Advancing evolutionary genetic inference in humans and other taxa

推进人类和其他类群的进化遗传推断

• 批准号: 10388396
• 负责人: DANIEL R SCHRIDER
• 金额: $ 38.58万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10388396
• 关键词: Address; Aedes; Affect; Area; Computing Methodologies; Culicidae; Data; Data Analyses; Data Set; Development; Disease; Evolution; Genes; Genetic; Genetic Variation; Genome; Genomics; Health; Human; Human Genome; Insecticides; Machine Learning; Methodology; Methods; Natural Selections; Pattern; Phylogenetic Analysis; Play; Population; Public Health; Research; Research Personnel; Resistance; Role; Sampling; Sequence Alignment; Series; Shapes; Software Tools; Testing; Time; Variant; Work; base; genomic data; improved; insight; machine learning method; novel; novel strategies; statistics; success; time use; tool; vector; vector mosquito

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.

项目概要/摘要 背景：进化基因组学的一个主要挑战是描述塑造当今社会的力量的特征 遗传变异的模式。例如，自然选择影响遗传的程度和方式 多样性仍然备受争议。研究人员主要通过开发解决这个问题 基因组序列变异的统计测试或总结，提供对进化力量的见解 玩。然而，由于此类方法通常依赖于数据的单个单变量摘要，因此有价值 原始数据集中存在的歧视性信息会丢失。因此，更有效的策略是使用 基因组数据的多维摘要（例如，摘要统计的大向量），甚至是全部 输入数据（例如序列比对的矩阵表示）以做出更准确的推论。一个 更强大的方法是利用多次对同一群体进行采样的数据集 点，允许人们观察行动中的进化动态。尽管此类基因组时间序列数据 变得越来越普遍，适当的计算方法的发展已经落后 此类数据的扩散。 提案：施莱德实验室致力于开发和应用强大的机器学习方法来实现进化 推理。我们未来五年的工作将产生利用新颖表征的强大软件工具 基因组数据集，包括时间序列数据。这些努力将极大地提高研究人员的能力 从群体基因组和系统发育数据中做出准确的进化推论。的确， 初步结果表明，我们的方法在进化方面远远优于当前的方法 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。特别是，我们的 使用时间序列数据将比其他数据更有信心地揭示负责近期适应的位点 目前可能。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 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，最近适应的基因也丰富了与疾病的关联。此外，我们是 构建蚊媒物种埃及伊蚊和白纹伊蚊的时间序列数据集。我们将 询问这些数据以获取最近和正在进行的适应的证据——这项工作将揭示负责的位点 用于杀虫剂抗性的进化和其他控制工作。初步数据也令人鼓舞 表明我们在系统发生学方面的工作将大大提高这项重要研究的推理能力 区域。更广泛地说，本提案中描述的新颖方法的成功有可能 改变进化基因组数据分析的方法论格局。