Analyzing DNA methylation patterns in subjects diagnosed with schizophrenia using machine learning methods.

Torabi Moghadam B, Etemadikhah M, Rajkowska G, Stockmeier C, Grabherr M, Komorowski J, Feuk L, Carlström EL

J Psychiatr Res 114 (-) 41-47 [2019-07-00; online 2019-04-02]

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.

Bioinformatics Support for Computational Resources [Service]

NGI Uppsala (SNP&SEQ Technology Platform) [Service]

NGI Uppsala (Uppsala Genome Center) [Service]

National Genomics Infrastructure [Service]

PubMed 31022588

DOI 10.1016/j.jpsychires.2019.04.001

Crossref 10.1016/j.jpsychires.2019.04.001

pii: S0022-3956(18)31394-3