Bioinformatic Analysis of Epigenomic Studies for Major Depressive Disorder


  • Farwah B. Alam North South University, Dhaka, Bangladesh
  • Yeimy González-Giraldo Center for Psychosocial Studies for Latin America and the Caribbean, Universidad Antonio Nariño, Bogotá, Colombia
  • Diego Forero Health and Sport Sciences Research Group, School of Health and Sport Sciences, Fundación Universitaria del Área Andina, Bogotá, Colombia



Epigenomics, DNA Methylation, Psychiatric Genomics, Bioinformatics, Major depressive disorder


Background: Major depressive disorder (MDD) is a common psychiatric entity, being characterized by alterations in mood and in other clinical dimensions. Several epigenome-wide association studies (EWAS) for MDD have been published. Here, we aimed to identify common genes in EWAS and their convergence with multiple lines of genomic evidence. Methods: We carried out a computational analysis using data of EWAS, which included a meta-analysis for brain samples of MDD, a convergence analysis for brain and blood samples, and top results from available genome-wide expression and association data. Functional enrichment and protein-protein interaction network analyses were also done. Results: The meta-analysis for brain samples detected a significant gene, FAM53B. A list of forty-four top differentially methylated (DM) candidate genes was found, including GRM8, NOTCH4 and SEMA6A, in addition to known druggable genes. The binding-sites for brain-expressed transcription factors, CREB and FOXO1, were enriched in the top DM genes. The protein-protein interaction networks showed that DM genes for MDD, such as RPRM and TMEM14B, play a central role. Conclusion: In this study, we found integrative evidence for the possible role of novel candidate genes and pathways. These genes are involved in mechanisms of synaptic plasticity, which have been associated with several psychiatric disorders. Analysis of epigenetic factors have a great potential for the identification of the mechanisms involved in the pathogenesis of MDD, taking into account their possible role in the interaction between genetic factors and the environment.



How to Cite

Alam, F. B., González-Giraldo, Y., & Forero, D. (2022). Bioinformatic Analysis of Epigenomic Studies for Major Depressive Disorder. Archivos De Neurociencias, 27(2), 11–18.



Original Articles