Premenstrual dysphoric disorder (PMDD) affects nearly 5% of women of reproductive age. Symptomatic heterogeneity, together with largely unknown genetics, has greatly hindered its effective treatment. In the present study, analysis of genomic sequencing-based copy number variations (CNVs) called from 100 kb white blood cell DNA sequence windows by means of semisupervized clustering led to the segregation of patient genomes into the D and V groups, which correlated with the depression and invasion clinical types, respectively, with 89.0% consistency. Application of diagnostic CNV features selected using the correlation-based machine learning method enabled the classification of the CNVs obtained into the D group, V group, total patient group, and control group with an average accuracy of 83.0%. The power of the diagnostic CNV features was 0.98 on average, suggesting that these CNV features could be used for the molecular diagnosis of the major clinical types of PMDD. This demonstrated concordance between the CNV profiles and clinical types of PMDD supported the validity of symptom-based diagnosis of PMDD for differentiating between its two major clinical types, as well as the predominantly genetic nature of PMDD with a host of overlaps between multiple susceptibility genes/pathways and the diagnostic CNV features as indicators of involvement in PMDD etiology.

经前烦躁症 (PMDD) 影响近 5% 的育龄妇女。症状的异质性，加上很大程度上未知的遗传学，极大地阻碍了其有效治疗。在本研究中，通过半监督聚类分析从 100 kb 白细胞 DNA 序列窗口调用的基于基因组测序的拷贝数变异 (CNV)，导致患者基因组分为 D 组和 V 组，这与抑郁症和侵袭性临床类型分别具有 89.0% 的一致性。应用使用基于相关性的机器学习方法选择的诊断 CNV 特征，能够将获得的 CNV 分类为 D 组、V 组、总患者组和对照组，平均准确率为 83.0%。诊断 CNV 特征的功效为 0。平均 98，表明这些 CNV 特征可用于 PMDD 主要临床类型的分子诊断。这表明 CNV 谱和 PMDD 临床类型之间的一致性支持基于症状的 PMDD 诊断以区分其两种主要临床类型的有效性，以及 PMDD 的主要遗传性质与多个易感基因之间的大量重叠/通路和诊断 CNV 特征作为参与 PMDD 病因的指标。