Multi-locus genome-wide association analysis supports the role of glutamatergic synaptic transmission in the etiology of major depressive disorder

Abstract

Major depressive disorder (MDD) is a common psychiatric illness characterized by low mood and loss of interest in pleasurable activities. Despite years of effort, recent genome-wide association studies (GWAS) have identified few susceptibility variants or genes that are robustly associated with MDD. Standard single-SNP (single nucleotide polymorphism)-based GWAS analysis typically has limited power to deal with the extensive heterogeneity and substantial polygenic contribution of individually weak genetic effects underlying the pathogenesis of MDD. Here, we report an alternative, gene-set-based association analysis of MDD in an effort to identify groups of biologically related genetic variants that are involved in the same molecular function or cellular processes and exhibit a significant level of aggregated association with MDD. In particular, we used a text-mining-based data analysis to prioritize candidate gene sets implicated in MDD and conducted a multi-locus association analysis to look for enriched signals of nominally associated MDD susceptibility loci within each of the gene sets. Our primary analysis is based on the meta-analysis of three large MDD GWAS data sets (total N=4346 cases and 4430 controls). After correction for multiple testing, we found that genes involved in glutamatergic synaptic neurotransmission were significantly associated with MDD (set-based association P=6.9 × 10(-4)). This result is consistent with previous studies that support a role of the glutamatergic system in synaptic plasticity and MDD and support the potential utility of targeting glutamatergic neurotransmission in the treatment of MDD.

Figure 1

Multi-locus association analysis procedure for major depressive disorder (MDD). Our analysis consisted of two major steps: (1) candidate gene-set compilation and (2) enrichment association analysis. The gene-set-compilation step aims to prioritize groups of biologically relevant genes that are most relevant to MDD pathogenesis. First, a list of widely studied MDD candidate genes was assembled from previous GWAS (genome-wide association studies) studies. The text-mining tool GRAIL (Gene Relationships Among Implicated Loci) was used to identify biological relationships between the selected genes. We used the GRAIL biological keywords to select relevant gene sets from the Gene Ontology database. The second enrichment analysis step aims to identify gene sets that show aggregated association with major depression than would be expected by chance. In order to control for different gene sizes, SNP density and linkage disequilibrium (LD) across genomic region and LD-independent MDD association regions were generated using PLINK. Enrichment of the candidate gene sets was examined using GWAS-specific multi-locus association analysis tool, INRICH, based on the LD-independent regions. The second-step permutation strategy in INRICH was conducted for correcting the testing of multiple gene sets that are not independent of each other.