Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia

Abstract

With the recent success of genome-wide association studies (GWAS), a wealth of association data has been accomplished for more than 200 complex diseases/traits, proposing a strong demand for data integration and interpretation. A combinatory analysis of multiple GWAS datasets, or an integrative analysis of GWAS data and other high-throughput data, has been particularly promising. In this study, we proposed an integrative analysis framework of multiple GWAS datasets by overlaying association signals onto the protein-protein interaction network, and demonstrated it using schizophrenia datasets. Building on a dense module search algorithm, we first searched for significantly enriched subnetworks for schizophrenia in each single GWAS dataset and then implemented a discovery-evaluation strategy to identify module genes with consistent association signals. We validated the module genes in an independent dataset, and also examined them through meta-analysis of the related SNPs using multiple GWAS datasets. As a result, we identified 205 module genes with a joint effect significantly associated with schizophrenia; these module genes included a number of well-studied candidate genes such as DISC1, GNA12, GNA13, GNAI1, GPR17, and GRIN2B. Further functional analysis suggested these genes are involved in neuronal related processes. Additionally, meta-analysis found that 18 SNPs in 9 module genes had P(meta)<1 × 10⁻⁴, including the gene HLA-DQA1 located in the MHC region on chromosome 6, which was reported in previous studies using the largest cohort of schizophrenia patients to date. These results demonstrated our bi-directional network-based strategy is efficient for identifying disease-associated genes with modest signals in GWAS datasets. This approach can be applied to any other complex diseases/traits where multiple GWAS datasets are available.

Figure 1. Materials and MethodscMaterials and Methodsgenes for schizophrenia.

Figure 2. Distribution of module scores (Z_m) from two GWAS datasets.

Each circle in the plot represents a module. The circles in red indicate those selected modules (see text). X-axis: module scores from the discovery GWAS dataset. Y-axis: module scores from the evaluation GWAS dataset.

Figure 3. Meta-analysis results of the two most significant genes.

Figures were generated using the LocusZoom online tool. X-axis is the genome coordinate. Y-axis is the -logP _meta values. Each point represents a SNP. The color of points is according to their level of linkage disequilibrium (LD) with the index SNPs. In this case, the index SNP is the most significant one in each panel. The LD measure is r² based on the HapMap CEU population (release 22).