Automated genome-phenome analysis

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）的成本正在下降，其在临床实践中的推广将在很大程度上受到解释结果和与患者临床结果进行比较的成本的限制。该项目通过将自动基因组测序与患者发现与已知疾病发现的“表型”进行自动比较来测试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，提供自动化的基因组-表型分析。目的1是为基因组-表型分析计算统一的严重性评分，以取代目前使用迭代人工修改布尔滤波的方法。新方法是一种基于定量严重性分数的一次性方法，然后通过与现象的比较进行处理。该方法结合了SeattleSeq提供的许多基因变异评估，包括保守性评分、读取质量评分和种群中的变异频率，以自动构建定量的严重性评分。为了完善量化严重程度评分输入权重，将分析10名已经使用SimulConsult辅助诊断的患者。这是建立在2012年添加到SimulConsult的功能基础上的，该功能可以导入和处理WES结果的“变异表”，其中包括HGNC基因名称、严重程度评分和合子。此外，还将添加导入多个变体表并计算交集的功能（即，在两者中都存在的变体），因此可以合并家族遗传信息。目的2是通过回顾性分析20例患者病例，评估自动化基因组-表型分析在识别患者已知致病基因方面的有效性，这些患者已经对一个有两个或更多受影响成员的家庭进行了WES检查，并发现了已知的致病突变。诊断的准确性将通过(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其他成员那里获得基因组。总体目标是通过整合表型信息和基因组信息，结合两种独立的诊断评估，创建和测试使WES更实用、更准确的分析能力。今天，口译费用超过了偿还率，与相关实验室的访谈表明需要降低成本。随着表型在遗传异常中所占的比例越来越大，自动化基因组-表型分析的适用性和市场将会增长。

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.