Optimizing Genetic Testing for Deafness for Clinical Diagnostics

项目摘要 在这份以目标为导向的建议书中，根据融资机会公告(FOA)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 每人不到500美元。这一倡议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人被诊断出患有这种疾病，50%的新生儿 《八旬老人》(Morton Ann N Y Acad Sci，1991)。57个基因与非综合征性听力损失有关 (NSHL)，它也是一种极端异质性的特征，给诊断带来了巨大的挑战。 目前针对耳聋的基因检测策略是不充分的。对大多数人来说，只有少数基因是 包括在内，选择标准通常反映：1)作为耳聋原因的高患病率(即GJB2)；2) 与另一个可识别的特征(即SLC26A4和扩大的前庭导水管)有关；或3)a 可识别的音频特征(即WFS1所见的低频听力损失)(Hilgert等人Mut Res 2009)。 然而，最近出现的强大的DNA靶标浓缩和测序技术使其 有可能为耳聋提供全面的基因检测，既高效又经济。我们有 表明在单个平台上同时分析所有耳聋基因(称为 耳镜)(Sheeller Et Al PNAS 2010)。与这一努力相关的是，我们还验证了Audiogene是一种 使用患者听力图来预测ADNSHL遗传原因的表型工具(Hildebrand等人 Genet Med，2008；Hildebrand等人喉镜，2009)。 在这些发现的基础上，在这项提案中，我们将完成两个具体目标。 具体目标1：提供全面、高通量、低成本的DNA序列生成和 关于耳聋基因检测的分析 目标1：用于基因检测的全面、高通量、低成本的DNA序列分析 通过使用靶向测序，耳聋是可能的，其敏感性和特异性可与桑格测序相媲美 序列浓缩和大规模并行测序。 具体目标2：优化基于机器学习的听力测量数据的音频分析和 通过扩展和改进我们开发的名为 Audiogene 目标2：作为一种现象组工具，一个机器学习软件系统在一组广泛的 听力测量数据可以用来预测和消除特定的基因或基因变异作为致病原因 基于听力测量数据的耳聋。 这些特定目标的实现将改变聋哑人的临床评估 通过使基因测试成为继历史、体检之后最重要的诊断测试 检查和听力学评估。

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.