Protein Interaction Networks Link Schizophrenia Risk Loci to Synaptic Function

Abstract

Schizophrenia is a severe and highly heritable psychiatric disorder affecting approximately 1% of the population. Genome-wide association studies have identified 108 independent genetic loci with genome-wide significance but their functional importance has yet to be elucidated. Here, we develop a novel strategy based on network analysis of protein-protein interactions (PPI) to infer biological function associated with variants most strongly linked to illness risk. We show that the schizophrenia loci are strongly linked to synaptic transmission (P _FWE < .001) and ion transmembrane transport (P _FWE = .03), but not to ontological categories previously found to be shared across psychiatric illnesses. We demonstrate that brain expression of risk-linked genes within the identified processes is strongly modulated during birth and identify a set of synaptic genes consistently changed across multiple brain regions of adult schizophrenia patients. These results suggest synaptic function as a developmentally determined schizophrenia process supported by the illness's most associated genetic variants and their PPI networks. The implicated genes may be valuable targets for mechanistic experiments and future drug development approaches.

Keywords: GWAS; functional analysis; genetics; pathway analysis.

Fig. 1.

Overview of network based functional analysis. a) Schematic illustration of the network linking the PPI to ontological category information. b) Selection of the 44 loci mapping to single, unique genes with at least 1 link to the PPI network. c) Network illustration of the 2 significantly schizophrenia associated ontological categories “synaptic transmission” (GO:0007268) and “ion transmembrane transport” (GO:0034765). Nodes are colored depending on their predominant link to a given ontological category or its respectively associated proteins (red-colored proteins are shared between ontological categories and red-colored risk loci predominantly associated with such nodes).

Fig. 2.

Gene expression patterns throughout development stages and across brain regions. Genes were ordered using complete linkage hierarchical clustering on the Euclidean distance of their expression levels. Genes shown in bold show significant change throughout developmental stages (Spearman correlation, False Discovery Rate [FDR] < 0.05). Graphical layout adapted from ref.

Fig. 3.

Overlap of significant expression differences across datasets. Displayed genes showed expression changes with P _FDR < .05. Arrows indicate whether expression was consistently increased (up arrow) or decreased (down arrow) across the datasets where a significant change in a given gene was observed.