Analyzing DNA methylation patterns in subjects diagnosed with schizophrenia using machine learning methods. (July 2019)

• Record Type: Journal Article
• Authors: Torabi Moghadam, Behrooz; Etemadikhah, Mitra; Rajkowska, Grazyna; Stockmeier, Craig; Grabherr, Manfred; Komorowski, Jan; Feuk, Lars; Carlström, Eva Lindholm
• Is Part Of: Journal of psychiatric research. Volume 114(2019)
• Page Start: 41
• Page End: 47
• Publication Date: 2019-07
• Subjects: DNA methylation -- Schizophrenia -- Machine learning -- Classification -- Clustering; DNAm DNA methylation -- MCFS Monte Carlo Feature Selection -- CpG Cytosine-phosphate-Guanine -- DMR Deferentially Methylated Region; Psychiatry -- Periodicals; Mental Disorders -- Periodicals; Maladies mentales -- Périodiques; Psychiatry; Electronic journals; Periodicals; 616.89005
• DOI: 10.1016/j.jpsychires.2019.04.001
• Languages: English
• ISSNs: 0022-3956

Abstract

Abstract: 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.