Major Depressive Disorder (MDD) is a clinically heterogeneous syndrome with diverse etiological pathways. Traditional Epigenome-Wide Association Studies (EWAS) have successfully identified risk loci based on differential methylation magnitude. As a complementary perspective, effect-size-based ranking alone may not fully capture regulatory nodes that exhibit modest methylation changes but occupy critical upstream positions in biological networks. Here, we report findings and hypotheses from a two-tier computational analysis of DNA methylation data (GSE198904; \(n=206\) ), combining conventional statistical approaches with machine learning-assisted regulatory inference.

翻译：重度抑郁症（MDD）是一种临床异质性综合征，具有多样化的病因学通路。传统的全表观基因组关联研究（EWAS）已基于差异甲基化程度成功识别出风险位点。作为一种补充视角，仅基于效应大小的排序可能无法完全捕捉那些甲基化变化适度但在生物网络中占据关键上游位置的调控节点。本文报告了对DNA甲基化数据（GSE198904；\(n=206\)）进行双层计算分析的研究发现与假说，该分析结合了传统统计方法与机器学习辅助的调控推断。