Integrated pathogenicity assessment of clinically actionable genetic variants

临床上可操作的遗传变异体的综合致病性评估 ! 项目总结/摘要 大型生物银行，如我们所有人和百万退伍军人项目，现在已经收集了遗传数据， 数以百万计的患者，以及其他人口健康研究正在迅速扩大。中变体的解释 临床上可操作的疾病基因在这样的项目中变得越来越普遍。美国大学 医学遗传学和基因组学的专家建议，序列解释包括一组最小的 59个基因，无论测序适应症如何（ACMG 59）。这些基因负责多种 临床症状，并已被广泛研究。然而，即使在研究充分的疾病基因中， 大多数变异仅在一个或两个家族中观察到。这使得确定他们的角色变得很有挑战性 疾病的起因。此外，虽然可能存在关于变体的现有证据，但它通常不足以用于 解释，因为数据库中的许多变异最初是在小的有症状的队列中发现的， 匹配的对照组，因此它们的关联可能会受到显著性或效应大小的错误估计的影响， 和非平凡分数很可能是假的。 由于这些原因，临床基因组学的中心挑战是解释临床上可操作的基因组中的变异。 在测序过程中识别的基因。由于ACMG 59基因已被深入研究， 对于它们的临床适用性，有独特丰富的功能和结构数据可用于 改善预测。在这里，我们建议开发新的数据，可用于临床评估， 变体，包括结构后果的新预测，区域和结构信息选择性 限制和临床诊断和流行病学健康数据的临床风险。利用这些数据，我们将 开发贝叶斯统计模型来预测突变的影响， 联合会和临床实验室进行的评估。 这将特别包括努力深入改进结构和 利用这些基因中的每一个的广泛的科学和医学知识来研究功能影响。接下来我们 联合收割机将结构和功能见解与大规模人口数据相结合。我们将统计 相关错义变体组的变异畸变，并且还鉴定了 富含与癌症相关的复发性体细胞或生殖系变异。最后，我们将开发一个 贝叶斯预测框架，集成了全套变量观测和特征， 预测个体变异的临床风险，并前瞻性地测量其在临床 诊断实验室! !

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. ! !