Linking Variants to Multi-scale Phenotypes via a Synthesis of Subnetwork Inference and Deep Learning

通过子网推理和深度学习的综合将变异与多尺度表型联系起来

• 批准号: 10627971
• 负责人: Mark W. Craven
• 金额: $ 65.56万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10627971
• 关键词: Active Learning; Address; Benchmarking; Biological; Catalogs; Clinical; Clinical assessments; Code; Communities; Creativeness; Data; Data Collection; Data Set; Diagnostic; Disease; Effectiveness; Elements; Gene Expression; Genes; Genetic; Genetic Variation; Genome; Genomics; Genotype; Health; Human; Individual; Knowledge; Learning; Link; Machine Learning; Methodology; Methods; Modeling; Molecular; Molecular Profiling; Organism; Outcome; Pathway interactions; Penetrance; Phenotype; Play; Prognosis; Rare Diseases; Research; Resolution; Role; Series; Technology; Testing; Training; Validation; Variant; Work; causal variant; data integration; deep learning; deep neural network; design; disease diagnosis; disease phenotype; diverse data; experimental study; gene function; gene product; genetic variant; genome wide association study; genomic variation; improved; insight; interest; learning algorithm; learning network; learning strategy; molecular scale; predictive modeling; rare variant; response

Project Summary The ability to accurately predict the effect of genetic variation on phenotypes at multiple scales would radically transform our ability to apply genomic technologies in order to understand human health and disease. This predictive ability would significantly improve the effectiveness of a broad spectrum of genomic analyses ranging from genome-wide association studies for common diseases to diagnostic odysseys searching for genetic causes of rare diseases. To address this challenge, we propose to develop a trainable approach for predicting the phenotypic impact of genetic variants. This approach will support predictions for a broad range of genetic variations, phenotypes, and biological contexts. It will incorporate and exploit mechanistic knowledge of pathways where available, but augment this pathway knowledge with learned models where it is not. This approach will consist of a synthesis of (i) methods that link genomic variants to their effect on expression or function of individual gene products, (ii) methods that link those relationships into the subnetworks involved in cellular responses of interest, (iii) machine-learning approaches that infer models pertaining to a variety of genotype-phenotype relations from large training sets. We will also develop and apply active learning algorithms to identify the most informative experiments for subsequent analysis by IGVF Consortium. Additionally, we will develop and apply a statistical framework for elucidating genetic modifiers, through probabilistic, network-informed inference of common variants identified in GWAS that modify the impact of rare variants implicated in sequencing-based association studies. Throughout the project, we will work closely with other IGVF Centers to guide experimental data collection, benchmark methods from across Centers, and contribute to the variant-element-phenotype catalog which will have broad applications by the community.

项目摘要 在多个尺度上准确预测遗传变异对表型的影响的能力将从根本上 改变我们应用基因组技术的能力，以了解人类健康和疾病。这 预测能力将显著提高广谱基因组分析的有效性 从常见疾病的全基因组关联研究到寻找 罕见疾病的遗传原因。 为了应对这一挑战，我们建议开发一种可训练的方法来预测表型的影响， 基因变异这种方法将支持预测广泛的遗传变异，表型， 和生物学背景。它将整合并利用可用的途径机械知识，但 在没有学习模型的地方，用学习模型来增强这种途径知识。这一办法将包括一项综合报告， （i）将基因组变异与其对单个基因产物的表达或功能的影响联系起来的方法，（ii） 将这些关系链接到感兴趣的细胞反应中涉及的子网络的方法，（iii） 机器学习方法，其推断与各种基因型-表型关系有关的模型， 大型训练集。 我们还将开发和应用主动学习算法，以确定最翔实的实验， IGVF Consortium的后续分析。此外，我们将开发和应用统计框架， 阐明遗传修饰剂，通过概率，网络信息推理的共同变异确定 在GWAS中，其修改了基于测序关联研究中涉及的罕见变体的影响。 在整个项目中，我们将与其他IGVF中心密切合作，指导实验数据收集， 来自各中心的基准方法，并有助于变异元件表型目录， 被社会广泛应用。