Extending Genome-Phenome Analysis

描述（由申请人提供）：全外显子组测序（WES）成本的下降正接近WES在临床实践中的推广将在很大程度上受到解释结果并将其与患者临床结果进行比较的成本的限制。该项目建立在我们的证明能力，以减少这种解释成本配对我们的诊断软件，广泛用于临床诊断，与自动基因组测序。临床诊断软件将患者与已知疾病中发现的“表型”进行比较，因此在SBIR第1阶段资助下开发的基因组分析组合被称为自动基因组表型分析。这种屡获殊荣的能力之所以受到重视，是因为它能够在几秒钟内分析基因组，并且具有独立于假设的性质。在这里，我们建议推进基因组-表型组分析如下：目的1是推广的分析超出了三人组（受影响的个人加父母），以支持更广泛的家庭结构。这些包括有一个以上兄弟姐妹的核心家庭，核心家庭以外的家庭和无关的受影响个人。这些能力将有助于临床诊断和发现基因与疾病之间的新联系。这些功能将以保持分析的速度和独立于假设的性质的方式添加。目的2是使用外显子组检测拷贝数变异（CNV）并在临床背景下分析基因组数据。使用WES进行CNV分析将通过减少在外显子组分析之前订购微阵列的需要来降低诊断成本，并且将促进临床护理中DNA缺失和重复的自动化分析。目标3是通过考虑哪些基因是良好阅读但正常的，这些信息对于排除其他诊断很重要，从而改进核心分析。该分析还将处理受影响个体是纯合还是杂合的模糊情况，并且这样做的方式只增加诊断的可能性，但不会减少原始分析所考虑的可能性。目标4是通过报告偶然发现和以便于与转诊医生互动的方式导出信息以及向公共数据库报告基因组变异来提高产出。总体目标是提高准确性，减少分析的时间和成本，使WES作为临床工具和基因发现工具更加强大。如今，口译成本超过了报销率，对实验室的采访表明，高成本的主要原因是临床相关性的手动性质，我们将其自动化。随着表型成为已知的遗传异常的更大部分，我们的自动基因组-表型分析的适用性和市场将增长。

DESCRIPTION (provided by applicant): The declining cost of whole exome sequencing (WES) is nearing the point at which the spread of WES into clinical practice will be limited largely by the cost of interpreting the results and comparing them to the patient's clinical findings. This project builds on our demonstrated capability to reduce this interpretation cost by pairing our diagnostic software, in wide use for clinical diagnosis, with automated genomic sequencing. The clinical diagnostic software compares patients to "phenotypes" of findings in known diseases, so the combination with genome analysis, developed under an SBIR Phase 1 grant, is referred to as automated genome-phenome analysis. This award-winning capability is valued because of its ability to analyze genomes in seconds, and its hypothesis-independent nature. Here we propose to advance the genome-phenome analysis as follows: Aim 1 is to generalize the analysis beyond the trio (affected individual plus parents) in order to support a wider variety of family structures. These include nuclear families with more than one sibling, families that extend beyond the nuclear family and unrelated affected individuals. These capabilities will be useful in both clinical diagnosis and discovery of new connections between genes and diseases. These capabilities will be added in a way that preserves the speed and hypothesis-independent nature of the analysis. Aim 2 is to detect copy number variation (CNV) using exomes and analyze that genomic data in the clinical context. Using WES for CNV analysis will lower the cost of diagnosis by reducing the need to order a microarray before exome analysis, and will facilitate the automated analysis of DNA deletions and duplications in clinical care. Aim 3 is to improve the core analysis by taking into account which genes were well-read but normal, information that is important in excluding other diagnoses. The analysis will also deal with situations of ambiguity over whether an affected individual is homozygous or heterozygous, and do so in a way that only adds possibilities for diagnosis but doesn't reduce possibilities considered by the original analysis. Aim 4 is to improve output by reporting on incidental findings and exporting information in ways that facilitate interactions with referring physicians and reporting of genome variants to public databases. The overall goal is to improve accuracy and reduce the time and cost of analysis, making WES more robust as a clinical tool, as well as a tool for gene discovery. Today, interpretation costs exceed reimbursement rates, and interviews with labs suggest that the major reason for high costs is the manual nature of the clinical correlation, which we automate. As the phenotype becomes known for a greater fraction of genetic abnormalities, the applicability of our automated genome-phenome analysis and the market for it will grow.