Optimizing Genetic Testing for Deafness for Clinical Diagnostics

优化耳聋基因检测以进行临床诊断

• 批准号: 8224101
• 负责人: Richard J.H. Smith
• 金额: $ 60.81万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-21 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8224101
• 关键词: Audiometry; Bar Codes; Caring; Clinical; DNA; DNA Sequence; DNA Sequence Analysis; Data; Data Analyses; Data Set; Detection; Diagnosis; Diagnostic; Diagnostic tests; Dideoxy Chain Termination DNA Sequencing; Family; Frequencies; Funding Opportunities; Generations; Genes; Genetic; Genetic Variation; Genetic screening method; Genets; Goals; Hearing Impaired Persons; High Prevalence; Impairment; Laboratories; Laryngoscopes; Life; Link; Low Frequency Deafness; Machine Learning; Minority; Mutation; Newborn Infant; Octogenarian; Patients; Persons; Physical Examination; Recording of previous events; Resources; Sampling; Screening procedure; Selection Criteria; Sensitivity and Specificity; Sensory; Sequence Analysis; System; Technology; Training; Usher Syndrome; Variant; Vestibular Aqueduct; base; cost; cost effective; deafness; driving force; hearing impairment; improved; meetings; phenome; research clinical testing; response; software systems; tool; trait

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: 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. Achieving this goal will change the clinical evaluation of deaf and hard-of-hearing persons by making genetic testing the most important diagnostic test after a history and physical exam.

描述（由申请人提供）：在这份根据资助机会公告 (FOA) 编号 PAR-11-003 提交的目标驱动提案中，我们将为临床医生提供全面的耳聋基因检测，每人费用低于 500 美元。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 名新生儿和 50% 的八旬老人中就有 1 人被诊断出患有该病（Morton Ann N Y Acad Sci 1991）。有 57 个基因与非综合征性听力损失 (NSHL) 相关，它也是一种极其异质的特征，给诊断带来了巨大的挑战。 目前的耳聋基因检测策略还不够充分。对于大多数基因来说，仅包括少数基因，选择标准通常反映：1）作为耳聋原因的高患病率（即 GJB2）； 2）与另一个可识别特征（即SLC26A4和扩大的前庭导水管）相关；或 3) 可识别的音频特征（即 WFS1 中出现的低频听力损失）（Hilgert et al Mut Res 2009）。 然而，最近出现的强大的 DNA 靶标富集和测序技术使得为耳聋提供高效且具有成本效益的全面基因检测成为可能。我们已经证明，可以在一个平台（称为 OtoSCOPE）上同时分析所有耳聋基因（Shearer 等人 PNAS 2010）。与这一努力相关，我们还验证了 AudioGene 作为一种表型工具，它使用患者听力图来预测 ADNSHL 的遗传原因（Hildebrand 等 Genet Med 2008；Hildebrand 等 Laryngoscopy 2009）。 基于这些发现，在本提案中我们将完成两个具体目标。 具体目标 1：为耳聋基因检测提供全面、高通量、低成本的 DNA 序列生成和分析 目标 1：通过使用靶向序列富集和大规模并行测序，可以在与桑格测序相当的灵敏度和特异性下，为耳聋基因检测提供全面、高通量、低成本的 DNA 序列分析。 具体目标 2：通过扩展和改进我们开发的名为 AudioGene 的平台，优化基于机器学习的听力数据音频分析和基因型数据的表型过滤。 目标 2：作为一种现象组工具，经过大量听力数据训练的机器学习软件系统可用于根据听力数据预测和消除导致耳聋的特定基因或基因变异。 实现这些具体目标将使基因检测成为继病史、体格检查和听力学评估之后最重要的诊断测试，从而改变聋哑人和听力障碍者的临床评估。 公共健康相关性：在这份为响应资助机会公告 (FOA) PAR-11-003 号而提交的目标驱动提案中，我们将为临床医生提供全面的耳聋基因检测，每人费用低于 500 美元。实现这一目标将使基因检测成为继病史和体检之后最重要的诊断测试，从而改变聋哑人和听力障碍者的临床评估。