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- Cheng, Yu-Ching, et al.
(author)
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Genome-Wide Association Analysis of Young-Onset Stroke Identifies a Locus on Chromosome 10q25 Near HABP2.
- 2016
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In: Stroke; a journal of cerebral circulation. - 1524-4628. ; 47:2, s. 307-16
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Journal article (peer-reviewed)abstract
- Although a genetic contribution to ischemic stroke is well recognized, only a handful of stroke loci have been identified by large-scale genetic association studies to date. Hypothesizing that genetic effects might be stronger for early- versus late-onset stroke, we conducted a 2-stage meta-analysis of genome-wide association studies, focusing on stroke cases with an age of onset <60 years.
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2. |
- Cole, John W, et al.
(author)
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Genetics of the thrombomodulin-endothelial cell protein C receptor system and the risk of early-onset ischemic stroke.
- 2018
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In: PloS one. - : Public Library of Science (PLoS). - 1932-6203. ; 13:11
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Journal article (peer-reviewed)abstract
- Polymorphisms in coagulation genes have been associated with early-onset ischemic stroke. Here we pursue an a priori hypothesis that genetic variation in the endothelial-based receptors of the thrombomodulin-protein C system (THBD and PROCR) may similarly be associated with early-onset ischemic stroke. We explored this hypothesis utilizing a multi-stage design of discovery and replication.Discovery was performed in the Genetics-of-Early-Onset Stroke (GEOS) Study, a biracial population-based case-control study of ischemic stroke among men and women aged 15-49 including 829 cases of first ischemic stroke (42.2% African-American) and 850 age-comparable stroke-free controls (38.1% African-American). Twenty-four single-nucleotide-polymorphisms (SNPs) in THBD and 22 SNPs in PROCR were evaluated. Following LD pruning (r2≥0.8), we advanced uncorrelated SNPs forward for association analyses. Associated SNPs were evaluated for replication in an early-onset ischemic stroke population (onset-age<60 years) consisting of 3676 cases and 21118 non-stroke controls from 6 case-control studies. Lastly, we determined if the replicated SNPs also associated with older-onset ischemic stroke in the METASTROKE data-base.Among GEOS Caucasians, PROCR rs9574, which was in strong LD with 8 other SNPs, and one additional independent SNP rs2069951, were significantly associated with ischemic stroke (rs9574, OR = 1.33, p = 0.003; rs2069951, OR = 1.80, p = 0.006) using an additive-model adjusting for age, gender and population-structure. Adjusting for risk factors did not change the associations; however, associations were strengthened among those without risk factors. PROCR rs9574 also associated with early-onset ischemic stroke in the replication sample (OR = 1.08, p = 0.015), but not older-onset stroke. There were no PROCR associations in African-Americans, nor were there any THBD associations in either ethnicity.PROCR polymorphisms are associated with early-onset ischemic stroke in Caucasians.
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3. |
- Giese, Anne Katrin, et al.
(author)
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Design and rationale for examining neuroimaging genetics in ischemic stroke : The MRI-GENIE study
- 2017
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In: Neurology: Genetics. - 2376-7839. ; 3:5
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Journal article (peer-reviewed)abstract
- Objective: To describe the design and rationale for the genetic analysis of acute and chronic cerebrovascular neuroimaging phenotypes detected on clinical MRI in patients with acute ischemic stroke (AIS) within the scope of the MRI-GENetics Interface Exploration (MRI-GENIE) study. Methods: MRI-GENIE capitalizes on the existing infrastructure of the Stroke Genetics Network (SiGN). In total, 12 international SiGN sites contributedMRIs of 3,301 patients with AIS. Detailed clinical phenotyping with the web-based Causative Classification of Stroke (CCS) system and genome-wide genotyping data were available for all participants. Neuroimaging analyses include themanual and automated assessments of established MRI markers. A high-throughputMRI analysis pipeline for the automated assessment of cerebrovascular lesions on clinical scans will be developed in a subset of scans for both acute and chronic lesions, validated against gold standard, and applied to all available scans. The extracted neuroimaging phenotypes will improve characterization of acute and chronic cerebrovascular lesions in ischemic stroke, including CCS subtypes, and their effect on functional outcomes after stroke. Moreover, genetic testing will uncover variants associated with acute and chronic MRI manifestations of cerebrovascular disease.Conclusions: The MRI-GENIE study aims to develop, validate, and distribute the MRI analysis platform for scans acquired as part of clinical care for patients with AIS, which will lead to (1) novel genetic discoveries in ischemic stroke, (2) strategies for personalized stroke risk assessment, and (3) personalized stroke outcome assessment.
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4. |
- Giese, Anne Katrin, et al.
(author)
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White matter hyperintensity burden in acute stroke patients differs by ischemic stroke subtype
- 2020
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In: Neurology. - 0028-3878. ; 95:1, s. 79-88
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Journal article (peer-reviewed)abstract
- ObjectiveTo examine etiologic stroke subtypes and vascular risk factor profiles and their association with white matter hyperintensity (WMH) burden in patients hospitalized for acute ischemic stroke (AIS).MethodsFor the MRI Genetics Interface Exploration (MRI-GENIE) study, we systematically assembled brain imaging and phenotypic data for 3,301 patients with AIS. All cases underwent standardized web tool-based stroke subtyping with the Causative Classification of Ischemic Stroke (CCS). WMH volume (WMHv) was measured on T2 brain MRI scans of 2,529 patients with a fully automated deep-learning trained algorithm. Univariable and multivariable linear mixed-effects modeling was carried out to investigate the relationship of vascular risk factors with WMHv and CCS subtypes.ResultsPatients with AIS with large artery atherosclerosis, major cardioembolic stroke, small artery occlusion (SAO), other, and undetermined causes of AIS differed significantly in their vascular risk factor profile (all p < 0.001). Median WMHv in all patients with AIS was 5.86 cm3 (interquartile range 2.18-14.61 cm3) and differed significantly across CCS subtypes (p < 0.0001). In multivariable analysis, age, hypertension, prior stroke, smoking (all p < 0.001), and diabetes mellitus (p = 0.041) were independent predictors of WMHv. When adjusted for confounders, patients with SAO had significantly higher WMHv compared to those with all other stroke subtypes (p < 0.001).ConclusionIn this international multicenter, hospital-based cohort of patients with AIS, we demonstrate that vascular risk factor profiles and extent of WMH burden differ by CCS subtype, with the highest lesion burden detected in patients with SAO. These findings further support the small vessel hypothesis of WMH lesions detected on brain MRI of patients with ischemic stroke.
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5. |
- Traylor, Matthew, et al.
(author)
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Genetic Variation at 16q24.2 is associated with small vessel stroke.
- 2017
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In: Annals of neurology. - : Wiley. - 1531-8249 .- 0364-5134. ; 81:3, s. 383-394
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Journal article (peer-reviewed)abstract
- Genome-wide association studies (GWAS) have been successful at identifying associations with stroke and stroke subtypes, but have not yet identified any associations solely with small vessel stroke (SVS). SVS comprises a quarter of all ischaemic stroke and is a major manifestation of cerebral small vessel disease, the primary cause of vascular cognitive impairment. Studies across neurological traits have shown younger onset cases have an increased genetic burden. We leveraged this increased genetic burden by performing an age-at-onset informed GWAS meta-analysis, including a large younger onset SVS population, to identify novel associations with stroke.We used a three-stage age-at-onset informed GWAS to identify novel genetic variants associated with stroke. On identifying a novel locus associated with SVS, we assessed its influence on other small vessel disease phenotypes, as well as on mRNA expression of nearby genes, and on DNA methylation of nearby CpG sites in whole blood and in the fetal brain.We identified an association with SVS in 4,203 cases and 50,728 controls on chromosome 16q24.2 (OR(95% CI)=1.16(1.10-1.22); p=3.2x10(-9) ). The lead SNP (rs12445022) was also associated with cerebral white matter hyperintensities (OR(95% CI)=1.10(1.05-1.16); p=5.3x10(-5) ; N=3,670), but not intracerebral haemorrhage (OR(95% CI)=0.97(0.84-1.12); p=0.71; 1,545 cases, 1,481 controls). rs12445022 is associated with mRNA expression of ZCCHC14 in arterial tissues (p=9.4x10(-7) ), and DNA methylation at probe cg16596957 in whole blood (p=5.3x10(-6) ).16q24.2 is associated with SVS. Associations of the locus with expression of ZCCHC14 and DNA methylation suggest the locus acts through changes to regulatory elements. This article is protected by copyright. All rights reserved.
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6. |
- Wu, Ona, et al.
(author)
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Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data
- 2019
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In: Stroke. - 1524-4628. ; 50:7, s. 1734-1741
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Journal article (peer-reviewed)abstract
- Background and Purpose- We evaluated deep learning algorithms' segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods- Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms' performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results- The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P<0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm3 (0.9-16.6 cm3). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P<0.0001) and different topography compared with other stroke subtypes. Conclusions- Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.
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