New quantitative approaches to interpret variant pathogenicity

项目摘要 没有足够的知识和能力来解释NEXT确定的基因变异的致病性 基因测序是基因组医学实施的一个主要瓶颈。《美国人》 医学遗传学和基因组学与分子病理学协会(ACMG/AMP)指南 确定高置信度的致病和可能致病的变异，但在可伸缩性方面受到限制。许多变种 被ACMG/AMP指南归类为意义不确定的变体，但没有说明 这些变异或多或少都有可能是致病的，导致不适当的医疗。因此，我 建议开发标准化的定量方法来提高我们解释基因组变异的能力 在高吞吐量的情况下准确。In-silo工具通常用于根据以下条件指定变异型致病性 但它们的预测精度是有限的。目前的方法还没有经过校准。 基因，相同的致病性分数并不能推断出不同基因致病的可能性相同 基因。在这项建议中，1)我的目标是重新校准包含特定基因特征的致病分数。 使致病性分数在不同基因之间更具可比性，并提高致病性的准确性 利用高级深度神经网络模型和饱和诱变的函数数据进行预测 学习。2)对ACMG/AMP变异分类进行量化，并给出变异概率 使用高级监督学习和利用一个大病例来研究临床相关基因的致病性 控制队列。改进的计算预测(目标1)将改进下游的不同优先顺序 分析和加强ACMG/AMP指南中使用的计算证据。估计 基于ACMG/AMP指南的变异致病概率(目标2)将改善沟通 在实验室、医疗保健提供者和患者之间通知基因检测结果。

Project Summary Insufficient knowledge and throughput to interpret pathogenicity of genetic variants identified by next generation sequencing (NGS) is a major bottleneck for genomic medicine implementation. The American College of Medical Genetics and Genomics and Association for Molecular Pathology (ACMG/AMP) guidelines identify high-confidence pathogenic and likely pathogenic variants but are limited in scalability. Many variants are classified as variants of uncertain significance by the ACMG/AMP guidelines without an indication of which of these variants are more or less likely to be pathogenic, leading to inappropriate medical treatment. Hence, I propose to develop standardized quantitative approaches to improve our ability to interpret genomic variations accurately at high-throughput. In-silico tools are commonly used to assign variant pathogenicity based on conservation, but their predictive accuracy is limited. The current methods have not been calibrated across genes, and the same pathogenicity score does not infer the same likelihood of pathogenicity across different genes. In this proposal, 1) I aim to recalibrate the pathogenicity scores incorporating gene-specific features making the pathogenicity scores more comparable across genes, and improve the accuracy of pathogenicity predictions using advanced deep neural network models and functional data from saturation mutagenesis studies. 2) I aim to quantify the ACMG/AMP variant classification and provide probability of variant pathogenicity for clinically relevant genes using advanced supervised learning and leveraging a large case- control cohort. The improved computational predictions (Aim 1) will refine variant prioritization for downstream analyses and strengthen the computational evidence used in the ACMG/AMP guidelines. The estimated probability of variant pathogenicity based on ACMG/AMP guideline (Aim 2) will improve communication between laboratories, health care providers and patients about genetic test results.