Multiple large-scale studies such as genome-wide association studies (GWAS) have been performed to identify genetic contributors to suicidal behaviors (SB). We aimed to summarize and analyze the information obtained in SB GWAS, to explore the biological process gene ontology (GO) of genes associated with SB from GWAS, and to determine the possible implications of the genes associated with SB in Kyoto encyclopedias of genes and genomes (KEGG) biological pathways. The articles included in the analysis were obtained from PubMed and Scopus databases. Enrichment analyses were performed in Enrichr to evaluate the KEGG pathways and GO of the genes associated with SB of GWAS. The findings of biological process GO analysis showed 924 GO involved in genes related with SB; of those, the regulation of glucose import in response to insulin stimulus, regulation of protein localization to plasma membrane, positive regulation of endopeptidase activity, heterotypic cell-cell adhesion, regulation of cardiac muscle cell contraction, positive regulation of protein localization to plasma membrane, and positive regulation of protein localization to cell periphery biological process GO showed significant statistical association. Furthermore, we obtained 130 KEGG pathways involved in genes related with SB, which Aldosterone synthesis and secretion, Rap1 signaling pathway and arrhythmogenic right ventricular cardiomyopathy pathways showed a significant statistical association. These findings give a better perspective of the biological participation of genes associated with SB, which will be important to perform adequate strategies to prevent and treat SB.

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Background The aim of this study is to identify differentially methylated regions (DMRs) in the genomes of a sample of cognitively healthy individuals and a sample of individuals with LOAD, all of them nonagenarians from Costa Rica. Methods In this study, we compared whole blood DNA methylation profiles of 32 individuals: 21 cognitively healthy and 11 with LOAD, using the Infinium MethylationEPIC BeadChip. First, we calculated the epigenetic age of the participants based on Horvath’s epigenetic clock. DMRcate and Bumphunter were used to identify DMRs. After in silico and knowledge-based filtering of the DMRs, we performed a methylation quantitative loci (mQTL) analysis (rs708727 and rs960603). Results On average, the epigenetic age was 73 years in both groups, which represents a difference of over 20 years between epigenetic and chronological age in both affected and unaffected individuals. Methylation analysis revealed 11 DMRs between groups, which contain six genes and two pseudogenes. These genes are involved in cell cycle regulation, embryogenesis, synthesis of ceramides, and migration of interneurons to the cerebral cortex. One of the six genes is PM20D1, for which altered expression has been reported in LOAD. After genotyping previously reported mQTL SNPs for the gene, we found that average methylation in the PM20D1 DMR differs between genotypes for rs708727, but not for rs960603. Conclusions This work supports the possible role of PM20D1 in protection against AD, by showing differential methylation in blood of affected and unaffected nonagenarians. Our results also support the influence of genetic factors on PM20D1 methylation levels.

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