4.7 Article

A genome-wide genotyping study in patients with ischaemic stroke:: initial analysis and data release

Journal

LANCET NEUROLOGY
Volume 6, Issue 5, Pages 414-420

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/S1474-4422(07)70081-9

Keywords

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Funding

  1. Intramural NIH HHS Funding Source: Medline
  2. Medical Research Council [G0701075] Funding Source: Medline
  3. NINDS NIH HHS [R01 NS042733, R01 NS42733, K08 NS045802-01A2, K08 NS045802] Funding Source: Medline
  4. MRC [G0701075] Funding Source: UKRI
  5. Medical Research Council [G0701075] Funding Source: researchfish

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Background Despite evidence of a genetic role in stroke, the identification of common genetic risk factors for this devastating disorder remains problematic. We aimed to identify any common genetic variability exerting a moderate to large effect on risk of ischaemic stroke, and to generate publicly available genome-wide genotype data to facilitate others doing the same. Methods We applied a genome-wide high-density single-nucleotide-polymorphism (SNP) genotyping approach to a cohort of samples with and without ischaemic stroke (n=278 and 275, respectively), and did an association analysis adjusted for known confounders in a final cohort of 249 cases and 268 controls. More than 400 000 unique SNPs were assayed. Findings We produced more than 200 million genotypes in 553 unique participants. The raw genotypes of all the controls have been posted publicly in a previous study of Parkinson's disease. From this effort, results of genotype and allele association tests have been publicly posted for 88% of stroke patients who provided proper consent for public release. Preliminary analysis of these data did not reveal any single locus conferring a large effect on risk for ischaemic stroke. Interpretation The data generated here comprise the first phase of a genome-wide association analysis in patients with stroke. Release of phase I results generated in these publicly available samples from each consenting individual makes this dataset a valuable resource for data-mining and augmentation.

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