Statistical Approach to Uncovering Gene Networks Perturbed by Cis-acting and Trans-acting eQTLswith Active Learning

项目摘要/摘要 基因表达模式由顺式调控元件和反式作用元件上的复杂网络决定 各种因素。来自匹配个体的等位基因特异性fic表达和基因数据的rna-seq定量fi提供 有机会了解这个基因调控网络是如何由基因变异连接和修改fi的。到目前为止, 此类数据集的分析仅在单基因的基础上执行，忽略了 许多相互作用的基因，只有批量收集的数据，才会将每个样本视为同等有价值的，甚至 尽管来自每个样本的rna-seq和基因组序列数据仅在特定的fic环境中提供信息。 为了解决这些局限性，我们建议将等位基因与fic表达的数量性状基因座(Eqtl)相结合。 用遗传基因组学方法作图，通过将遗传变异视为自然的- 发生等位基因fic表达的扰动，并积极指导数据收集过程以科学地预测fi 捕捉数据中自然产生的最具信息量的扰动。我们提出的计算框架 要开发的是解决这个问题的第一个fi，并将包括1)表示的概率图形模型 以及学习受顺式和反式eQTL干扰的基因网络以及2)主动样本选择算法 用于评估为哪些样本收集额外的RNA-Seq或基因数据并更新当前网络 用新的样品做模型。我们将把我们的计算技术应用于模拟、小鼠交叉和 EQTLGen联盟数据，以重建受遗传变异干扰的基因网络，并比较 主动和批量学习策略的执行。特别是，我们将探索实现以下目标的可能性 计算生物学家和啮齿动物研究合作研究中的主动数据收集策略 老鼠遗传学家。这项拟议的研究将为生物医学研究人员提供通用的计算 解开致病基因调控机制和顺式/反式eQTL的框架 具有成本效益的数据收集策略。

PROJECT SUMMARY / ABSTRACT Gene expression pattern is determined by the complex network over cis-regulatory elements and trans-acting factors. RNA-seq quantification of allele-specific expression and genotype data from matched individuals provide opportunities to understand how this gene regulatory network is wired and modified by genetic variants. So far, analyses of such datasets have been performed only on a single-gene basis, ignoring the complex network over many interacting genes, and only with data collected in batch, treating each sample as equally valuable, even though RNA-seq and genome sequence data from each sample are informative only in specific circumstances. To address these limitations, we propose to combine allele-specific expression quantitative trait locus (eQTL) mapping with genetical genomics approach to reconstruct gene networks by treating genetic variants as naturally- occurring perturbations of allele-specific expression and to actively guide the data collection process to efficiently capture the most informative naturally occurring perturbations in data. The computational framework we propose to develop is the first to address this problem and will include 1) probabilistic graphical models for representing and learning gene networks perturbed by cis- and trans-acting eQTLs and 2) active sample selection algorithms for assessing for which samples to collect additional RNA-seq or genotype data and updating the current network model with new samples. We will apply our computational technique to simulated, mouse intercross, and the eQTLGen Consortium data to reconstruct gene networks perturbed by genetic variants and to compare the performance of active and batch learning strategies. In particular, we will explore the possibilities of implementing active data collection strategy in rodent studies in a collaborative research between a computational biologist and a mouse geneticist. The proposed research will provide biomedical researchers with a general computational framework for unraveling the gene regulatory mehanisms and cis-/trans-acting eQTLs that give rise to diseases with cost-effective data collection strategies.