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

项目摘要 细胞中空等位基因的分子表型的第一阶段(形态)联盟将产生一个目录 用体外细胞系统研究约1000个人类基因零等位基因的分子和细胞表型。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建模的 人的发展。基因敲除对复杂的人类表型的影响可以高度依赖。 在相应的细胞类型、细胞阶段和组织微环境上。因此，为了概括这些见解 从形态研究到活体环境，我们需要协调形态资源和分子/细胞 通过灵活和强大的计算框架，确定适当的细胞类型或组织的表型。我们的目标是 通过两种相辅相成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 命名为moDAG：多体有向无环图。MoDAG结合了来自Moric和其他公司的多组数据 估计基因调控网络的研究和最先进的统计方法。MoDAG模型单元 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来研究微环境。MoDAG可用于预测基因的效应 在体外细胞系统中敲除，从而帮助确定未来形态研究的目标基因的优先顺序 学习。其次，我们提出了一种基于生物信息的深度学习方法SDAN：Supervised Deep 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 识别与基因敲除相关的基因组。基因集提供了更健壮的基因表征 与单个基因相比，基因敲除更容易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 或者纸巾。最后，我们应用这两种方法来预测基因敲除和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。我们的计算框架架起了 Moric的资源，积累了各种人类细胞类型和组织的基因组数据，并提供有效的 为将来的研究产生新的见解或假设的解决方案。

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.