Abstract
Genome-wide association studies (GWAS) of major depression and its relevant biological phenotypes have been extensively conducted in large samples, and transcriptome-wide analyses in the tissues of brain regions relevant to pathogenesis of depression, e.g., dorsolateral prefrontal cortex (DLPFC), have also been widely performed recently. Integrating these multi-omics data will enable unveiling of depression risk genes and even underlying pathological mechanisms. Here, we employ summary data-based Mendelian randomization (SMR) and integrative risk gene selector (iRIGS) approaches to integrate multi-omics data from GWAS, DLPFC expression quantitative trait loci (eQTL) analyses and enhancer-promoter physical link studies to prioritize high-confidence risk genes for depression, followed by independent replications across distinct populations. These integrative analyses identify multiple high-confidence depression risk genes, and numerous lines of evidence supporting pivotal roles of the netrin 1 receptor (DCC) gene in this illness across different populations. Our subsequent explorative analyses further suggest that DCC significantly predicts neuroticism, well-being spectrum, cognitive function and putamen structure in general populations. Gene expression correlation and pathway analyses in DLPFC further show that DCC potentially participates in the biological processes and pathways underlying synaptic plasticity, axon guidance, circadian entrainment, as well as learning and long-term potentiation. These results are in agreement with the recent findings of this gene in neurodevelopment and psychiatric disorders, and we thus further confirm that DCC is an important susceptibility gene for depression, and might be a potential target for new antidepressants.
Highlights
A primary current challenge in the psychiatry field is to dissect the underlying neurobiological basis of common mental illnesses such as major depression, which is said to be one of the ten most disabling conditions in the world[1]
We applied multi-SNP-based summary data-based Mendelian randomization (SMR) method to test the associations between risk SNPs of depression identified in the genome-wide association studies (GWAS) study[4] and mRNA expression based on two independent dorsolateral prefrontal cortex (DLPFC) expression quantitative trait loci (eQTL) datasets from BrainSeq Phase 2 (N = 397)[26] and CommonMind (N = 467)[27], respectively
SMR analyses using this larger eQTL samples replicated most of the risk genes identified in the earlier datasets, and a total of 10 risk genes exhibited statistical significance throughout all the SMR analyses (Fig. 1; Table 1)
Summary
A primary current challenge in the psychiatry field is to dissect the underlying neurobiological basis of common mental illnesses such as major depression, which is said to be one of the ten most disabling conditions in the world[1]. Recent genome-wide association studies (GWAS) of depressive subjects and healthy controls have identified multiple statistically robust loci[3,4], providing numerous candidates for in-depth exploration of its pathological mechanisms. This research regimen has been widely applied in recent years, the fact that majority of the disease risk loci identified by GWAS reside in the noncoding genomic regions has significantly hampered the accomplishments in elucidating their biological and pathological impacts. Accumulating studies have found that noncoding variations of complex diseases tend to be associated with mRNA expression[7], and analyzing the expression quantitative trait loci (eQTL) effects of the risk alleles in relevant tissues is a plausible strategy to probe the risk genes from risk locus[8,9,10]. Several integrative analyses using GWAS and brain eQTL data have revealed susceptibility genes and potential biological mechanisms for psychiatric disorders[11,12,13]
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