EpigeneticsNeurosciencePeer-reviewed journal article
Network modules linking expression and methylation in prefrontal cortex of schizophrenia
Dongdong LiniD, Jiayu CheniD, Kuaikuai DuaniD, Nora I. Perrone‐BizzozeroiD, Jing Sui (Beijing Normal University), my correct affiliation is beijing normal university, not Qingdao University of Science and Technology, please correct the current affiliation. Thank youiD, Vince D. CalhouniD, Jingyu LiuiD
Abstract
Tremendous work has demonstrated the critical roles of genetics, epigenetics as well as their interplay in brain transcriptional regulations in the pathology of schizophrenia (SCZ). There is great success currently in the dissection of the genetic components underlying risk-conferring transcriptomic networks. However, the study of regulating effect of epigenetics, as a modulator of environmental factors, in the etiopathogenesis of SCZ still faces many challenges. In this work we investigated DNA methylation and gene expression from the dorsolateral prefrontal cortex (DLPFC) region of schizophrenia patients and healthy controls using weighted correlation network approaches. We identified and replicated two expression and two methylation modules significantly associated with SCZ. Among them, one pair of expression and methylation modules were significantly overlapped in the module genes which were enriched in astrocyte-associated functional pathways, and specifically expressed in astrocytes. Another two linked expression-methylation module pairs were involved aging process with module genes mostly related to oligodendrocyte development and myelination, and specially expressed in oligodendrocytes. Further examination of underlying quantitative trait loci (QTLs) showed significant enrichment in genetic risk of most psychiatric disorders for expression QTLs but not for methylation QTLs. These results support the coherence between methylation and gene expression in a network level, and suggest a combinatorial effect of genetics and epigenetics in regulating gene expression networks specific to glia cells in relation with SCZ and aging process.
- organism: human
- assay: DNA methylation
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 13
- FWCI
- 0.65
- Field percentile
- top 37%
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