Background: High-throughput sequencing of genes indicating susceptibility to noise-induced hearing loss has not previously been reported.
Aims/objectives: To identify and analyze genes associated with susceptibility to noise-induced hearing loss (NIHL) and characterize differences in susceptibility to hearing loss by genotype.
Material and methods: Pure tone audiometry tests were performed on 113 workers exposed to high-intensity noise. Whole-exome sequencing (WES) was conducted and NIHL susceptibility genes screened for training unsupervised and supervised machine learning models. Immunofluorescence staining of mouse cochlea was used to observe patterns of NIHL susceptibility gene expression.
Results: Participants were divided into a NIHL and a control group, according to the results of audiometry tests. Seventy-three possible NIHL susceptibility genes were input into the machine learning model. Two subgroups of NIHL could be distinguished by unsupervised machine learning and the classification was evaluated by the supervised machine learning algorithm. The VWF gene had the highest mutation frequency in the NIHL group and was expressed mainly in the spiral ligament.
Conclusions and significance: NIHL susceptibility genes were screened and NIHL subgroups could be distinguished. VWF may be a novel NIHL susceptibility gene.
Keywords: Noise-induced hearing loss; machine learning; prediction model; whole-exome sequencing.