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Analyzing DNA methylation patterns in subjects diagnosed with schizophrenia using machine learning methods
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medicinsk genetik och genomik. Uppsala University, Science for Life Laboratory, SciLifeLab.
Univ Mississippi, Med Ctr, Dept Psychiat & Human Behav, Jackson, MS 39216 USA.
Univ Mississippi, Med Ctr, Dept Psychiat & Human Behav, Jackson, MS 39216 USA.
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2019 (English)In: Journal of Psychiatric Research, ISSN 0022-3956, E-ISSN 1879-1379, Vol. 114, p. 41-47Article in journal (Refereed) Published
Abstract [en]

Schizophrenia is a common mental disorder with high heritability. It is genetically complex and to date more than a hundred risk loci have been identified. Association of environmental factors and schizophrenia has also been reported, while epigenetic analyses have yielded ambiguous and sometimes conflicting results. Here, we analyzed fresh frozen post-mortem brain tissue from a cohort of 73 subjects diagnosed with schizophrenia and 52 control samples, using the Illumina Infinium HumanMethylation450 Bead Chip, to investigate genome-wide DNA methylation patterns in the two groups. Analysis of differential methylation was performed with the Bioconductor Minfi package and modern machine-learning and visualization techniques, which were shown previously to be successful in detecting and highlighting differentially methylated patterns in case-control studies. In this dataset, however, these methods did not uncover any significant signals discerning the patient group and healthy controls, suggesting that if there are methylation changes associated with schizophrenia, they are heterogeneous and complex with small effect.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD , 2019. Vol. 114, p. 41-47
Keywords [en]
DNA methylation, Schizophrenia, Machine learning, Classification, Clustering
National Category
Psychiatry
Identifiers
URN: urn:nbn:se:uu:diva-390083DOI: 10.1016/j.jpsychires.2019.04.001ISI: 000472127300006PubMedID: 31022588OAI: oai:DiVA.org:uu-390083DiVA, id: diva2:1340800
Funder
Swedish Research Council FormaseSSENCE - An eScience CollaborationEU, European Research Council, 282330Available from: 2019-08-06 Created: 2019-08-06 Last updated: 2019-08-06Bibliographically approved

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Moghadam, Behrooz TorabiEtemadikhah, MitraGrabherr, ManfredKomorowski, JanFeuk, LarsLindholm Carlström, Eva

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Moghadam, Behrooz TorabiEtemadikhah, MitraGrabherr, ManfredKomorowski, JanFeuk, LarsLindholm Carlström, Eva
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Computational Biology and BioinformaticsMedicinsk genetik och genomikScience for Life Laboratory, SciLifeLabDepartment of Medical Biochemistry and Microbiology
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