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Analyzing DNA methylation patterns in Schizophrenic patients 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. (Medical Genetics and Genomics)
Department of Psychiatry and Human Behavior, University of Mississippi Medical Center, Jackson, MS, USA.
Department of Psychiatry and Human Behavior, University of Mississippi Medical Center, Jackson, MS, USA.
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(English)Article in journal (Other academic) Submitted
Abstract [en]

Schizophrenia is common mental disorder with known genetic component involved. Since the association of environmental factors and schizophrenia has been reported, we analyzed a cohort of 75 schizophrenic and 50 control samples to investigate DNA methylation patterns, as one of the key players of epigenetic gene regulation.

Here we applied machine-learning and visualization methods, which were shown previously to be successful in detecting and highlighting differentially methylated patterns between cases and controls. On this data set, however, these methods did not uncover any signal discerning schizophrenia patients and healthy controls, suggesting that if a link exists, it is heterogeneous and complex.

National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:uu:diva-320678OAI: oai:DiVA.org:uu-320678DiVA, id: diva2:1090234
Available from: 2017-04-23 Created: 2017-04-23 Last updated: 2018-01-13
In thesis
1. Computational discovery of DNA methylation patterns as biomarkers of ageing, cancer, and mental disorders: Algorithms and Tools
Open this publication in new window or tab >>Computational discovery of DNA methylation patterns as biomarkers of ageing, cancer, and mental disorders: Algorithms and Tools
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Epigenetics refers to the mitotically heritable modifications in gene expression without a change in the genetic code. A combination of molecular, chemical and environmental factors constituting the epigenome is involved, together with the genome, in setting up the unique functionality of each cell type.

DNA methylation is the most studied epigenetic mark in mammals, where a methyl group is added to the cytosine in a cytosine-phosphate-guanine dinucleotides or a CpG site. It has been shown to have a major role in various biological phenomena such as chromosome X inactivation, regulation of gene expression, cell differentiation, genomic imprinting. Furthermore, aberrant patterns of DNA methylation have been observed in various diseases including cancer.

In this thesis, we have utilized machine learning methods and developed new methods and tools to analyze DNA methylation patterns as a biomarker of ageing, cancer subtyping and mental disorders.

In Paper I, we introduced a pipeline of Monte Carlo Feature Selection and rule-base modeling using ROSETTA in order to identify combinations of CpG sites that classify samples in different age intervals based on the DNA methylation levels. The combination of genes that showed up to be acting together, motivated us to develop an interactive pathway browser, named PiiL, to check the methylation status of multiple genes in a pathway. The tool enhances detecting differential patterns of DNA methylation and/or gene expression by quickly assessing large data sets.

In Paper III, we developed a novel unsupervised clustering method, methylSaguaro, for analyzing various types of cancers, to detect cancer subtypes based on their DNA methylation patterns. Using this method we confirmed the previously reported findings that challenge the histological grouping of the patients, and proposed new subtypes based on DNA methylation patterns. In Paper IV, we investigated the DNA methylation patterns in a cohort of schizophrenic and healthy samples, using all the methods that were introduced and developed in the first three papers.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. p. 55
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1520
Keywords
DNA methylation, machine learning, biomarker, cancer, ageing, classification
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-320720 (URN)978-91-554-9924-2 (ISBN)
Public defence
2017-06-12, A1:111a, BMC Building, Husargatan 3, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2017-05-22 Created: 2017-04-24 Last updated: 2018-01-13

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Department of Cell and Molecular BiologyDepartment of Immunology, Genetics and PathologyDepartment of Medical Biochemistry and Microbiology
Bioinformatics (Computational Biology)

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CiteExportLink to record
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