Identifying Genetic Variants Associated With Noise-induced Hearing Loss Based on a Novel Strategy for Evaluating Individual Susceptibility

Abstract

Background: The overall genetic profile for noise-induced hearing loss (NIHL) remains to be explored. Here we used a novel machine learning (ML) strategy to evaluate individual susceptibility to NIHL and identify the underlying genetic variants based on a subsample of participants with extreme phenotype. Methods: Demographic and audiometric data of 5,539 shipbuilding workers from large cross-sectional surveys were included in four ML algorithms to predict the hearing level. The area under the curve (AUC) and prediction accuracy were used to assess the performance of the classification models. We screened 300 participants that were misclassified by all of the four ML models, with extreme phenotypes implying they were either highly susceptible or resistant to NIHL and used whole-exome sequencing (WES) to identify the underlying variants associated with NIHL risk among the NIHL-susceptible and NIHL-resistant individuals. Subsequently, candidate risk loci were validated in a large independent noise-exposed cohort, followed by a meta-analysis.Results: With 10-fold cross-validation, the performances of the four ML models were robust and similar, with average AUC and accuracy ranging from 0.783 to 0.798 and 73.7% to 73.8%, respectively. The phenotypes of the NIHL-susceptible group and NIHL-resistant group were significantly different (all p<0.001). After WES analysis and filtering, 12 novel variants contributing to NIHL susceptibility were identified and replicated. The meta-analyses shown that the rs41281334 A allele of CDH23 (OR=1.506, 95% CI=1.106-2.051) and the rs12339210 C allele of WHRN (OR=3.06, 95% CI=1.398-6.700) were significantly associated with increased risk of NIHL after adjustment for conventional risk factors.Conclusions: This study determined two novel genetic variants in CDH23 rs41281334 and WHRN rs12339210 associated with NIHL risk, based on a potential approach for evaluating individual susceptibility using ML models. Trial registration: Chinese Clinical Trial Registry (registration number: ChiCTR-RPC-17012580)