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 stu...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.展开更多
基金supported by grants to HX from University Grants Council(SRF116SC01UROP18SC06+10 种基金UROP20SC07)Innovation and Technology Commission(ITS/085/10ITS113/15FPITCPD/17-9ITT/023/17GPITT/026/18GP)of Hong Kong SARShenzhen Municipal Council of Science and Technology,Guangdong(JCYJ20170818113656988)Guangdong Province Basic and Applied Basic Research Fund(2021A1515011169)Shandong Province First Class Disciple Development Grant and Tai-Shan Scholar Program,Shandongand Ministry of Science and Technology(National Science and Technology Major Project,No.2017ZX09301064,2017ZX09301064004)People’s Republic of China,as well as grants from National Natural Science Foundation of China to M.Q.(8157151623)and J.W.(81603510)。
文摘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.
