Machine learning-based genetic diagnosis models for hereditary hearing loss by the GJB2, SLC26A4 and MT-RNR1 variants

Xiaomei Luo; Fengmei Li; Wenchang Xu; Kaicheng Hong; Tao Yang; Jiansheng Chen; Xiaohe Chen; Hao Wu

doi:10.1016/j.ebiom.2021.103322

Machine learning-based genetic diagnosis models for hereditary hearing loss by the GJB2, SLC26A4 and MT-RNR1 variants

EBioMedicine. 2021 Jul:69:103322. doi: 10.1016/j.ebiom.2021.103322. Epub 2021 Jun 20.

Authors

Xiaomei Luo¹, Fengmei Li², Wenchang Xu², Kaicheng Hong², Tao Yang³, Jiansheng Chen⁴, Xiaohe Chen⁵, Hao Wu⁶

Affiliations

¹ University of Science and Technology of China, No.96 Jinzhai Road, Hefei, Anhui 230026, China, No.96 Jinzhai Road, Hefei, Anhui 230026, China; Department of Medical Electronics, Chinese Academy of Sciences, Suzhou Institute of Biomedical Engineering and Technology, No. 88, Keling Road, Suzhou, Jiangsu 215163, China.
² Department of Medical Electronics, Chinese Academy of Sciences, Suzhou Institute of Biomedical Engineering and Technology, No. 88, Keling Road, Suzhou, Jiangsu 215163, China.
³ Department of Otorhinolaryngology-Head and Neck Surgery, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Ear Institute, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China.
⁴ Department of Electronic Engineering, Tsinghua University, Beijing 100084, China; Beijing National Research Center for Information Science and Technology, Beijing 100084, China; University of Science and Technology Beijing, Beijing 100083, China.
⁵ University of Science and Technology of China, No.96 Jinzhai Road, Hefei, Anhui 230026, China, No.96 Jinzhai Road, Hefei, Anhui 230026, China; Department of Medical Electronics, Chinese Academy of Sciences, Suzhou Institute of Biomedical Engineering and Technology, No. 88, Keling Road, Suzhou, Jiangsu 215163, China. Electronic address: chenxh@sibet.ac.cn.
⁶ Department of Otorhinolaryngology-Head and Neck Surgery, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Ear Institute, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China. Electronic address: haowu@sh-jei.org.

Abstract

Background: Hereditary hearing loss (HHL) is the most common sensory deficit, which highly afflicts humans. With gene sequencing technology development, more variants will be identified and support genetic diagnoses, which is difficult for human experts to diagnose. This study aims to develop a machine learning-based genetic diagnosis model of HHL-related variants of GJB2, SLC26A4 and MT-RNR1.

Methods: This case-control study included 1898 subjects, among which 1354 were HHL patients and 544 were carriers. Risk assessment models were established based on variants at 144 sites in three genes related to HHL by building six machine learning (ML) models. We compared the ML models with the genetic risk score (GRS) and expert interpretation (EI) to verify the clinical performance.

Findings: Among the six ML models, the support vector machine (SVM) showed the best performance. For the prediction of HHL-related gene sites in subjects with variants, the area under the receiver operating characteristic (AUC) of the SVM model was 0.803 (0.680-0.814) in the 10-fold stratified cross-validation and 0.751 (0.635-0.779) in external validation. The predicted results were better than both EI and GRS. Furthermore, 11 sites were identified as the smallest feature set that can be accurately predicted.

Interpretation: The developed SVM model has great potential to be an efficient and effective tool for HHL prediction when high throughput sequencing data are available.

Funding: This study was supported by the National Key Research and Development Program (2017YFC1001800).

Keywords: Genetic diagnosis; Genetic risk score; Hereditary hearing loss; Machine learning.

MeSH terms

Adolescent
Adult
Child
Child, Preschool
Connexin 26 / genetics*
Diagnosis, Computer-Assisted / methods*
Female
Genetic Testing / methods*
Hearing Loss, Sensorineural / diagnosis
Hearing Loss, Sensorineural / genetics*
Humans
Infant
Male
Mutation
RNA, Ribosomal / genetics*
Sulfate Transporters / genetics*
Support Vector Machine

Substances

GJB2 protein, human
RNA, Ribosomal
RNA, ribosomal, 12S
SLC26A4 protein, human
Sulfate Transporters
Connexin 26