New quantitative approaches to interpret variant pathogenicity

解释变异致病性的新定量方法

• 批准号: 10744328
• 负责人: Xiao Fan
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-17 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10744328
• 关键词: Alleles; American; Benign; Calibration; Cardiomyopathies; Categories; Classification; ClinVar; Clinical; Clinical Treatment; Communication; Computational Biology; Computing Methodologies; Confusion; DNA sequencing; Data; Data Set; Databases; Diagnostic; Disease; Family member; Foundations; Future; Gene Frequency; Genes; Genome; Genomic medicine; Genomics; Goals; Guidelines; Health Personnel; Human; Individual; Intuition; Knowledge; Label; Laboratories; Machine Learning; Measures; Medical; Medical Genetics; Methods; Modeling; Mutagenesis; Neural Network Simulation; Operative Surgical Procedures; Parents; Pathogenicity; Patient Recruitments; Patients; Pediatric Cardiomyopathy; Population; Positioning Attribute; Postdoctoral Fellow; Probability; Prognosis; Provider; Publishing; Registries; Research; Standardization; Techniques; Test Result; Time; Training; Variant; Veterans; accurate diagnosis; autism spectrum disorder; biobank; case control; clinical database; clinical implementation; clinically relevant; cohort; computer science; cost; deep neural network; exome; falls; genetic testing; genetic variant; genomic variation; human disease; implementation facilitation; improved; in silico; medical schools; molecular pathology; neural network; next generation; next generation sequencing; non-genetic; novel; programs; prophylactic; protein structure; public database; rare genetic disorder; reproductive; risk prediction; screening; supervised learning; tool; transfer learning; translational genomics; variant of unknown significance; web based interface

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.

项目摘要 知识和吞吐量不足以解释下一步确定的遗传变异的致病性 生成测序（NGS）是基因组医学实施的主要瓶颈。美国人 医学遗传学与基因组学院与分子病理协会（ACMG/AMP）指南 识别高信心的致病性和可能的​​致病性变异，但可伸缩性有限。许多变体 通过ACMG/AMP指南将其归类为具有不确定意义的变体 这些变体或多或少具有致病性，导致不适当的医疗。因此，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 准确在高通量处。塞里科工具通常用于基于 保护，但是它们的预测准确性是有限的。当前的方法尚未校准 基因和相同的致病性评分不会推断出不同的致病性可能性 基因。在此提案中，1）我的目标是重新校准结合基因特异性特征的致病性评分 使致病性得分在各个基因之间更具可比性，并提高致病性的准确性 使用先进的深神网络模型和饱和诱变的功能数据进行预测 研究。 2）我的目标是量化ACMG/AMP变体分类并提供变体的概率 使用高级监督学习和利用大病例的临床相关基因的致病性 控制队列。改进的计算预测（AIM 1）将完善下游的变体优先级 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（AIM 2）的变异致病性概率将改善沟通 在实验室，卫生保健提供者和患者之间有关基因检测结果。