Advancing evolutionary genetic inference in humans and other taxa

项目总结/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，自然选择影响遗传的程度和方式 多样性仍然是一个极具争议的问题。研究人员在很大程度上解决了这个问题， 基因组序列变异的统计测试或总结，提供了对进化力量的见解， 玩吧然而，由于这种方法通常依赖于数据的单个单变量汇总， 原始数据集中存在的区别信息丢失。因此，一个更有成效的战略是利用 基因组数据的多维摘要（例如，摘要统计的大向量）或甚至基因组数据的整体 输入数据（例如，序列比对的矩阵表示）以进行更准确的推断。一个 更有效的方法是利用在多个时间对同一总体进行抽样的数据集 点，允许一个观察进化动力学的行动。尽管这种基因组时间序列数据是 随着计算机技术的日益普及，相应的计算方法的发展已经落后了 这些数据的扩散。 建议：Schrider实验室寻求开发和应用强大的机器学习方法， 推论我们在未来五年的工作将产生强大的软件工具， 基因组数据集，包括时间序列数据。这些努力将大大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，我们的方法大大优于目前的方法在进化 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。特别是我们 使用时间序列数据将揭示基因座负责最近的适应性更大的信心比 目前可能。我们的努力将有助于解决关于适应在塑造 人类基因组的多样性模式。这项研究对公共卫生有重要意义 同时，作为最近适应的基础的基因也因疾病关联而丰富。而且我们 构建蚊子媒介物种埃及伊蚊和白纹伊蚊的时间序列数据集。我们将 询问这些数据，以寻找最近和正在进行的适应的证据-这项工作将揭示负责 对杀虫剂和其他控制措施的抗药性的演变。令人鼓舞的初步数据还 这表明，我们在遗传学方面的工作将大大提高这一重要研究的推理能力 区更广泛地说，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.