AI-based genetic discovery for hearing loss

基于人工智能的听力损失基因发现

• 批准号: 10708476
• 负责人: GARY A PELTZ
• 金额: $ 65.96万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-16 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10708476
• 关键词: Ablation; Acceleration; Alleles; Auditory; Auditory system; Candidate Disease Gene; Clustered Regularly Interspaced Short Palindromic Repeats; Cochlea; DNA Sequence Alteration; Data; Databases; Ear; Elderly; Engineering; Environment; Evaluation; Exons; Gene Expression; Gene Expression Profiling; Genes; Genetic; Genome; Genome engineering; Genomics; Goals; Health; Homologous Gene; Human; Human Genetics; Inbred Strain; Inbred Strains Mice; Individual; Knock-in; Knock-in Mouse; Knock-out; Knowledge; Laboratory mice; Methods; Modeling; Morphology; Mus; Mutation; Noise-Induced Hearing Loss; Phenotype; Presbycusis; Probability; Resources; Series; Shapes; Stretching; Variant; candidate identification; computational pipelines; computerized tools; genetic effector; genetic variant; genome wide association study; hearing impairment; improved; interest; model organism; mouse genetics; mouse genome; mouse model; novel; prevent; trait

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.

抽象的 与年龄相关的听力损失是老年人最常见的条件之一。听力的许多遗传因素 已经确定了损失，但还有更多的人有待识别；我们对 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）与年龄有关的听力损失。没有模型生物比 实验室鼠标以改善人类健康，而鼠标模型已经塑造了我们对 哺乳动物听觉系统。具有遗传突变的小鼠已被用来识别至关重要的基因 听觉功能，以及表征引起听力损失的人类遗传因素。 以寡聚为基础的自发听力损失在几种经过良好研究的近交小鼠菌株中形成 （A/J，DBA/2J，MA/MY，点头/LTJ，NOR/LTJ，C57BR/CDJ，C57L/J）。我们最近开发的基于AI的 计算管道（GNNHAP）确定了四个因自发听力损失的病因遗传因素 三个菌株（A/J，DBA/2，点头/LTJ）。但是，为了加速基因发现的听力损失的速度，这是 项目将通过使其能够分析近交基因组中存在的结构变体等位基因来增强我们的AI 菌株，并添加三个计算能力来优先考虑候选基因。增强的AI将 能够：（i）确定已鉴定小鼠候选基因的人类同源物中的等位基因是否是 与人类GWA的听力丧失有关； （ii）分析表型数据库以确定小鼠线是否 候选基因的淘汰赛损害了听力。 （iii）分析基因中的基因表达数据 表达分析资源（齿轮）确定是否确定了候选鼠基因（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 四种菌株听力丧失的遗传因素（MA/MY，NOR/LTJ，C57BR/CDJ，C57L/J）。因为至关重要 特征遗传效应器机制，最先进的基因组工程的状态用于产生敲击素 （ki）小鼠，它们对野生型听力丧失的病因遗传因素的恢复。详细的 对这些Ki小鼠进行评估以表征这些ki小鼠的个体（和组合）效应 听力损失和人工耳蜗的突变。其遗传效应器机制的表征将 揭示一组相互作用的寡原因子如何产生自发的听力损失。作为一个伸展目标，我们 将使用其中一些Ki小鼠来确定我们是否可以开发出一种新型的基因X环境模型以进行噪声 诱发听力损失。