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Sefanja Achterberg

Researcher at Utrecht University

Publications -  22
Citations -  1114

Sefanja Achterberg is an academic researcher from Utrecht University. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 9, co-authored 13 publications receiving 933 citations.

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Journal ArticleDOI

Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE collaboration): a meta-analysis of genome-wide association studies.

Matthew Traylor, +91 more
- 01 Nov 2012 - 
TL;DR: The results show that, although genetic variants can be detected in patients with ischaemic stroke when compared with controls, all associations validated are specific to a stroke subtype, and this finding has two implications.
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Loci associated with ischaemic stroke and its subtypes (SiGN) : A genome-wide association study

Sara L. Pulit, +202 more
- 01 Feb 2016 - 
TL;DR: A novel locus (G allele at rs12122341) at 1p13.2 near TSPAN2 that was associated with large artery atherosclerosis-related stroke was identified and supported robust associations with ischaemic stroke for four other loci that have been reported in previous studies.
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Asymptomatic Carotid Artery Stenosis and the Risk of Ischemic Stroke According to Subtype in Patients With Clinical Manifest Arterial Disease

TL;DR: Patients with clinically manifest arterial disease or type 2 diabetes mellitus have a low risk of developing ischemic stroke, irrespective of its subtype and independent of the degree of ACAS stenosis.
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Meta-analysis in more than 17,900 cases of ischemic stroke reveals a novel association at 12q24.12

Laura L. Kilarski, +54 more
- 19 Aug 2014 - 
TL;DR: A novel association on chromosome 12q24 (rs10744777, odds ratio [OR] 1.10 [1.07–1.13], p = 7.12 × 10−11) was identified with ischemic stroke rather than an individual stroke subtype, with similar effect sizes seen in different stroke subtypes.
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A novel MMP12 locus is associated with large artery atherosclerotic stroke using a genome-wide age-at-onset informed approach.

TL;DR: The results show that a covariate-informed design, by adjusting for age-at-onset of stroke, can detect variants not identified by conventional GWAS.