Genome-Wide Gene Expression Signature Of Depression

Abstract

Background Many studies have attempted to identify the molecular signature of depression. The results, however, are inconsistent. The small sample sizes, choice of tissue/cell types and suboptimal statistical methods are the major limitations that might contribute to this inconsistency. In our study, to identify the whole blood transcriptome signature of geriatric depression, we utilized two large population cohorts aged over 65 -The Sydney Memory and Aging Study, MAS (N=521) and The Older Australian Twin Study, OATS (N=324) as discovery and replication cohorts, respectively. Major Depression was assessed according to DSM-IV criteria. Methods The genome-wide gene expression data were obtained using Illumina HT-12 v4. After quality control and pre-processing, the application of stringent filtering criteria (by detection p-value (p Results The eigengenes of two modules were associated with the depression phenotype (p=0.01 and p=0.02). Closer inspection of the modules of interest revealed that only 37 out of 82 genes in one module and 17 out of 64 genes in another module were significantly associated with the phenotype of depression. Correlational analyses of individual genes within the depression relevant modules revealed that 8 out of 37 significant genes in one module were protein coding genes, involved in various translational, metabolic and immune processes (PCYOX1L, RPL14, MCTS1, GIMAP7, NDUFB9, BOLA2, EIF3M, RPL7A). The second module contained 5 protein coding genes out of 17 associated with depression, the known molecular functions of which include catalytic, enzyme regulation, transcription factor and translational regulation activities (PRCP, POLR2J2, ATF4, TAOK3, EIF2B5). The two top genes from both modules are known to be involved in metabolic process regulation by reactive oxygen species (PCYOX1L, prenylcysteine oxidase-like (p Discussion Our results support the oxidative stress hypothesis of depression and provide new insights into pathophysiological mechanisms of geriatric depression. In addition, we will present the results from pathway analyses (Ingenuity Pathway Analysis software, IPA) performed on genes identified as relevant to depression in this study. To bridge genotype with whole blood transcriptome and identify genomic loci that influence the identified gene expression signature of depression, we will present results from genome-wide eQTL analyses, which are ongoing.