Copy number variation profile-based genomic typing of premenstrual dysphoric disorder in Chinese

Hong Xue; Zhenggang Wu; Xi Long; Ata Ullah; Si Chen; Wai-Kin Mat; Peng Sun; Ming-Zhou Gao; Jie-Qiong Wang; Hai-Jun Wang; Xia Li; Wen-Jun Sun; Ming-Qi Qiao

doi:10.1016/j.jgg.2021.08.012

Volume 48 Issue 12

Dec. 2021

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Article Navigation > Journal of Genetics and Genomics > 2021 > 48(12): 1070-1080

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Copy number variation profile-based genomic typing of premenstrual dysphoric disorder in Chinese

doi: 10.1016/j.jgg.2021.08.012

a. Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250355, China;
b. Division of Life Science and Applied Genomics Center, Hong Kong University of Science and Technology, Hong Kong, China;
c. School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, Jiangsu 210009, China;
d. HKUST Shenzhen Research Institute, Nanshan, Shenzhen 518057, China;
e. Guangzhou HKUST Fok Ying Tung Research Institute, Science and Technology Building, Nansha Information Technology Park, Nansha, Guangzhou 511458, China

Funds:

and 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), respectively.

Guangdong Province Basic and Applied Basic Research Fund (2021A1515011169)

Shandong Province First Class Disciple Development Grant and Tai-Shan Scholar Program, Shandong

UROP18SC06

Shenzhen Municipal Council of Science and Technology, Guangdong (JCYJ20170818113656988)

ITCPD/17-9

UROP20SC07) and Innovation and Technology Commission (ITS/085/10

ITT/026/18GP) of Hong Kong SAR

ITT/023/17GP

We thank Ms. Peggy Lee for technical support. The study was supported by grants to HX from University Grants Council (SRF116SC01

ITS113/15FP

Received Date: 2021-03-10
Accepted Date: 2021-08-13
Rev Recd Date: 2021-08-13
Publish Date: 2021-12-20

Abstract

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.
- Clinical subtyping,
- Genomic sequencing,
- Machine learning,
- Recurrent copy number variation,
- Replication phase,
- Semisupervized

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References(33)

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