Learning about the evolution of structural variations from genomic and transcriptomic data

从基因组和转录组数据中了解结构变异的演变

基本信息

批准号：
10625833
负责人：
Raquel Assis
金额：
$ 37.36万
依托单位：
FLORIDA ATLANTIC UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2026-06-30
项目状态：
未结题

项目摘要

PROJECT SUMMARY Structural variations are key drivers of both evolutionary adaptation and human disease. My group develops and applies computational and statistical approaches for understanding the evolution of structural variations from patterns in their genomic and transcriptomic data. During the past few years, our studies have focused primarily on gene duplication, which represents the most common type of structural variation observed in nature. In particular, we investigated the origins of evolutionary innovation after gene duplication, a problem of long- standing interest in the evolutionary genomics community. To answer this question, we designed the first method for classifying evolutionary outcomes of duplicate genes from phylogenetic comparisons of their gene expression profiles. By applying this decision tree method to multi-tissue gene expression data, we were able to classify evolutionary outcomes of duplicate genes in Drosophila, mammals, and grasses. These studies revealed frequent tissue-specific expression divergence after duplication, as well as sequence and expression differences within and among taxa that are consistent with natural selection. In a follow-up population-genomic analysis, we demonstrated that natural selection indeed plays an important role in the evolutionary outcomes of young duplicate genes in Drosophila. Later, we developed analogous decision tree classifiers for two additional types of structural variations: gene deletion and translocation. Applications of our methods to sequence and expression data from multiple tissues and developmental stages in Drosophila uncovered rapid divergence concordant with adaptation, suggesting that natural selection shapes the evolutionary trajectories of structural variations generated by deletion and translocation as well. However, our recent analyses revealed that there are many limitations of these decision tree methods, including sensitivity to gene expression stochasticity, lack of statistical support, and inability to predict parameters driving the evolution of structural variations. Thus, during the next five years, my group will develop a suite of tailored model-based statistical and machine learning approaches for classifying the evolutionary outcomes and predicting the evolutionary parameters of structural variations arising from duplication, deletion, inversion, and translocation events. Our preliminary studies indicate that these techniques will be much more powerful and accurate than previous approaches, and will therefore compose major advancements in evolutionary investigations of structural variations. In addition to implementing our methods in open source software packages, we will apply them to assay the evolutionary implications of different types of structural variations in humans and several other animal and plant taxa. Comparisons will be made among different types of structural variations, their evolutionary outcomes, and taxonomic groups. The major goal of these studies will be to ascertain the general rules by which different types of structural variation contribute to evolutionary innovation. Together, these studies will shed light on how gene duplication, deletion, inversion, and translocation work in concert to generate a diversity of complex adaptations across the tree of life.

项目概要结构变异是进化适应和人类疾病的关键驱动因素。我的团队不断发展并应用计算和统计方法来理解结构变异的演变他们的基因组和转录组数据中的模式。在过去的几年里，我们的研究主要集中在基因复制，代表自然界中观察到的最常见的结构变异类型。在特别是，我们研究了基因复制后进化创新的起源，这是一个长期存在的问题。对进化基因组学界一直感兴趣。为了回答这个问题，我们设计了第一种方法用于通过基因表达的系统发育比较对重复基因的进化结果进行分类配置文件。通过将这种决策树方法应用于多组织基因表达数据，我们能够对果蝇、哺乳动物和草中重复基因的进化结果。这些研究揭示重复后频繁出现组织特异性表达分歧，以及序列和表达差异类群内部和类群之间与自然选择一致。在后续的群体基因组分析中，我们证明自然选择确实在年轻人的进化结果中发挥着重要作用果蝇中的重复基因。后来，我们为另外两种类型开发了类似的决策树分类器结构变异：基因缺失和易位。我们的方法在测序和表达中的应用来自果蝇多个组织和发育阶段的数据发现了与适应，表明自然选择塑造了结构变异的进化轨迹也由删除和易位产生。然而，我们最近的分析表明，有很多这些决策树方法的局限性，包括对基因表达随机性的敏感性、缺乏统计数据支持，并且无法预测驱动结构变化演变的参数。因此，在接下来的五年内，我的团队将开发一套基于模型的定制统计和机器学习方法对进化结果进行分类并预测结构变异的进化参数来自复制、删除、倒置和易位事件。我们的初步研究表明，这些技术将比以前的方法更加强大和准确，因此将组成结构变异的进化研究取得了重大进展。除了实施我们的开源软件包中的方法，我们将应用它们来分析不同的进化含义人类和其他几种动植物类群的结构变异类型。将会进行比较不同类型的结构变异、它们的进化结果和分类群之间的关系。主要这些研究的目标是确定不同类型的结构变异的一般规则为进化创新做出贡献。总之，这些研究将揭示基因复制、缺失、倒转和易位协同作用，在生命树上产生了多种复杂的适应。