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Sökning: WFRF:(Worrall Bradford B.)

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2.
  • Malik, R., et al. (författare)
  • Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes
  • 2018
  • Ingår i: Nature Genetics. - : NATURE PUBLISHING GROUP. - 1061-4036 .- 1546-1718. ; 50:D, Munich, Germany. [Chauhan, Ganesh] Indian Inst Sci, Ctr Brain Res, Bangalore, Karnataka, India. [Chauhan, Ganesh; Sargurupremraj, Muralidharan; Mishra, Aniket; Tzourio, Christophe; Debette, [Traylor, Matthew; Rutten-Jacobs, Loes; Markus, Hugh S.] Univ Cambridge, Div Clin Neurosci, Stroke [Sargurupremraj, Muralidharan; Mishra, Aniket; Debette, Stephanie] Bordeaux Univ Hosp, Inst [Okada, Yukinori; Kanai, Masahiro; Kamatani, Yoichiro] RIKEN Ctr Integrat Med Sci, Lab Stat Anal, [Okada, Yukinori; Kanai, Masahiro; Sakaue, Saori] Osaka Univ, Grad Sch Med, Dept Stat Genet, Osaka, [Okada, Yukinori] Osaka Univ, Immunol Frontier Res Ctr WPI IFReC, Lab Stat Immunol, Suita, Osaka, [Giese, Anne-Katrin; Rost, Natalia S.] Harvard Med Sch, MGH, Dept Neurol, Boston, MA USA. [van der Laan, Sander W.] Univ Utrecht, Univ Med Ctr Utrecht, Div Heart & Lungs, Lab Expt Cardiol,Dept [Gretarsdottir, Solveig; Thorleifsson, Gudmar; Thorsteinsdottir, Unnur; Stefansson, Kari] DeCODE Genet [Anderson, Christopher D.; Rosand, Jonathan] MGH, Ctr Genom Med, Boston, MA USA. [Anderson, Christopher D.; Ay, Hakan; Rost, Natalia S.; Rosand, Jonathan] MGH, J Philip Kistler Stroke [Anderson, Christopher D.; Rosand, Jonathan] Broad Inst, Program Med & Populat Genet, Cambridge, s. 524-
  • Tidskriftsartikel (refereegranskat)abstract
    • Stroke has multiple etiologies, but the underlying genes and pathways are largely unknown. We conducted a multiancestry genome-wide-association meta-analysis in 521,612 individuals (67,162 cases and 454,450 controls) and discovered 22 new stroke risk loci, bringing the total to 32. We further found shared genetic variation with related vascular traits, including blood pressure, cardiac traits, and venous thromboembolism, at individual loci (n = 18), and using genetic risk scores and linkage-disequilibrium-score regression. Several loci exhibited distinct association and pleiotropy patterns for etiological stroke sub-types. Eleven new susceptibility loci indicate mechanisms not previously implicated in stroke pathophysiology, with prioritization of risk variants and genes accomplished through bioinformatics analyses using extensive functional datasets. Stroke risk loci were significantly enriched in drug targets for antithrombotic therapy.
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3.
  • Roselli, Carolina, et al. (författare)
  • Multi-ethnic genome-wide association study for atrial fibrillation
  • 2018
  • Ingår i: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 50:9, s. 1225-1233
  • Tidskriftsartikel (refereegranskat)abstract
    • Atrial fibrillation (AF) affects more than 33 million individuals worldwide(1) and has a complex heritability(2). We conducted the largest meta-analysis of genome-wide association studies (GWAS) for AF to date, consisting of more than half a million individuals, including 65,446 with AF. In total, we identified 97 loci significantly associated with AF, including 67 that were novel in a combined-ancestry analysis, and 3 that were novel in a European-specific analysis. We sought to identify AF-associated genes at the GWAS loci by performing RNA-sequencing and expression quantitative trait locus analyses in 101 left atrial samples, the most relevant tissue for AF. We also performed transcriptome-wide analyses that identified 57 AF-associated genes, 42 of which overlap with GWAS loci. The identified loci implicate genes enriched within cardiac developmental, electrophysiological, contractile and structural pathways. These results extend our understanding of the biological pathways underlying AF and may facilitate the development of therapeutics for AF.
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4.
  • Soderholm, M., et al. (författare)
  • Genome-wide association meta-analysis of functional outcome after ischemic stroke
  • 2019
  • Ingår i: Neurology. - : American Academy of Neurology. - 0028-3878 .- 1526-632X. ; 92:12, s. E1271-E1283
  • Tidskriftsartikel (refereegranskat)abstract
    • Objective To discover common genetic variants associated with poststroke outcomes using a genome-wide association (GWA) study. Methods The study comprised 6,165 patients with ischemic stroke from 12 studies in Europe, the United States, and Australia included in the GISCOME (Genetics of Ischaemic Stroke Functional Outcome) network. The primary outcome was modified Rankin Scale score after 60 to 190 days, evaluated as 2 dichotomous variables (0-2 vs 3-6 and 0-1 vs 2-6) and subsequently as an ordinal variable. GWA analyses were performed in each study independently and results were meta-analyzed. Analyses were adjusted for age, sex, stroke severity (baseline NIH Stroke Scale score), and ancestry. The significance level was p < 5 x 10(-8). Results We identified one genetic variant associated with functional outcome with genome-wide significance (modified Rankin Scale scores 0-2 vs 3-6, p = 5.3 x 10(-9)). This intronic variant (rs1842681) in the LOC105372028 gene is a previously reported trans-expression quantitative trait locus for PPP1R21, which encodes a regulatory subunit of protein phosphatase 1. This ubiquitous phosphatase is implicated in brain functions such as brain plasticity. Several variants detected in this study demonstrated suggestive association with outcome (p < 10(-5)), some of which are within or near genes with experimental evidence of influence on ischemic stroke volume and/or brain recovery (e.g., NTN4, TEK, and PTCH1). Conclusions In this large GWA study on functional outcome after ischemic stroke, we report one significant variant and several variants with suggestive association to outcome 3 months after stroke onset with plausible mechanistic links to poststroke recovery. Future replication studies and exploration of potential functional mechanisms for identified genetic variants are warranted.
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5.
  • Wu, O., et al. (författare)
  • Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data
  • 2019
  • Ingår i: Stroke. - : American Heart Association. - 0039-2499 .- 1524-4628. ; 50:7, s. 1734-1741
  • Tidskriftsartikel (refereegranskat)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 (rho=0.92; P<0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm(3) (0.9-16.6 cm(3)). 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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6.
  • Schirmer, Markus D., et al. (författare)
  • White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts – The MRI-GENIE study
  • 2019
  • Ingår i: NeuroImage: Clinical. - : Elsevier. - 2213-1582. ; 23
  • Tidskriftsartikel (refereegranskat)abstract
    • White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94–0.95; p < 0.01) and Pearson correlation of total brain volume (r = 0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N = 2783) and identify a decrease in total brain volume of −2.4 cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited.
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7.
  • Ay, Hakan, et al. (författare)
  • Pathogenic Ischemic Stroke Phenotypes in the NINDS-Stroke Genetics Network
  • 2014
  • Ingår i: Stroke. - : American Heart Association. - 0039-2499. ; 45:12, s. 3589-3596
  • Tidskriftsartikel (refereegranskat)abstract
    • BACKGROUND AND PURPOSE: NINDS (National Institute of Neurological Disorders and Stroke)-SiGN (Stroke Genetics Network) is an international consortium of ischemic stroke studies that aims to generate high-quality phenotype data to identify the genetic basis of pathogenic stroke subtypes. This analysis characterizes the etiopathogenetic basis of ischemic stroke and reliability of stroke classification in the consortium. METHODS: Fifty-two trained and certified adjudicators determined both phenotypic (abnormal test findings categorized in major pathogenic groups without weighting toward the most likely cause) and causative ischemic stroke subtypes in 16 954 subjects with imaging-confirmed ischemic stroke from 12 US studies and 11 studies from 8 European countries using the web-based Causative Classification of Stroke System. Classification reliability was assessed with blinded readjudication of 1509 randomly selected cases. RESULTS: The distribution of pathogenic categories varied by study, age, sex, and race (P<0.001 for each). Overall, only 40% to 54% of cases with a given major ischemic stroke pathogenesis (phenotypic subtype) were classified into the same final causative category with high confidence. There was good agreement for both causative (κ 0.72; 95% confidence interval, 0.69-0.75) and phenotypic classifications (κ 0.73; 95% confidence interval, 0.70-0.75). CONCLUSIONS: This study demonstrates that pathogenic subtypes can be determined with good reliability in studies that include investigators with different expertise and background, institutions with different stroke evaluation protocols and geographic location, and patient populations with different epidemiological characteristics. The discordance between phenotypic and causative stroke subtypes highlights the fact that the presence of an abnormality in a patient with stroke does not necessarily mean that it is the cause of stroke.
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8.
  • Maguire, Jane M., et al. (författare)
  • GISCOME – Genetics of Ischaemic Stroke Functional Outcome network : A protocol for an international multicentre genetic association study
  • Ingår i: European Stroke Journal. - : SAGE Publications. - 2396-9873. ; 2:3, s. 229-237
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction: Genome-wide association studies have identified several novel genetic loci associated with stroke risk, but how genetic factors influence stroke outcome is less studied. The Genetics of Ischaemic Stroke Functional outcome network aims at performing genetic studies of stroke outcome. We here describe the study protocol and methods basis of Genetics of Ischaemic Stroke Functional outcome. Methods: The Genetics of Ischaemic Stroke Functional outcome network has assembled patients from 12 ischaemic stroke projects with genome-wide genotypic and outcome data from the International Stroke Genetics Consortium and the National Institute of Neurological Diseases Stroke Genetics Network initiatives. We have assessed the availability of baseline variables, outcome metrics and time-points for collection of outcome data. Results: We have collected 8831 ischaemic stroke cases with genotypic and outcome data. Modified Rankin score was the outcome metric most readily available. We detected heterogeneity between cohorts for age and initial stroke severity (according to the NIH Stroke Scale), and will take this into account in analyses. We intend to conduct a first phase genome-wide association outcome study on ischaemic stroke cases with data on initial stroke severity and modified Rankin score within 60–190 days. To date, we have assembled 5762 such cases and are currently seeking additional cases meeting these criteria for second phase analyses. Conclusion: Genetics of Ischaemic Stroke Functional outcome is a unique collection of ischaemic stroke cases with detailed genetic and outcome data providing an opportunity for discovery of genetic loci influencing functional outcome. Genetics of Ischaemic Stroke Functional outcome will serve as an exploratory study where the results as well as the methodological observations will provide a basis for future studies on functional outcome. Genetics of Ischaemic Stroke Functional outcome can also be used for candidate gene replication or assessing stroke outcome non-genetic association hypotheses.
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10.
  • Traylor, Matthew, et al. (författare)
  • Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE Collaboration): a meta-analysis of genome-wide association studies
  • 2012
  • Ingår i: Lancet Neurology. - : Lancet Ltd. - 1474-4465. ; 11:11, s. 951-962
  • Tidskriftsartikel (refereegranskat)abstract
    • Background Various genome-wide association studies (GWAS) have been done in ischaemic stroke, identifying a few loci associated with the disease, but sample sizes have been 3500 cases or less. We established the METASTROKE collaboration with the aim of validating associations from previous GWAS and identifying novel genetic associations through meta-analysis of GWAS datasets for ischaemic stroke and its subtypes. Methods We meta-analysed data from 15 ischaemic stroke cohorts with a total of 12 389 individuals with ischaemic stroke and 62 004 controls, all of European ancestry. For the associations reaching genome-wide significance in METASTROKE, we did a further analysis, conditioning on the lead single nudeotide polymorphism in every associated region. Replication of novel suggestive signals was done in 13 347 cases and 29 083 controls. Findings We verified previous associations for cardioembolic stroke near PITX2 (p=2.8x10(-16)) and ZFHX3 (p=2.28x10(-8)), and for large-vessel stroke at a 9p21 locus (p=3.32x10(-5)) and HDAC9 (p=2.03x10(-12)). Additionally, we verified that all associations were subtype specific. Conditional analysis in the three regions for which the associations reached genome-wide significance (PITX2, ZFHX3, and HDAC9) indicated that all the signal in each region could be attributed to one risk haplotype. We also identified 12 potentially novel loci at p<5x10(-6). However, we were unable to replicate any of these novel associations in the replication cohort. Interpretation Our results show that, although genetic variants can be detected in patients with ischaemic stroke when compared with controls, all associations we were able to confirm are specific to a stroke subtype. This finding has two implications. First, to maximise success of genetic studies in ischaemic stroke, detailed stroke subtyping is required. Second, different genetic pathophysiological mechanisms seem to be associated with different stroke subtypes.
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