Advancing evolutionary genetic inference in humans and other taxa

项目摘要/摘要 背景：进化基因组学的一个主要挑战是描述塑造当今世界的力量 遗传变异的模式。例如，自然选择影响基因的程度和方式 多样性仍然极具争议性。研究人员已经在很大程度上通过开发 对基因组序列变异的统计测试或总结，提供对进化力量的洞察 玩。然而，由于这些方法通常依赖于数据的单一单变量汇总，因此很有价值 原始数据集中存在的判别信息丢失。因此，一个更有成效的策略是使用 基因组数据的多维汇总(例如，汇总统计的大矢量)，甚至是 输入数据(例如，序列比对的矩阵表示)以做出更准确的推断。一个 更有效的方法是利用多次对相同总体进行采样的数据集 点，使人们能够观察行动中的进化动态。尽管这样的基因组时间序列数据 在越来越普遍的情况下，适当的计算方法的开发已经滞后 这类数据的激增。 建议：施莱德实验室寻求开发和应用强大的机器学习方法来进化 推论。我们在未来五年的工作将产生利用新表示法的强大软件工具 基因组数据集，包括时间序列数据。这些努力将显著提高研究人员的能力 从种群基因组和系统发育数据中做出准确的进化推断。的确， 初步结果表明，我们的方法在进化方面远远优于目前的方法。 遗传学。更重要的是，我们将使用这些工具来回答紧迫的进化问题。尤其是，我们的 使用时间序列数据将比使用时间序列数据更可信地揭示导致最近适应的基因座 目前是可能的。我们的努力将有助于解决关于适应在塑造过程中的作用的争议 整个人类基因组的多样性模式。这项研究对公共卫生具有重要意义。 此外，最近适应的基因也因疾病关联而变得丰富。此外，我们正在 构建埃及伊蚊和白纹伊蚊的时间序列数据集。我们会 询问这些数据以寻找最近和正在进行的适应的证据-这项工作将揭示 用于杀虫剂抗药性的演变和其他控制努力。令人鼓舞的初步数据也 表明我们在系统发育学方面的工作将大大提高这项重要研究的推论能力 区域。更广泛地说，本提案中描述的新方法的成功有可能 改变进化基因组数据分析的方法论格局。

Project Summary/Abstract Background: A major challenge in evolutionary genomics is to characterize the forces shaping present-day patterns of genetic variation. For instance, the extent and manner in which natural selection affects genetic diversity remains highly controversial. Researchers have largely addressed this problem by developing statistical tests or summaries of genome sequence variation that provide insights into the evolutionary forces at play. However, because such approaches typically rely on a single univariate summary of the data, valuable discriminatory information present in the original dataset is lost. A more fruitful strategy would thus be to use multidimensional summaries of genomic data (e.g. a large vector of summary statistics) or even the totality of the input data (e.g. a matrix-representation of a sequence alignment) to make more accurate inferences. An even more powerful approach is to utilize data sets in which the same population is sampled at multiple time points, allowing one to observe evolutionary dynamics in action. Although such genomic time-series data are becoming more prevalent, the development of appropriate computational methodologies has lagged behind the proliferation of such data. Proposal: The Schrider Lab seeks to develop and apply powerful machine learning methods for evolutionary inference. Our work over the next five years will yield powerful software tools leveraging novel representations of genomic datasets, including time-series data. These efforts will dramatically improve researchers' ability to make accurate evolutionary inferences from both population genomic and phylogenetic data. Indeed, preliminary results demonstrate that our methods vastly outperform current approaches in evolutionary genetics. More importantly, we will use these tools to answer pressing evolutionary questions. In particular, our use of time-series data will reveal loci responsible for recent adaptation with much greater confidence than currently possible. Our efforts will help to resolve the controversy over the role of adaptation in shaping patterns of diversity across the human genome. This research has important implications for public health as well, as genes underlying recent adaptations are enriched for disease-associations. Moreover, we are constructing a time-series dataset in the mosquito vector species Aedes aegypti and Aedes albopictus. We will interrogate these data for evidence of recent and ongoing adaptation—this work will reveal loci responsible for the evolution of resistance to insecticides and other control efforts. Encouraging preliminary data also suggest that our work in phylogenetics will substantially improve inferential power in this important research area. More broadly, the success of the novel approaches described in this proposal has the potential to transform the methodological landscape of evolutionary genomic data analysis.