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      DNA methylation and stroke prognosis: an epigenome-wide association study

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          Abstract

          Background and aims

          Stroke is the leading cause of adult-onset disability. Although clinical factors influence stroke outcome, there is a significant variability among individuals that may be attributed to genetics and epigenetics, including DNA methylation (DNAm). We aimed to study the association between DNAm and stroke prognosis.

          Methods and results

          To that aim, we conducted a two-phase study (discovery-replication and meta-analysis) in Caucasian patients with ischemic stroke from two independent centers (BasicMar [discovery, N = 316] and St. Pau [replication, N = 92]). Functional outcome was assessed using the modified Rankin Scale (mRS) at three months after stroke, being poor outcome defined as mRS > 2. DNAm was determined using the 450K and EPIC BeadChips in whole-blood samples collected within the first 24 h. We searched for differentially methylated positions (DMPs) in 370,344 CpGs, and candidates below p-value < 10 –5 were subsequently tested in the replication cohort. We then meta-analyzed DMP results from both cohorts and used them to identify differentially methylated regions (DMRs).

          After doing the epigenome-wide association study, we found 29 DMPs at p-value < 10 –5 and one of them was replicated: cg24391982, annotated to thrombospondin-2 ( THBS2) gene ( p-value discovery = 1.54·10 –6; p-value replication = 9.17·10 –4; p-value meta-analysis = 6.39·10 –9). Besides, four DMRs were identified in patients with poor outcome annotated to zinc finger protein 57 homolog ( ZFP57), Arachidonate 12-Lipoxygenase 12S Type ( ALOX12), ABI Family Member 3 ( ABI3) and Allantoicase ( ALLC) genes ( p-value < 1·10 –9 in all cases).

          Discussion

          Patients with poor outcome showed a DMP at THBS2 and four DMRs annotated to ZFP57, ALOX12, ABI3 and ALLC genes. This suggests an association between stroke outcome and DNAm, which may help identify new stroke recovery mechanisms.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13148-024-01690-2.

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          Most cited references36

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          METAL: fast and efficient meta-analysis of genomewide association scans

          Summary: METAL provides a computationally efficient tool for meta-analysis of genome-wide association scans, which is a commonly used approach for improving power complex traits gene mapping studies. METAL provides a rich scripting interface and implements efficient memory management to allow analyses of very large data sets and to support a variety of input file formats. Availability and implementation: METAL, including source code, documentation, examples, and executables, is available at http://www.sph.umich.edu/csg/abecasis/metal/ Contact: goncalo@umich.edu
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            Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays.

            The recently released Infinium HumanMethylation450 array (the '450k' array) provides a high-throughput assay to quantify DNA methylation (DNAm) at ∼450 000 loci across a range of genomic features. Although less comprehensive than high-throughput sequencing-based techniques, this product is more cost-effective and promises to be the most widely used DNAm high-throughput measurement technology over the next several years. Here we describe a suite of computational tools that incorporate state-of-the-art statistical techniques for the analysis of DNAm data. The software is structured to easily adapt to future versions of the technology. We include methods for preprocessing, quality assessment and detection of differentially methylated regions from the kilobase to the megabase scale. We show how our software provides a powerful and flexible development platform for future methods. We also illustrate how our methods empower the technology to make discoveries previously thought to be possible only with sequencing-based methods. http://bioconductor.org/packages/release/bioc/html/minfi.html. khansen@jhsph.edu; rafa@jimmy.harvard.edu Supplementary data are available at Bioinformatics online.
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              GREAT improves functional interpretation of cis-regulatory regions.

              We developed the Genomic Regions Enrichment of Annotations Tool (GREAT) to analyze the functional significance of cis-regulatory regions identified by localized measurements of DNA binding events across an entire genome. Whereas previous methods took into account only binding proximal to genes, GREAT is able to properly incorporate distal binding sites and control for false positives using a binomial test over the input genomic regions. GREAT incorporates annotations from 20 ontologies and is available as a web application. Applying GREAT to data sets from chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) of multiple transcription-associated factors, including SRF, NRSF, GABP, Stat3 and p300 in different developmental contexts, we recover many functions of these factors that are missed by existing gene-based tools, and we generate testable hypotheses. The utility of GREAT is not limited to ChIP-seq, as it could also be applied to open chromatin, localized epigenomic markers and similar functional data sets, as well as comparative genomics sets.
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                Author and article information

                Contributors
                jjimenez3@imim.es
                jjimenez@imim.es
                Journal
                Clin Epigenetics
                Clin Epigenetics
                Clinical Epigenetics
                BioMed Central (London )
                1868-7075
                1868-7083
                6 June 2024
                6 June 2024
                2024
                : 16
                : 75
                Affiliations
                [1 ]Neurovascular Research Group, Department of Neurology, Hospital del Mar Research Institute, ( https://ror.org/042nkmz09) C/ del Dr. Aiguader, 88, 08003 Barcelona, Spain
                [2 ]GRID grid.413396.a, ISNI 0000 0004 1768 8905, Institut d’Investigació Biomèdica Sant Pau (IIB SANT PAU), ; Sant Quintí, Barcelona, Spain
                [3 ]GRID grid.420175.5, ISNI 0000 0004 0639 2420, Center for Cooperative Research in Biosciences (CIC bioGUNE), Basque Research and Technology Alliance (BRTA), ; Biscaia, Spain
                [4 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Psychiatry, NeuroGenomics and Informatics, , Washington University School of Medicine, ; St. Louis, MO 63110 USA
                [5 ]Unit of Medical Epidemiology, Department of Surgical Sciences, Uppsala University, ( https://ror.org/048a87296) Uppsala, Sweden
                [6 ]GRID grid.5612.0, ISNI 0000 0001 2172 2676, Medicine Department, , DCEXS-Universitat Pompeu Fabra (UPF), ; 08002 Barcelona, Spain
                [7 ]Cancer Epigenetics Group, Josep Carreras Leukaemia Research Institute (IJC), Badalona, ( https://ror.org/00btzwk36) Barcelona, Catalonia Spain
                [8 ]Centro de Investigacion Biomedica en Red Cancer (CIBERONC), ( https://ror.org/04hya7017) Madrid, Spain
                [9 ]Institucio Catalana de Recerca I Estudis Avançats (ICREA), ( https://ror.org/0371hy230) Barcelona, Catalonia Spain
                [10 ]Physiological Sciences Department, School of Medicine and Health Sciences, University of Barcelona (UB), ( https://ror.org/021018s57) Barcelona, Catalonia Spain
                [11 ]Medicine Department, Autonomous University of Barcelona, ( https://ror.org/052g8jq94) Barcelona, Spain
                Article
                1690
                10.1186/s13148-024-01690-2
                11155152
                38845005
                97505317-93d2-483b-8488-345da1d52b44
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 28 March 2024
                : 29 May 2024
                Funding
                Funded by: Sara Borrell program, funded by Instituto de Salud Carlos III
                Award ID: CD22/00001, J.J.-B.
                Award Recipient :
                Funded by: RICORS-ICTUS
                Award ID: RD21/0006/0021
                Award ID: RD21/0006/0021
                Award Recipient :
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Genetics
                epigenetics,stroke outcome,thrombospondin-2,dna methylation
                Genetics
                epigenetics, stroke outcome, thrombospondin-2, dna methylation

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