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Dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis represents one of the most consistent pathophysiological findings in depressive disorders. Cortisol signaling is affected by proteins that mediate its cellular responses or alters its availability to mineralocorticoid and glucocorticoid receptors. In our study, we evaluated candidate genes that may influence the risk for depression and suicide due to its involvement in cortisol signaling. The aim of the study was to assess whether the genotypes of these genes are associated with the risk for depression, severity of depressive symptoms, suicidal ideation, and suicide attempts. And whether there is interaction between genes and early-life stress. In this study, 100 healthy controls and 140 individuals with depression were included. The subjects were clinically assessed using the 21-item GRID-Hamilton questionnaires (GRID-HAMD-21), Beck Scale for Suicidal Ideation (BSI), and the Childhood Trauma Questionnaire (CTQ). A robust multifactorial dimensionality reduction analysis was used to characterize the interactions between the genes HSD11B1 , NR3C1, NR3C2 , and MDR1 and early-life stress. It was found a significant association of the heterozygous genotype of the MDR1 gene rs1128503 polymorphism with reduced risk of at least one suicide attempt (OR: 0.08, p = 0.003*) and a reduction in the number of suicide attempts (β = −0.79, p = 0.006*). Furthermore, it was found that the MDR1 rs1228503 and NR3C2 rs2070951 genes interact with early-life stress resulting in a strong association with depression (p = 0.001). Our findings suggest that polymorphisms in the MDR1 and NR3C2 genes and their interaction with childhood trauma may be important biomarkers for depression and suicidal behaviors. [ABSTRACT FROM AUTHOR]

Although genome-wide association studies have identified multiple Alzheimer's disease (AD)-associated loci by selecting the main effects of individual single-nucleotide polymorphisms (SNPs), the interpretation of genetic variance in AD is limited. Based on the linear regression method, we performed genome-wide SNP-SNP interaction on cerebrospinal fluid Aβ 42 to identify potential genetic epistasis implicated in AD, with age, gender, and diagnosis as covariates. A GPU-based method was used to address the computational challenges posed by the analysis of epistasis. We found 368 SNP pairs to be statistically significant, and highly significant SNP-SNP interactions were identified between the marginal main effects of SNP pairs, which explained a relatively high variance at the Aβ 42 level. Our results replicated 100 previously reported AD-related genes and 5 gene-gene interaction pairs of the protein-protein interaction network. Our bioinformatics analyses provided preliminary evidence that the 5-overlapping gene-gene interaction pairs play critical roles in inducing synaptic loss and dysfunction, thereby leading to memory decline and cognitive impairment in AD-affected brains. [ABSTRACT FROM AUTHOR]