Copy number variation profile-based genomic typing of premenstrual dysphoric disorder in Chinese.
J Genet Genomics
; 48(12): 1070-1080, 2021 12.
Article
em En
| MEDLINE
| ID: mdl-34530168
ABSTRACT
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.
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Base de dados:
MEDLINE
Assunto principal:
Variações do Número de Cópias de DNA
/
Transtorno Disfórico Pré-Menstrual
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article