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

项目总结 结构变异是进化适应和人类疾病的关键驱动因素。我的团队开发和 应用计算和统计方法理解结构变化的演化 基因组和转录组数据中的模式。在过去几年里，我们的研究主要集中在 关于基因复制，这是自然界中观察到的最常见的结构变化类型。在……里面 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。 对进化基因组学社区的长期兴趣。为了回答这个问题，我们设计了第一种方法 用于根据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。通过将该决策树方法应用于多组织基因表达数据，我们能够对其进行分类 果蝇、哺乳动物和草本植物中重复基因的进化结果。这些研究揭示了 重复后频繁的组织特异性表达差异以及序列和表达差异 在符合自然选择的分类群内部和分类群之间。在后续的种群基因组分析中，我们 证明自然选择确实在年轻人的进化结果中起着重要的作用 果蝇体内的重复基因。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，快速分化与 适应，表明自然选择塑造了结构变化的进化轨迹 也是由删除和易位产生的。然而，我们最近的分析显示，有许多 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 支持，以及无法预测推动结构变化演变的参数。因此，在接下来的几年里 五年来，我的团队将开发一套量身定做的基于模型的统计和机器学习方法，用于 构造变异的演化结果分类及演化参数预测 来自复制、缺失、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强大和更准确，因此将组成 构造变异演化研究的重大进展。除了实施我们的 方法在开放源码软件包中，我们将应用它们来分析不同 人类和其他几个动植物分类群的结构变异类型。我们将进行比较 在不同类型的结构变异中，它们的进化结果和分类群。少校 这些研究的目标将是确定不同类型的结构变异所依据的一般规律 为进化创新作出贡献。总之，这些研究将阐明基因复制、缺失、 倒置和易位协同工作，在生命之树上产生了各种复杂的适应。

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.