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

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以了解人类健康和疾病的能力。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诊断奥德赛搜索 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培训的方法来预测 基因变异。这种方法将支持对广泛的遗传变异、表型、 以及生物学背景。它将在可能的情况下整合和利用路径的机械知识，但 在没有学习到的地方，用学习过的模型来增强这条路径的知识。这一方法将由一项综合 (I)将基因组变异与其对单个基因产品的表达或功能的影响联系起来的方法，(Ii) 将这些关系链接到与感兴趣的细胞反应有关的子网络的方法，(Iii) 机器学习方法，推断与各种基因型-表型关系有关的模型 庞大的训练集。 我们还将开发和应用主动学习算法来确定最有信息量的实验 IGVF财团随后进行的分析。此外，我们将开发和应用一个统计框架，用于 通过识别的常见变异的概率、网络信息推断来阐明遗传修饰 这改变了基于测序的关联研究中涉及的稀有变异的影响。 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， 对各个中心的方法进行基准测试，并为变量元素表型目录做出贡献，这将 受到社会各界的广泛应用。

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.