Automated genome-phenome analysis

自动化基因组-表型分析

• 批准号: 8455053
• 负责人: MICHAEL M SEGAL
• 金额: $ 26.14万
• 依托单位: SIMULCONSULT, INC.
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-27 至 2013-09-26
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8455053
• 关键词: Affect; Age of Onset; Candidate Disease Gene; Clinical; Code; Computer software; Data; Databases; Diagnosis; Diagnostic; Disease; Effectiveness; Family; Family member; Fees; Frequencies; Genes; Genetic; Genetic Counseling; Genetic Variation; Genome; Goals; Incidence; Individual; Interview; Manuals; Marketing; Methods; Metric; Modification; Molecular Abnormality; Names; Pathogenicity; Patients; Phase; Phenotype; Physicians; Population; Positioning Attribute; Probability; Process; Reading; Severities; Small Business Innovation Research Grant; Speed; TACSTD1 gene; Testing; Time; Variant; Weight; Work; base; clinical practice; commercialization; cost; diagnostic accuracy; disease-causing mutation; exome sequencing; experience; genome analysis; genome sequencing; improved; member; neurogenetics; novel strategies; phenome; proband; programs; prototype; public health relevance; text searching; tool

DESCRIPTION (provided by applicant): Automated genome-phenome analysis 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 the to the patient's clinical findings. This project tests the feasibility of reducing this interpretaton cost by pairing automated genome sequencing with an automated comparison of the patient's findings to the "phenotype" of findings of known diseases. This uses the SimulConsult diagnostic tool to provide "phenome analysis", and then integrate with genome analysis results to provide an automated genome-phenome analysis. Aim 1 is to compute unified severity scores for genome-phenome analysis so as to replace the current methods, which use iterative manual modifications of Boolean filtering of variants. The new approach is a one- pass method based on quantitative severity scores that are then processed by comparison to the phenome. This approach combines many assessments of gene variants provided by SeattleSeq, including conservation scores, read quality scores and variant frequency in the population, to automatically construct quantitative severity scores. To refine the quantitative severity score input weightings, 10 patients will be analyzed for whom SimulConsult has already been used to assist in diagnosis. This builds upon the ability added in 2012 to SimulConsult to import and process the "variant table" of WES results that includes the HGNC gene name, severity score, and zygosity. Also, the ability will be added to import more than one variant table and compute with the intersection (i.e., variants present in both) so familial genetic information can be incorporated. Aim 2 is to assess the effectiveness of automated genome-phenome analysis to identify known disease- causing genes in patients by retrospectively analyzing 20 patient cases in which WES was already performed on a family with two or more affected members and a known disease-causing mutation was found. The diagnostic accuracy will be assessed by (1) the rank of the correct diagnosis and (2) the probability assigned by the software. This will compare the genome alone, phenome alone, and genome + phenome approaches, as well as other situations involving incidence and onset ages. Aim 3 is to determine the need for having genomes from others in the family, by assessing differences between examining only the proband versus utilizing information on a second affected family member. The overall goal is to create and test the capability for making WES more practical to analyze and more accurate by integrating phenome information with the genome information, combining two independent assessments of the diagnosis. Today, interpretation costs exceed reimbursement rates, and interviews with relevant labs suggest need for lower costs. As the phenotype becomes known for a greater fraction of genetic abnormalities, the applicability of the automated genome-phenome analysis and the market for it will grow.

描述（由申请人提供）：自动化基因组表型分析 全外显子组测序 (WES) 的成本不断下降，其在临床实践中的传播将在很大程度上受到解释结果以及与患者临床发现进行比较的成本的限制。该项目测试了通过将自动基因组测序与患者的发现与已知疾病的“表型”的自动比较相结合来降低这种解释成本的可行性。它使用 SimulConsult 诊断工具提供“表型分析”，然后与基因组分析结果集成以提供自动化的基因组表型分析。目标 1 是计算基因组表型组分析的统一严重性评分，以取代当前使用变体布尔过滤的迭代手动修改的方法。新方法是一种基于定量严重性评分的一次性方法，然后通过与现象组进行比较进行处理。这种方法结合了 SeattleSeq 提供的许多基因变异评估，包括保守评分、读取质量评分和群体中的变异频率，以自动构建定量严重性评分。为了细化定量严重程度评分输入权重，将对 10 名已使用 SimulConsult 协助诊断的患者进行分析。这是建立在 2012 年 SimulConsult 新增功能的基础上的，用于导入和处理 WES 结果的“变异表”，其中包括 HGNC 基因名称、严重性评分和接合性。此外，还将添加导入多个变异表并计算交集（即两者中都存在变异）的功能，以便可以合并家族遗传信息。目标 2 是通过回顾性分析 20 例患者病例，评估自动基因组表型分析在识别患者已知致病基因方面的有效性，在这些病例中，已经对有两名或更多受影响成员的家庭进行了 WES，并发现了已知的致病突变。诊断准确性将通过 (1) 正确诊断的等级和 (2) 软件分配的概率来评估。这将比较单独的基因组、单独的表型组和基因组+表型组方法，以及涉及发病率和发病年龄的其他情况。目标 3 是通过评估仅检查先证者与利用第二位受影响家庭成员的信息之间的差异，确定是否需要获得家庭中其他人的基因组。总体目标是通过将表型信息与基因组信息相结合，结合两次独立的诊断评估，创建和测试使 WES 分析更实用、更准确的能力。如今，口译成本超过了报销率，对相关实验室的采访表明需要降低成本。随着表型因更大比例的遗传异常而闻名，自动化基因组-表型分析的适用性及其市场将会增长。