Statistical Methods for Inferring Gene-Phenotype Associations Using Omic Data from Gene Knockout and Human Phenotype Studies

使用基因敲除和人类表型研究的组学数据推断基因表型关联的统计方法

• 批准号: 10733165
• 负责人: Li Hsu
• 金额: $ 55.57万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-22 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10733165
• 关键词: Address; Alleles; Area Under Curve; Biological; Catalogs; Cell model; Cells; Chromatin; Classification; Collaborations; Communication; Communities; Community Outreach; Complex; Computer software; Computing Methodologies; Data; Data Analyses; Disease; Drug Targeting; Educational workshop; Epigenetic Process; Feedback; Future; Gene Combinations; Gene Expression; Genes; Goals; Graph; Harvest; Human; Human Biology; Human Development; In Vitro; Individual; Intervention; Knock-out; Learning; Medicine; Methods; Modeling; Molecular; Multiomic Data; Names; Noise; Outcome; Output; Phase; Phenotype; Production; Research Design; Research Project Grants; Resources; Signal Transduction; Signaling Molecule; Signaling Protein; Statistical Methods; Structure; System; Tissues; Validation; Variant; Work; causal variant; cell type; computer framework; data resource; deep learning; design; effectiveness measure; experience; flexibility; gene function; gene interaction; gene network; gene regulatory network; genome-wide; improved; in vivo; insight; knockout gene; learning strategy; member; molecular phenotype; multiple omics; novel strategies; open source; protein protein interaction; research study; webinar

Project Summary The phase 1 of the Molecular Phenotypes of Null Alleles in Cells (MorPhiC) consortium will produce a catalog of molecular and cellular phenotypes for null alleles of ~1000 human genes using in vitro cellular systems. These rich resources will allow us to study the gene functions in several multicellular systems that often model early human development. The impact of a gene knockout on complex human phenotypes can be highly dependent on the corresponding cell type, cell stage, and tissue microenvironment. Therefore, to generalize the insights from MorPhiC studies to in vivo settings, we need to harmonize MorPhiC resources and the molecular/cellular phenotypes of appropriate cell types or tissues, by a flexible and robust computational framework. We aim to achieve this goal by two complementary approaches. First, we will develop a dynamic gene regulatory network named moDAG: multi-omic Directed Acyclic Graph. MoDAG combines multi-omic data from MorPhiC and other studies and the state-of-the-art statistical methods to estimate a gene-regulatory network. MoDAG models cell types characterized by genome-wide epigenetic or gene expression data. It also accounts for signals from tissue microenvironment by modeling a set of signaling proteins. MoDAG can be used to predict the effect of gene knock out in the in vitro cellular systems, and thus help prioritize the genes to be targeted in future MorPhiC studies. Second, we propose a biologically informed deep learning method named as SDAN: Supervised Deep learning with gene Annotation. SDAN combines molecular phenotype of gene knockout with gene annotation to identify gene sets associated with gene knock out. Gene sets provide more robust characterization of gene knockout than individual genes and thus are more generalizable to different cell types or tissues. The gene annotation used by SDAN is gene-gene interaction network that can be modified according to relevant cell types or tissues. Finally, we apply these two methods to predict the phenotypic outcomes of gene knockouts and assess the association between gene knockouts and human phenotypes. Our computational framework bridges MorPhiC’s resource with accumulating omic data in various human cell types and tissues and provide effective solutions to generate new insights or hypothesis for future studies.

项目摘要 第一阶段的分子表型的等位基因在细胞（MorPhiC）财团将产生一个目录， 使用体外细胞系统对约1000个人类基因的无效等位基因进行分子和细胞表型分析。这些 丰富的资源将使我们能够研究几个多细胞系统中的基因功能，这些系统通常是早期模型 人类发展基因敲除对复杂人类表型的影响可能高度依赖于 对相应的细胞类型、细胞阶段和组织微环境的影响。因此，为了概括这些见解， 从MorPhiC研究到体内环境，我们需要协调MorPhiC资源和分子/细胞 表型的适当的细胞类型或组织，通过灵活和强大的计算框架。我们的目标是 通过两种互补的方法实现这一目标。首先，我们将建立一个动态的基因调控网络 moDAG：multi-omic Directed Acyclic Graph。MoDAG结合了来自MorPhiC和其他 研究和最先进的统计方法来估计基因调控网络。MoDAG模型细胞 以全基因组表观遗传或基因表达数据为特征的类型。它也解释了来自组织的信号 通过模拟一组信号蛋白来模拟微环境。MoDAG可用于预测基因的作用 在体外细胞系统中敲除，从而有助于优先考虑未来MorPhiC中靶向的基因 问题研究其次，我们提出了一种生物信息深度学习方法SDAN：Supervised Deep 使用基因注释学习。SDAN将基因敲除的分子表型与基因注释相结合， 鉴定与基因敲除相关基因组。基因集提供了更强大的基因表征 基因敲除比单个基因敲除更有效，因此更适用于不同的细胞类型或组织。基因 SDAN使用的注释是基因-基因相互作用网络，可以根据相关细胞类型进行修改 或纸巾。最后，我们应用这两种方法来预测基因敲除的表型结果， 评估基因敲除与人类表型之间的关联。我们的计算框架 MorPhiC的资源，积累了各种人类细胞类型和组织的组学数据，并提供有效的 解决方案，为未来的研究产生新的见解或假设。