A gene co-expression network-based analysis of multiple brain tissues reveals novel genes and molecular pathways underlying major depression.
Zachary F GerringEric R GamazonEske M Derksnull nullPublished in: PLoS genetics (2019)
Major depression is a common and severe psychiatric disorder with a highly polygenic genetic architecture. Genome-wide association studies have successfully identified multiple independent genetic loci that harbour variants associated with major depression, but the exact causal genes and biological mechanisms are largely unknown. Tissue-specific network approaches may identify molecular mechanisms underlying major depression and provide a biological substrate for integrative analyses. We provide a framework for the identification of individual risk genes and gene co-expression networks using genome-wide association summary statistics and gene expression information across multiple human brain tissues and whole blood. We developed a novel gene-based method called eMAGMA that leverages tissue-specific eQTL information to identify 99 biologically plausible risk genes associated with major depression, of which 58 are novel. Among these novel associations is Complement Factor 4A (C4A), recently implicated in schizophrenia through its role in synaptic pruning during postnatal development. Major depression risk genes were enriched in gene co-expression modules in multiple brain tissues and the implicated gene modules contained genes involved in synaptic signalling, neuronal development, and cell transport pathways. Modules enriched with major depression signals were strongly preserved across brain tissues, but were weakly preserved in whole blood, highlighting the importance of using disease-relevant tissues in genetic studies of psychiatric traits. We identified tissue-specific genes and gene co-expression networks associated with major depression. Our novel analytical framework can be used to gain fundamental insights into the functioning of the nervous system in major depression and other brain-related traits.
Keyphrases
- genome wide
- copy number
- dna methylation
- gene expression
- genome wide identification
- poor prognosis
- genome wide association
- genome wide analysis
- white matter
- transcription factor
- resting state
- cerebral ischemia
- binding protein
- mental health
- bipolar disorder
- stem cells
- brain injury
- early onset
- subarachnoid hemorrhage
- mass spectrometry