Background/Objectives: Major depressive disorder (MDD) is a mental health condition that can severely impact patients’ social lives, leading to withdrawal and difficulty in maintaining relationships. Environmental factors such as trauma and stress can worsen MDD by interacting with genetic predispositions. Epigenetics, which examines changes in gene expression influenced by the environment, may help identify patterns linked to depression. This study aimed to explore the epigenetic mechanisms behind MDD by analysing six public datasets (n = 1125 MDD cases, 398 controls in blood; n = 95 MDD cases, 96 controls in brain tissues) from the Gene Expression Omnibus. Methods: As an innovative approach, two meta-analyses of DNA methylation patterns were conducted alongside an investigation of stochastic epigenetic mutations (SEMs), epigenetic age acceleration, and rare epivariations. Results: While no significant global methylation differences were observed between MDD cases and controls, hypomethylation near the SHF gene (brain-specific probe cg25801113) was consistently found in MDD cases. SEMs revealed a gene-level burden in MDD, though epigenetic age acceleration was not central to the disorder. Additionally, 51 rare epivariations were identified in blood tissue and 1 in brain tissue linked to MDD. Conclusions: The study emphasises the potential role of rare epivariations in MDD’s epigenetic regulation but calls for further research with larger, more diverse cohorts to confirm these findings.

Major depressive disorder (MDD) is a prevalent mental health condition with substantial social and economic consequences. This study focuses on the interplay between genetic and environmen-tal factors in MDD, with particular emphasis on epigenetic mechanisms such as stochastic epige-netic mutations (SEMs), epigenetic age acceleration, and epigenetic drift. We conducted a meta-analysis of DNA methylation patterns across six datasets (n=1125 MDD cases, 398 controls in whole blood; n=95 MDD cases, 96 controls in brain tissues) from the Gene Expression Omnibus. The analysis did not reveal significant global methylation differences between MDD cases and controls. However, a brain-specific probe (cg25801113) near the SHF gene consistently exhibited hypomethylation, suggesting its potential relevance to MDD. SEMs were assessed, revealing a dis-tinct burden at the gene level in MDD cases, though no significant findings emerged for epigenetic age acceleration, indicating it may not play a central role in MDD. The study also identified rare epivariations in specific genes unique to MDD cases. In blood tissue, 51 genes were associated with these unique epivariations, while 1 gene was highlighted in the brain tissue. These results un-derscore the importance of exploring rare epivariations in understanding the molecular mecha-nisms underlying MDD and offer potential targets for further investigation.

Background: In vitro fertilization (IVF) outcomes remain variable and difficult to predict, even in individuals without diagnosed infertility. This uncertainty complicates clinical decision-making and impedes the identification of biological factors that underlie IVF success or failure. A major barrier to progress is the absence of standardized criteria to distinguish true biological inefficiencies from random variation or technical noise in IVF procedures. Objective: To develop a robust statistical framework for defining IVF under- and overperformance in a fertile population, enabling precise phenotyping of IVF inefficiencies and laying the foundation for future genomic and mechanistic studies. Methods: We conducted the largest retrospective cohort analysis to date of IVF outcomes in 114.237 oocyte donation cycles, from 32.492 rigorously screened donors who underwent 84.228 stimulation cycles. IVF performance was evaluated across four developmental stages: oocyte maturation, fertilization, blastocyst formation, and preimplantation development efficiency. Donors were classified as statistical under- or overperformers using a combination of binomial testing and mixed-effects logistic regression models, adjusting for identified confounders. Reproducibility of performance classifications was assessed through multi-cycle consistency analyses and supervised predictive modelling. Results: Despite stringent fertility screening, 0.9% to 2.1% of donors were identified as statistically underperformers in individual developmental metrics, with similar proportions observed for overperformers. Minimal overlap between donors falling into significant categories across different developmental metrics supported the interpretation of IVF performance as comprising biologically distinct endophenotypes. Underperformance was reproducible across multiple cycles in a subset of donors (P < 0.001), indicating a likely biological basis rather than random or procedural variability. Logistic regression models demonstrated modest predictive ability for future outlier status (AUC > 0.61 and < 0.71), further supporting the presence of stable, donor-specific traits. Interestingly, comparison of donor clinical variables revealed a possibly clinically meaningful difference in the distribution of donors that had a documented live birth prior to donation between performance groups for all developmental metrics, indicating a potential association with proven fertility and better IVF outcomes. Finally, 109 donors across performance groups were surveyed, revealing no identifiable differences in natural fertility outcomes, further reinforcing that IVF inefficiency does not necessarily reflect underlying infertility. Conclusion: This unprecedented and methodologically rigorous analysis demonstrates that statistically significant deviations in IVF outcomes can occur even among presumed fertile donors. These findings challenge the assumption that poor IVF outcomes are inherently indicative of infertility and support a shift toward defining IVF failure through biological, rather than purely clinical, criteria. Our standardized phenotyping framework offers a powerful tool for identifying the molecular and genetic contributors to IVF inefficiency and for advancing personalized reproductive medicine.