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.

项目概要 细胞中无效等位基因的分子表型 (MorPhiC) 联盟的第一阶段将产生一个目录 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。 丰富的资源将使我们能够研究几个经常在早期建模的多细胞系统中的基因功能 基因敲除对复杂人类表型的影响可能高度依赖。 因此，概括了相应的细胞类型、细胞阶段和组织微环境。 从 MorPhiC 研究到体内环境，我们需要协调 MorPhiC 资源和分子/细胞 我们的目标是通过灵活而强大的计算框架来确定适当细胞类型或组织的表型。 首先，我们将通过两种互补的方法来实现这一目标。 命名为 moDAG：多组学有向无环图 MoDAG 结合了来自 MorPhiC 和其他的多组学数据。 研究和最先进的统计方法来估计 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，它还解释了来自组织的信号。 通过对一组信号蛋白进行建模，MoDAG 可用于预测基因的作用。 体外细胞系统中，从而有助于优先考虑未来敲除 MorPhiC 中的目标基因 其次，我们提出了一种基于生物学的深度学习方法，称为 SDAN：Supervised Deep。 SDAN 将基因敲除的分子表型与基因注释相结合。 识别与基因敲除相关的基因集 基因集提供更可靠的基因表征。 敲除比单个基因更普遍，因此更适用于不同的细胞类型或组织。 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 最后，我们应用这两种方法来预测基因敲除和组织的表型结果。 评估基因敲除和人类表型之间的关联。 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 为未来的研究产生新的见解或假设的解决方案。