AI-based genetic discovery for hearing loss

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Abstract Age-related hearing loss is one of the most common conditions in the elderly. Many genetic factors for hearing loss have been identified, but many more remain to be identified; and our lack of knowledge about the mechanisms by which they cause hearing loss is a barrier that must be overcome if we are to develop methods for preventing (or reversing) age-related hearing loss. No model organism has contributed more than the laboratory mouse to improving human health, and mouse models have shaped our understanding of the mammalian auditory system. Mice with genetic mutations have been used to identify genes that are critical for auditory function, and for characterizing human genetic factors that cause hearing loss. A spontaneous hearing loss with an oligogenic basis develops in several well-studied inbred mouse strains (A/J, DBA/2J, MA/My, NOD/LtJ, NOR/LtJ, C57BR/cdJ, C57L/J). Our recently developed AI-based computational pipeline (GNNHap) identified four causative genetic factors for spontaneous hearing loss in three strains (A/J, DBA/2, NOD/LtJ). However, to accelerate the pace of genetic discovery for hearing loss, this project will enhance our AI by enabling it to analyze structural variant alleles present in the genomes of inbred strains, and by adding three computational capabilities for prioritizing candidate genes. The enhanced AI will be able to: (i) determine if alleles within the human homologues of identified mouse candidate genes were associated with hearing loss in human GWAS; (ii) analyze a phenotypic database to determine if a mouse line with a knockout of a candidate gene has impaired hearing; and (iii) analyze gene expression data in the Gene Expression Analysis Resource (gEAR) to determine whether identified candidate murine genes (and their human homologues) are expressed in the ear. The enhanced computational tool will then be used to identify genetic factors for hearing loss in four strains (MA/My, NOR/LtJ, C57BR/cdJ, C57L/J). Since it is critical to characterize genetic effector mechanisms, state of the art genome engineering is used to generate knockin (KI) mice, which have a reversion of a causative genetic factor for hearing loss to wild type. A detailed evaluation of these KI mice is performed to characterize the individual (and combined) effect of these mutations on hearing loss and cochlear morphology. Characterization of their genetic effector mechanisms will reveal how a set of interacting oligogenic factors produce a spontaneous hearing loss. As a stretch goal, we will use some of these KI mice to determine if we can develop a novel gene x environment model for noise- induced hearing loss.