Functional Genomics Analysis to Disentangle the Role of Genetic Variants in Major Depression

Abstract

Background: Major Depression is the leading cause of impairment worldwide. The understanding of its molecular underpinnings is key to identifying new potential biomarkers and drug targets to alleviate its burden in society. Leveraging available GWAS data and functional genomic tools to assess regulatory variation could help explain the role of Major Depression associated genetic variants in disease pathogenesis. We have conducted a fine-mapping analysis of genetic variants associated with Major Depression and applied a pipeline focused on gene expression regulation by using two complementary approaches: cis-eQTL colocalization analysis using GTEx data and alteration of transcription factor binding sites with pattern matching approaches and chromatin accessibility data.Results: The fine-mapping of major depression genetic variants uncovered putative causally associated variants whose proximal genes were linked with Major Depression pathophysiology. Four genetic variants altering the expression of 5 genes were found by colocalization analysis, highlighting the role of SLC12A5, involved in chlorine homeostasis in neurons, and MYRF, related with central nervous system myelination and oligodendrocyte differentiation. The transcription factor binding analysis revealed the potential role of the genomic variant rs62259947 in modulating the expression of P4HTM through the alteration of YY1 binding site, altogether regulating hypoxia response.Conclusions: The combination of GWAS signals, cis-eQTL, transcription factor binding site information and active regulatory regions in the chromatin, enabled the prioritization of putative causal genetic variants in Major Depression. Importantly, our pipeline can be applied when only index genetic variants are available. Finally, the presented approach enabled the proposal of mechanistic hypotheses of these genetic variants and their role in disease pathogenesis.

Benchmarking post-GWAS analysis tools in major depression: Challenges and implications

“…Frontiers in Genetics frontiersin.org potential biomarker for MD (Yamagata and Nakagawa, 2020). Considering all eGenes together, they are enriched in their association with transcription factor regulation (Supplementary Table S9), which has already been related to MD (Zhong et al, 2019;Li X. et al, 2021;Pérez-Granado et al, 2022). The mapping of eGenes to protein interaction clusters indicated that the three sets of genes (i.e., PICS+CLPP, PICS+fasENLOC and TORUS+fastENLOC) belonged to distinct clusters and are thus likely to be involved in different biological processes.…”

“…Nevertheless, PICS+CLPP and PICS+fastENLOC associated sets of clusters were enriched with genes associated with processes involving TF regulation as well as inflammation or neurogenesis (Supplementary Tables S10, S11). All these processes are associated with MD pathogenesis (Shadrina et al, 2018;Zhong et al, 2019;Li X. et al, 2021, Li et al, 2021Pérez-Granado et al, 2022). In general, the identified eGenes are poorly characterized yet the cluster analysis shades some light on their potential molecular associations.…”

“…Based on previous experience in post-GWAS data analysis, we narrowed down the results to the most likely causal GVs (Farh et al, 2015;Ghoussaini et al, 2020;Pérez-Granado et al, 2022) by filtering GWAS GVs and eQTLs PICS probabilities, as well as their product by >10%. We run fastENLOC with fine-mapped GWAS GVs and eQTLs per tissue using the following parameters: default shrinkage 1) and total variants (7,666,894).…”