An integrative functional genomics approach for discovering biomarkers in schizophrenia

Abstract

Schizophrenia (SZ) is a complex disorder resulting from both genetic and environmental causes with a lifetime prevalence world-wide of 1%; however, there are no specific, sensitive and validated biomarkers for SZ. A general unifying hypothesis has been put forward that disease-associated single nucleotide polymorphisms (SNPs) from genome-wide association study (GWAS) are more likely to be associated with gene expression quantitative trait loci (eQTL). We will describe this hypothesis and review primary methodology with refinements for testing this paradigmatic approach in SZ. We will describe biomarker studies of SZ and testing enrichment of SNPs that are associated both with eQTLs and existing GWAS of SZ. SZ-associated SNPs that overlap with eQTLs can be placed into gene-gene expression, protein-protein and protein-DNA interaction networks. Further, those networks can be tested by reducing/silencing the gene expression levels of critical nodes. We present pilot data to support these methods of investigation such as the use of eQTLs to annotate GWASs of SZ, which could be applied to the field of biomarker discovery. Those networks that have association with SNP markers, especially cis-regulated expression, might lead to a more clear understanding of important candidate genes that predispose to disease and alter expression. This method has general application to many complex disorders.

Figure 1:

There were four exons from different genes (BAT2L, DEK, GSR and DBC1) that significantly discriminated first-episode individuals with SZ (blue) and controls (red). The individual exons with bootstrap P-values <10⁻⁸ were plotted in multidimensional scaling, complete separation of SZ and C was observed, except for one subject with SZ which could not be classified.

Figure 2:

Schematic of the SNP-eQTL concept, where SNPs associated to expression eQTLs (exon array, gene expression array or RNA-Seq) up to 2 Mb away can form a SNP-eQTL.

Figure 3:

Diagram of AGA exon array expression showing that our initial finding of decreased AGA expression in LCL [64] was replicated in 30 SZ and 8 healthy controls. We reported a cis-SNP association to the expression of the AGA transcript in LCL [64], that was independently replicated [65]. Y-axis is log2 scale of probe sets expression, control mean expression (red), SZ mean expression (blue) values at each AGA probe set.

Figure 4:

Graphical display of expression analysis of IRF5 demonstrating the significant relationship between the rs10954213 genotype and IRF5 probe set 3023264 expression in 60 subjects in the DLPFC (P = 8.1 × 10⁻⁵), and anterior cingulate cortex (P = 0.005). Y-axis is ΔΔCt, inverse to fold change; x-axis is two brain regions by genotype of IRF5 SNP rs10954213 [25].

Figure 5:

We identified a highly connected network of 18 dysregulated genes (shown in color, red: upregulated; green: downregulated) between SZ and controls, with direct connections (unbroken line) [34]. The central node is the transcription factor HFN4A (NR2A1) [71], with 5 of the 18 genes in the network passing Bonferroni correction for differential expression.