AI-based genetic discovery for hearing loss

摘要 年龄相关性听力损失是老年人最常见的病症之一。影响听力的遗传因素很多 已查明损失，但还有更多损失有待查明；以及我们对这方面知识的缺乏 如果我们要开发方法，它们导致听力损失的机制是必须克服的障碍 用于预防（或逆转）与年龄相关的听力损失。没有任何模式生物体的贡献能超过 实验室小鼠改善人类健康，小鼠模型塑造了我们对 哺乳动物的听觉系统。具有基因突变的小鼠已被用来识别对疾病至关重要的基因。 听觉功能，以及表征导致听力损失的人类遗传因素。 几种经过充分研究的近交系小鼠品系会出现寡基因自发性听力损失 （A/J、DBA/2J、MA/My、NOD/LtJ、NOR/LtJ、C57BR/cdJ、C57L/J）。我们最近开发的基于人工智能的 计算管道（GNNHap）确定了自发性听力损失的四个致病遗传因素 三种菌株（A/J、DBA/2、NOD/LtJ）。然而，为了加快听力损失基因发现的步伐，这 该项目将通过分析近交基因组中存在的结构变异等位基因来增强我们的人工智能 菌株，并添加三种计算能力来优先考虑候选基因。增强型人工智能将 能够：（i）确定已识别的小鼠候选基因的人类同源物中的等位基因是否是 与人类 GWAS 中的听力损失相关； (ii) 分析表型数据库以确定小鼠品系是否 候选基因被敲除后听力受损； (iii) 分析 Gene 中的基因表达数据 表达分析资源 (gEAR)，用于确定是否已识别出候选鼠基因（及其 人类同源物）在耳朵中表达。然后，增强的计算工具将用于识别 四种品系听力损失的遗传因素（MA/My、NOR/LtJ、C57BR/cdJ、C57L/J）。由于这至关重要 表征遗传效应机制，使用最先进的基因组工程来产生敲入 (KI) 小鼠，其听力损失的致病遗传因素逆转为野生型。详细的 对这些 KI 小鼠的评估是为了表征这些 KI 小鼠的个体（和组合）效应 听力损失和耳蜗形态的突变。其遗传效应机制的表征将 揭示一组相互作用的寡基因如何导致自发性听力损失。作为一个延伸目标，我们 将使用其中一些 KI 小鼠来确定我们是否可以开发一种新的噪声基因 x 环境模型 诱发听力损失。

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.