Gene co-expression network-based analysis of multiple brain tissues reveals novel genes and molecular pathways underlying major depressive disorder (MDD)

Major Depressive Disorder (MDD) is a serious mental health disorder with a global lifetime frequency of ~12% (17% of women, 9% of men). MDD is well known to be a very complex multifactorial trait (i.e. contributions from genetics, epigenetic factors, environmental effects, endogenous influences, and each individual’s microbiome). A recent genome-wide association study (GWAS) meta-analysis (135,458 MDD cases, 344,901 controls, plus two other GWAS totaling 246,363 cases, 561,190 controls) identified 102 independent variants associated with major depression — 87 of which were replicated in an independent sample of 1,509,153 individuals..!! 😊

Detailed functional studies have shown that many of these loci possess common [i.e. minor allele frequencies (MAFs) of greater than >0.01, or >1%] single nucleotide variants (SNVs) that regulate expression of multiple genes in brain tissue — with putative roles in central nervous system (CNS) development and synapse plasticity. Large-scale GWAS have also discovered altered immune pathways; these results suggest disease-associated SNVs modify MDD susceptibility by changing expression of target genes in a tissue-specific manner. Genes regulate the activity of one-another in large co-expression networks. Therefore, SNVs may not only affect the activity of a single target gene, but activities of multiple biologically related genes within the same co-expression network to influence manifestation of a complex trait such as MDD. Integration of GWAS SNV genotype data with gene co-expression network data across multiple tissues may be useful to elucidate biological pathways and processes underlying highly polygenic complex disorders such as MDD.

Genome-wide gene expression data have been successfully integrated with SNV genotype data to prioritize risk genes and reveal possible mechanisms underlying susceptibility to a range of psychiatric disorders; however, collection of phenotype, SNV genotype,

and gene expression data measured from each individual is impeded by cost and tissue availability — and identifying causal variants can be difficult due to linkage disequilibrium (LD; in population genetics, LD is the nonrandom association of alleles at different loci in a given population) and confounders (i.e. from environmental and other factors described above). Recent approaches address these limitations by integrating GWAS summary statistics with independent gene expression data provided by large international consortia [e.g. the multi-tissue Genotype-Tissue Expression (GTEx) project]. The most recent release of the GTEx project (version 7) contains SNV genotype data linked to gene expression across 53 tissues from 714 donors — including 13 brain tissues from 216 donors; this represents a valuable resource for studying gene expression and its relationship with genetic variation, known as expression quantitative trait loci (eQTL) mapping.

Using this framework for identifying individual risk genes, plus gene co-expression networks, and using GWAS summary statistics and gene expression information across multiple human brain tissues and whole blood, authors [see attached article] developed a novel gene-based method that leverages tissue-specific eQTL information to identify 99 biologically plausible risk genes associated with MDD, of which 58 are newly discovered. Among these novel associations is Complement Factor 4A (C4A) — recently implicated in schizophrenia through its role in “synaptic pruning” during postnatal development.

MDD risk genes were enriched in gene co-expression modules in numerous brain tissues and the implicated gene modules contained genes involved in synaptic signaling, neuronal development, and cell transport pathways. Modules enriched with MDD signals were strongly preserved across brain tissues, but were weakly preserved in whole blood; this finding underscores the importance of using disease-relevant tissues in genetic studies of psychiatric traits. The novel analytical framework [reported herein] should be useful to gain fundamental insights into CNS functioning in MDD and other brain-related traits. Moreover, this study underscores the existence of a “genetic predisposition,” from the time of brain formation in utero, which — when stimulated by appropriate signals (i.e. from epigenetic factors, environmental effects, endogenous influences, and each individual’s microbiome) — “push the individual ‘over the edge’ and into a full-blown manifestation of MDD.” ☹


PLoS Genet Jul 2019; 15: e1008245

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