Optimizing Genetic Testing for Deafness for Clinical Diagnostics

项目摘要 在这份目标驱动的提案中，为响应资助机会公告（FOA）而提交 编号PAR-11-003，我们将为临床医生提供全面的耳聋基因检测 每人500美元以下这一举措背后的驱动力是听力障碍的频率。 作为最常见的感觉障碍，每500名新生儿中就有1名被诊断出， （Morton Ann N Y Acad Sci 1991）. 57个基因与非综合征性听力损失有关 （NSHL），它也是一种极其异质的特征，对诊断提出了巨大的挑战。 目前的耳聋基因检测策略是不够的。对大多数人来说，只有少数基因 包括，选择标准通常反映：1）作为耳聋原因的高患病率（即GJB 2）; 2） 与另一个可识别的特征（即SLC 26 A4和扩大的前庭水管）相关;或3）a 可识别的音频特征（即，使用WFS 1观察到的低频听力损失）（希尔格特等人Mut Res 2009）。 然而，最近出现的强大的DNA靶富集和测序技术， 因此，有可能提供全面的耳聋基因检测，这是有效的和具有成本效益的。我们有 表明可以在一个平台上同时分析所有耳聋基因（称为 OtoSCOPE）（Shearer et al PNAS 2010）。与此相关的是，我们还验证了AudioGene作为一个 使用患者听力图预测ADNSHL遗传原因的表型工具（Hildebrand et al Genet Med 2008; Hildebrand等人Laryngoscope 2009）。 在这些调查结果的基础上，我们将在本提案中完成两个具体目标。 具体目标1：提供全面的、高通量的、低成本的DNA序列生成和 耳聋基因检测分析 目标1：用于基因检测的全面、高通量、低成本DNA序列分析， 耳聋是可能的，在敏感性和特异性可比桑格测序，通过使用靶向 序列富集，然后进行大规模平行测序。 具体目标2：优化听力数据的基于机器学习的音频分析， 通过扩展和改进我们开发的平台， AudioGene 目标2：作为一个现象组工具，一个机器学习软件系统在一组广泛的 听力测定数据可用于预测和消除作为听力损失原因的特定基因或基因变体。 基于听力测量数据的耳聋。 实现这些具体目标将改变耳聋和听力障碍的临床评估 通过使基因检测成为继病史、体检之后最重要的诊断测试， 检查和听力评估。

Project Summary In this goal-driven proposal, submitted in response to Funding Opportunity Announcement (FOA) Number PAR-11-003, we will make comprehensive genetic testing for deafness available to clinicians for under $500 per person. The driving force behind this initiative is the frequency of hearing impairment. As the most common sensory impairment, it is diagnosed in 1 of every 500 newborns and 50% of octogenarians (Morton Ann N Y Acad Sci 1991). With 57 genes implicated in nonsyndromic hearing loss (NSHL), it is also an extremely heterogeneous trait and presents a tremendous challenge to diagnosis. Current strategies for genetic testing for deafness are inadequate. For most, only a minority of genes is included, with selection criteria typically reflecting: 1) high prevalence as a cause of deafness (i.e. GJB2); 2) association with another recognizable feature (i.e. SLC26A4 and enlarged vestibular aqueduct); or 3) a recognizable audioprofile (i.e. low frequency hearing loss as seen with WFS1) (Hilgert et al Mut Res 2009). The recent advent of powerful DNA target enrichment and sequencing technologies, however, makes it possible to provide comprehensive genetic testing for deafness that is efficient and cost-effective. We have shown that it is possible to analyze all deafness genes simultaneously on a single platform (called OtoSCOPE) (Shearer et al PNAS 2010). Related to this endeavor, we have also validated AudioGene as a phenotypic tool that uses patient audiograms to predict the genetic cause of ADNSHL (Hildebrand et al Genet Med 2008; Hildebrand et al Laryngoscope 2009). Building on these findings, in this proposal we will complete two specific aims. Specific Aim 1: To provide comprehensive, high-throughput, low-cost DNA sequence generation and analysis for deafness genetic testing Goal 1: Comprehensive, high-throughput, low-cost DNA sequence analysis for genetic testing for deafness is possible at sensitivities and specificities comparable to Sanger sequencing by using targeted sequence enrichment followed by massively parallel sequencing. Specific Aim 2: To optimize both machine learning-based audioprofiling of audiometric data and phenotypic filtering of genotypic data by expanding and improving the platform we have developed called AudioGene Goal 2: As a phenome tool, a machine-learning software system trained on an extensive set of audiometric data can be used to predict and to eliminate specific genes or gene variants as causes of deafness based on audiometric data. Achieving these specific aims will change the clinical evaluation of deaf and hard-of-hearing persons by making genetic testing the most important diagnostic test after a history, physical examination and audiological assessment.