h4>Methods</h4>The National Institute of Neurological Disorders and Stroke SiGN includes ischemic stroke cases from 24 genetic research centers: 13 from the United States ...[more]

PMID: 24021684

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Project description:Genome-wide association studies (GWAS) are widely applied to identify susceptibility loci for a variety of diseases using genotyping arrays that interrogate known polymorphisms throughout the genome. A particular strength of GWAS is that it is unbiased with respect to specific genomic elements (e.g., coding or regulatory regions of genes), and it has revealed important associations that would have never been suspected based on prior knowledge or assumptions. To date, the discovered SNPs associated with complex human traits tend to have small effect sizes, requiring very large sample sizes to achieve robust statistical power. To address these issues, a number of efficient strategies have emerged for conducting GWAS, including combining study results across multiple studies using meta-analysis, collecting cases through electronic health records, and using samples collected from other studies as controls that have already been genotyped and made publicly available (e.g., through deposition of de-identified data into dbGaP or EGA). In certain scenarios, it may be attractive to use already genotyped controls and divert resources to standardized collection, phenotyping, and genotyping of cases only. This strategy, however, requires that careful attention be paid to the choice of "public controls" and to the comparability of genetic data between cases and the public controls to ensure that any allele frequency differences observed between groups is attributable to locus-specific effects rather than to a systematic bias due to poor matching (population stratification) or differential genotype calling (batch effects). The goal of this paper is to describe some of the potential pitfalls in using previously genotyped control data. We focus on considerations related to the choice of control groups, the use of different genotyping platforms, and approaches to deal with population stratification when cases and controls are genotyped across different platforms.

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Dataset Information

Stroke Genetics Network (SiGN) study: design and rationale for a genome-wide association study of ischemic stroke subtypes.

Publications

Stroke Genetics Network (SiGN) study: design and rationale for a genome-wide association study of ischemic stroke subtypes.

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