bioRxivSystems BiologyPreprint
Converging pathways found in copy number variation syndromes with high schizophrenia risk
Friederike EhrhartiD, Ana Isabel SilvaiD, Thérèse van AmelsvoortiD, Emma N. M. M. von ScheibleriD, Chris T. EveloiD, David E.J. LindeniD
Abstract
Schizophrenia genetics is complex, and the contribution of common and rare variants are not fully understood. Several specific copy number variations (CNVs) confer increased risk for schizophrenia, and the study of their effects is central to molecular models of mental illness. However, these CNVs - microdeletions or -duplications - are spread across the genome and differ in the number of genes affected and classes of coded proteins. This suggests that, in order to fully understand the contribution of these genetic variants to mental illness, we need to look beyond the deleted or duplicated genes, to their interaction partners and involved molecular pathways. In this study, we developed machine-readable interactive pathways to enable analysis of downstream effects of genes within CNV loci and identify common pathways between CNVs with high schizophrenia risk using the WikiPathways database, and schizophrenia risk gene collections from GWAS studies and a gene-disease association database. For CNVs that are pathogenic for schizophrenia, we found overlapping pathways, including BDNF signaling, cytoskeleton, cell-cell connections, inflammation and MAPK3 signaling. Common schizophrenia risk genes identified by different studies are found in all CNV pathways but not enriched. Our findings suggest that specific pathways - such as BDNF signaling - may be critical contributors to schizophrenia risk conferred by rare CNVs, and common risk variants may operate through distinct mechanisms. Our approach also highlights the importance of not only investigating deleted or duplicated genes within pathogenic CNV loci, but also study their direct interaction partners, which may explain pleiotropic effects of CNVs on schizophrenia risk.
- organism: human
- assay: GWAS summary statistics
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 3
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