Linking genetics to cellular behavior and disease via multimodal data integration

项目摘要/摘要 基因与疾病相关机制的研究主要集中在基因上。 监管机制。相比之下，遗传变异对细胞表型的影响，如 尽管形态和行为在疾病进展中很重要，但人们对它们知之甚少。这个可怜的人 理解的部分原因是对某些细胞表型的测量，如电生理 响应模式需要熟练的体力劳动，并且是逐个细胞执行的，因此吞吐量很低。 此外，一些细胞表型分析需要活细胞，对于神经元等细胞类型来说，活细胞 令人望而却步，难以从人类身上获得。 这项提案的目标是开发基于深度学习的框架，用于表征分子 和细胞表型的共同变化使用多模式数据集，然后预测这些细胞行为 调节基因变异对精神疾病和神经发育等疾病风险的影响 精神错乱。我们将通过开发三个框架来实现这一总体目标。 将细胞的分子和细胞表型联系起来的多模式模型。通过将基因调控与 细胞表型，如神经元电生理学和形态，然后我们就可以理解 分子水平上的变化传播到细胞表型，反之亦然。此外，我们可以使用 当细胞表型无法通过实验测量时，这些模型会将其归因于细胞表型。 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产生机制 关于遗传效应通过细胞表型对疾病风险的中介作用的假设。 与疾病进展相关的细胞表型的预测。我们将制定一项预测 探索哪些细胞表型随着疾病进展而显著变化的框架。通过 应用上面开发的归因框架，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有关。 我们期望这个项目的完成将产生将遗传学联系起来的可推广的计算框架 不同细胞类型和器官的分子和细胞表型。

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.