New quantitative approaches to interpret variant pathogenicity

项目摘要 知识和通量不足以解释next鉴定的遗传变异的致病性 世代测序（NGS）是基因组医学实施的主要瓶颈。美国 医学遗传学和基因组学学院和分子病理学协会（ACMG/AMP）指南 鉴定高置信度致病性和可能致病性变体，但可扩展性有限。许多变体 被ACMG/AMP指南归类为不确定意义的变体， 这些变异或多或少可能是致病的，导致不适当的医疗。所以我 我建议开发标准化的定量方法，以提高我们解释基因组变异的能力 准确地以高通量进行。计算机模拟工具通常用于基于以下因素分配变异致病性： 保守，但其预测精度有限。目前的方法还没有经过校准， 基因，并且相同的致病性得分并不意味着不同的致病性的可能性相同。 基因.在这个提议中，1）我的目标是重新校准致病性分数，将基因特异性特征纳入其中 使致病性评分在基因间更具可比性，并提高致病性的准确性 使用先进的深度神经网络模型和饱和诱变的功能数据进行预测 问题研究2)我的目标是量化ACMG/AMP变异分类，并提供变异概率 使用先进的监督学习和利用大型案例对临床相关基因的致病性- 对照组。改进的计算预测（目标1）将细化下游的变体优先级 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（目标2）的变异致病性概率将改善沟通 实验室、卫生保健提供者和患者之间就基因检测结果进行沟通。

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.