Automated genome-phenome analysis

全外显子组测序（WES）的成本下降正接近这样一个点，即WES在临床实践中的推广将在很大程度上受到解释结果和将结果与患者临床结果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发现的“表型”进行自动比较来降低这种解释成本的可行性。这使用SimulConsult诊断工具来提供“表型组分析”，然后与基因组分析结果集成以提供自动化的基因组-表型组分析。目的1是计算统一的严重性得分的基因组-表型分析，以取代目前的方法，使用迭代手动修改的布尔过滤的变体。新方法是一种基于定量严重性评分的一次性方法，然后通过与表型组的比较进行处理。这种方法结合了SeattleSeq提供的许多基因变异评估，包括群体中的保守性评分、读取质量评分和变异频率，以自动构建定量严重性评分。为了改进定量严重程度评分输入权重，将对10名已使用SimulConsult辅助诊断的患者进行分析。这建立在2012年添加到SimulConsult的能力基础上，以导入和处理WES结果的“变体表”，包括HGNC基因名称，严重程度评分和接合性。此外，还将添加导入多个变量表并使用交集进行计算的功能（即，两者都存在的变体），因此可以并入家族遗传信息。目的2是通过回顾性分析20例患者病例来评估自动基因组-表型组分析在识别患者中已知致病基因方面的有效性，其中已经对具有两个或更多受影响成员的家庭进行了WES，并发现了已知致病突变。诊断准确性将通过（1）正确诊断的等级和（2）由软件分配的概率。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。目的3是通过评估仅检查先证者与利用第二个受影响家庭成员的信息之间的差异，确定是否需要从家庭中的其他人获得基因组。总体目标是通过整合表型组信息与基因组信息，结合两种独立的诊断评估，创建和测试使WES分析更实用和更准确的能力。今天，口译费用超过了报销标准，对相关实验室的采访表明需要降低费用。随着表型在遗传异常中的比例越来越大，自动化基因组-表型组分析的适用性和市场将不断增长。

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.