Ischemic stroke is the 4th leading cause of death in the U.S. and a major cause of disability. The etiology of stroke is multifactorial and poorly understood. Genetics is a potentially powerful tool for better understanding disease etiology as it can highlight biological mechanisms underlying disease and point the way to improved prevention and treatment. The NINDS-sponsored Stroke Genetics Network (SiGN) was formed in 2010 to identify common genetic variants associated with ischemic stroke and its subtypes using state-of-the-art stroke subtyping and the genome-wide association study approach in over 17,000 stroke cases from 25 sites. The initial meta-analysis, just completed, reveals several tantalizing results and a large number of additional analyses are planned by SiGN investigators. The goal of this application is to secure support for additional key analyses to be carried out on this unique resource and to make this resource available broadly to the international research community. Using already available genotype data, we will (1) Discover rare and low-frequency variants influencing stroke susceptibility by performing association analyses of low-frequency and exonic SNPs with ischemic stroke and its subtypes in 10,348 stroke cases and controls; (2) Identify the likely causal genes and variants at 7 established loci that have been robustly associated with ischemic stroke subtypes using integrative approaches to fine map these regions and define sets of credible causal variants; and (3) Contrast the nature of the genetic architecture among stroke subtypes between stroke and its risk factors using quantitative genetic analyses that partition SNPs across the genome into different bins, or categories, allowing us to test a variety of different hypotheses about the genetic architecture of stroke subtypes. A further goal of our proposal is to advance the pace of genetic discovery in stroke by making the SiGN resource (data and results) accessible to the stroke genetics community on a secure platform at the Broad Institute managed by the NINDS-funded Platform for Accelerating Genetic Discovery for Cerebrovascular Disease. As part of this effort we will create web-based look-up tools that allow investigators to query the meta- analysis results. In a very short period of time, SiGN has emerged as a unifying force in the stroke genetics field because of its inclusion of the leading stroke genetics groups internationally. The analyses and activities proposed in this application are key next steps that take advantage of this extraordinarily rich resource and set in place a mechanism for keeping this productive international consortium together and moving forward.

Public Health Relevance

The NINDS-sponsored Stroke Genetics Network (SiGN) was formed in 2010 to identify common genetic variants associated with ischemic stroke and its subtypes using state-of-the-art stroke subtyping and the genome-wide association study approach in over 17,000 stroke cases from 25 sites. This application is to secure support for additional key analyses to be carried out in this unique resource, including an analysis of rare variants associated with stroke subtypes, and to make the entire SiGN resource (data and results) accessible to the stroke genetics community on a secure platform at the Broad Institute managed by the NINDS-funded Platform for Accelerating Genetic Discovery for Cerebrovascular Disease.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS100178-03
Application #
9654796
Study Section
Neurological, Aging and Musculoskeletal Epidemiology (NAME)
Program Officer
Bosetti, Francesca
Project Start
2017-06-01
Project End
2022-02-28
Budget Start
2019-03-01
Budget End
2020-02-29
Support Year
3
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of Maryland Baltimore
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
188435911
City
Baltimore
State
MD
Country
United States
Zip Code
21201
Wassertheil-Smoller, Sylvia; Qi, Qibin; Dave, Tushar et al. (2018) Polygenic Risk for Depression Increases Risk of Ischemic Stroke: From the Stroke Genetics Network Study. Stroke 49:543-548
Grond-Ginsbach, Caspar; Erhart, Philipp; Chen, Bowang et al. (2018) Copy Number Variation and Risk of Stroke. Stroke 49:2549-2554
So, Conan; Chaudhry, Naveed; Gandhi, Dheeraj et al. (2018) Endovascular Thrombectomy in Acute-Onset Ischemic Stroke - beyond the Standard Time Windows: A Case Report and a Review of the Literature. Case Rep Neurol 10:279-285