Linking genetics to cellular behavior and disease via multimodal data integration

通过多模式数据集成将遗传学与细胞行为和疾病联系起来

• 批准号: 10246113
• 负责人: Gerald Quon
• 金额: $ 143.15万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 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 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10246113
• 关键词: Affect; Behavior; Biological Assay; Cells; Development; Disease; Disease Progression; Electrophysiology (science); Gene Expression; Gene Expression Regulation; Gene Mutation; Genetic; Genetic Risk; Genetic Variation; Genotype; Goals; Human; Individual; Intervention; Link; Manuals; Measurement; Measures; Mediating; Mediation; Mental disorders; Modeling; Molecular; Morphology; Mutation; Neurodevelopmental Disorder; Neurons; Organ; Pattern; Phenotype; Regulator Genes; Risk; Tissues; base; cell behavior; cell type; computer framework; data integration; deep learning; disorder risk; genetic association; multimodal data; multimodality; response; therapeutic target

Project Summary / Abstract Studies of mechanisms underlying genetic associations with disease have primarily focused on gene regulatory mechanisms. In contrast, the impact of genetic variation on cellular phenotypes such as morphology and behavior is poorly understood, despite their importance in disease progression. This poor understanding is in part because measurement of some cellular phenotypes such as electrophysiological response patterns require skilled manual labor and is performed cell by cell, and is thus low throughput. Furthermore, some cellular phenotype assays require live cells, which for cell types such as neurons are prohibitively challenging to obtain from humans. The goal of this proposal is to develop deep learning-based frameworks for characterizing how molecular and cellular phenotypes covary using multimodal datasets, then to predict how these cellular behaviors mediate the effect of genetic variation on the risk of illnesses such as psychiatric and neurodevelopmental disorders. We will achieve this overall goal through the development of three frameworks. Multimodal models linking molecular and cellular phenotypes of cells. By linking gene regulation with cellular phenotypes such as neuron electrophysiology and morphology, we can then understand how changes at the molecular level propagate to cellular phenotypes and vice versa. Furthermore, we can use these models to impute cellular phenotypes when they cannot be measured experimentally. Identification of cellular phenotypes that mediate genetic risk of mental disorders. We will jointly model genotype, gene expression, cellular phenotypes and disease risk to generate mechanistic hypotheses about the mediation of genetic effects on disease risk through cellular phenotypes. Prediction of cellular phenotypes associated with disease progression. We will develop a prediction framework for exploring which cellular phenotypes change significantly with disease progression. By applying our imputation framework developed above, we will predict changes in neuron electrophysiological response and morphology associated with a range of psychiatric and neurodevelopmental disorders. We expect completion of this project to yield generalizable computational frameworks for linking genetics to molecular and cellular phenotypes for diverse cell types and organs.

项目总结/摘要 遗传与疾病相关机制的研究主要集中在基因 监管机制。相反，遗传变异对细胞表型的影响，如 尽管形态学和行为在疾病进展中很重要，但对其了解甚少。这个可怜 理解的部分原因是一些细胞表型的测量，如电生理学， 响应模式需要熟练的手工劳动，并且是逐个单元执行的，因此是低吞吐量的。 此外，一些细胞表型测定需要活细胞，其对于诸如神经元的细胞类型而言是可接受的。 从人类身上获得的挑战性非常大。 该提案的目标是开发基于深度学习的框架，以表征分子生物学是如何 和细胞表型协变使用多模态数据集，然后预测这些细胞行为如何 介导遗传变异对精神和神经发育等疾病风险的影响 紊乱我们将通过制定三个框架来实现这一总体目标。 连接细胞的分子和细胞表型的多模式模型。通过将基因调控与 细胞表型，如神经元电生理学和形态学，然后我们可以了解如何 分子水平的变化传播到细胞表型，反之亦然。此外，我们可以使用 当细胞表型不能通过实验测量时，这些模型可以对其进行估算。 鉴定介导精神障碍遗传风险的细胞表型。我们将共同 模型基因型，基因表达，细胞表型和疾病风险，以产生机制 关于通过细胞表型介导遗传对疾病风险的影响的假说。 与疾病进展相关的细胞表型预测。我们会预测 探索哪些细胞表型随疾病进展而显著变化的框架。通过 应用我们上面开发的估算框架，我们将预测神经元电生理学的变化， 反应和形态与一系列精神和神经发育障碍。 我们期望完成这个项目，以产生可推广的计算框架，连接遗传学 到不同细胞类型和器官的分子和细胞表型。