Integrated pathogenicity assessment of clinically actionable genetic variants

临床可操作基因变异的综合致病性评估 好了！ 项目摘要/摘要 像我们所有人和百万老兵计划这样的大型生物库现在已经收集了来自 数以百万计的患者和其他人口健康研究正在迅速扩大。对《红楼梦》异体字的解读 在这样的项目中，临床上可操作的疾病基因正变得越来越常见。美国大学 医学遗传学和基因组学研究所建议，序列解释包括至少一组 59个基因，与测序适应症无关(ACMG 59)。这些基因负责各种不同的 临床症状，并已被广泛研究。然而，即使在研究得很好的疾病基因中， 大多数变异体只在一个或两个家系中观察到。这使得确定他们的角色变得具有挑战性 在疾病的起因中。此外，虽然可能有关于变种的现有证据，但它通常不足以 解释，因为数据库中的许多变体最初是在没有症状的小队列中发现的 匹配的对照组，因此他们的关联可能会受到对重要性或影响大小的错误估计， 而一小部分很可能是虚假的。 出于这些原因，临床基因组学的一个中心挑战是解释临床上可操作的变异。 在测序过程中识别的基因。因为ACMG59基因已经被深入研究了 对于它们的临床适用性，有独特的丰富的功能和结构数据可以用来 改进预测。在这里，我们建议开发新的数据，这些数据可以用于临床评估 变种，包括对结构后果的新预测，区域和结构信息选择性 来自临床诊断和流行病学健康数据的约束和临床风险。使用这些数据，我们将 开发贝叶斯统计模型来预测突变的影响，这些突变可以补充现有的 财团和临床实验室做出的评估。 这将具体包括努力集中改进结构和结构的计算预测 利用这些基因中每一个基因的广泛科学和医学知识对功能产生影响。接下来，我们 将这种结构性和功能性洞察力与大规模人口数据结合起来。我们将对统计数据进行测量 相关错义变异体组的变异异常，并识别出 富含与癌症相关的反复发生的体细胞或生殖系变异。最后，我们将开发一种 贝叶斯预测框架，集成了全套不同的观测和特征，以改进 预测单个变种的临床风险，并前瞻性地衡量其在临床上的表现 诊断实验室。好了！ 好了！

Integrated pathogenicity assessment of clinically actionable genetic variants ! Project Summary/Abstract Large biobanks such as All of Us and the Million Veteran Project have now collected genetic data from millions of patients, and other population health studies are expanding rapidly. The interpretation of variants in clinically actionable disease genes is becoming increasingly common in such projects. The American College of Medical Genetics and Genomics has recommended that sequence interpretation include a minimum set of 59 genes regardless of the indication for sequencing (ACMG 59). These genes are responsible for a variety of clinical syndromes and have been extensively studied. However, even in well-studied disease genes, the majority of variants are only observed in one or two families. which makes it challenging to be sure of their role in causation of disease. Further, while there may be existing evidence about a variant, it is often inadequate for interpretation, as many variants in databases were originally identified in small, symptomatic cohorts without matched control groups, so their associations can suffer from incorrect estimates of significance or effect size, and a non-trivial fraction are likely to be spurious. For these reasons, a central challenge in clinical genomics is to interpret variants in clinically actionable genes that are identified during sequencing. Because the ACMG 59 genes have been studied intensively due to their clinical applicability, there is a unique abundance of functional and structural data that can be used to improve predictions. Here, we propose to develop new data that can be leveraged in the clinical assessment of variants including novel predictions of structural consequences, regional and structurally-informed selective constraint, and clinical risk from clinical diagnostic and epidemiologic health data. Using these data, we will develop a Bayesian statistical model to predict the effects of mutations that can complement existing assessments made by consortia and clinical laboratories. This will specifically include efforts to intensively improve computational predictions of structural and functional impact using the extensive scientific and medical knowledge in each of these genes. Next, we combine that structural and functional insight with large-scale population data. We will measure statistical aberration of variation for related groups of missense variants, and also identify groups of variant sites which are enriched in recurrent somatic or germline variation associated with cancer. Finally, we will develop a Bayesian prediction framework that integrates the full set of variant observations and characteristics to improve predictions of clinical risk for individual variants, and prospectively measure its performance in a clinical diagnostic laboratory. ! !