Computational Population Genetics

项目摘要 过去15年来，基因组测序技术的革命创造了一个爆炸式的增长， 人口基因组数据，但在我们理解这种规模的数据的能力方面留下了差距。这是 部分原因是传统的种群遗传模型不能反映基因组和地理过程 产生了现在正在收集的各种各样的数据。为了利用这股信息洪流， 需要新的分析方法和模式。 近年来，我们的团队在使用监督机器学习进行人口统计方面取得了很大进展。 基因组分析（Schrider和克恩2018年部分综述）。特别是，我们开创了 开发和应用深度学习技术，用于各种任务，包括检测 选择（克恩和Schrider 2018，Xue等人，2021），定位基因组中的渗入区（Schrider et al. 2018），表征重组景观（Adrion等人，2020），预测地理起源 （Battey et al. 2020），以及可视化群体遗传数据（Battey et al. 2021）。我们的一个特别重点是 在上一个供资期间的努力一直是了解冈比亚按蚊的进化， 撒哈拉以南非洲正在开展病媒控制工作，因此我们一直在并将继续制定统计数据， 这些重要的数据都在考虑之中。 我们对按蚊的研究加强了我们研究空间变异的重要性，特别是在 适应的背景。在这个建议中，我们建立在我们在前几年中一直在发展的想法之上。 资助期间，并讨论我们正在进行的研究计划的三个方面。该提案有三个部分：1） 我们将继续进行空间群体遗传学方面的工作，并提出发展推断的方法。 扩散参数直接从人口基因组数据，以及提高我们的理解， 空间结构影响GWAS和相关技术的方式。2)制定方法， 描述适应的群体基因组学。在本节中，我们特别感兴趣的是开发 解释等位基因相对于其周围模式的地理分布的深度学习方法， 基因组变异来发现有益的等位基因。此外，我们还将开发发现选择的方法， 这是建立在我们推断人口规模家谱的能力的最新改进之上的（Kelleher等人，2019, Speidel等人，2019年）。最后，提出了社区资源项目开发的新途径 我们的团队一直在领导的stdpopsim项目，旨在提供一个开放源代码，高度 一种可重复的、可操作的方法，用于在一些常见的研究中进行群体遗传模拟 系统.我们概述了对选择下的基因组进行更逼真模拟的计划， 重点是实施先前公布的选定参数的估计。此外，我们将使用 stdpopsim库用于对人口统计和选择性推理中常用的方法进行基准测试

Project Summary The revolution in genome sequencing technologies over the past 15 years has created an explosion of population genomic data but has left in its wake a gap in our ability to make sense of data at this scale. This is in part because traditional population genetic models do not reflect the genomic and geographic processes producing the tremendously diverse data now being collected. To capitalize on this flood of information we need new methods and modes of analysis. In recent years our group has made great strides in using supervised machine learning for population genomic analysis (reviewed in part in Schrider and Kern 2018). In particular, we have pioneered the development and application of deep learning techniques for a wide variety of tasks including detecting selection (Kern and Schrider 2018, Xue et al., 2021), localizing introgression tracts in the genome (Schrider et al. 2018), characterizing the landscape of recombination (Adrion et al. 2020), predicting geographic origin (Battey et al. 2020), as well as visualizing population genetic data (Battey et al. 2021). A particular focus of our efforts during the last funding period has been understanding evolution of Anopheles gambiae in response to vector control efforts underway in sub-Saharan Africa, thus we have been and continue to develop statistical methods with these important data in mind. Our work on Anopheles has reinforced in us the importance of studying spatial variation, particular in the context of adaption. In this proposal we build upon ideas we have been developing during the previous funding period and discuss three facets of our ongoing research program. The proposal has three sections: 1) we will continue our work on spatial population genetics, and propose to develop methods for inferring dispersal parameters directly from population genomic data, as well as to improve our understanding of the ways in which spatial structure can impact GWAS and related techniques. 2) To develop methods to further characterize the population genomics of adaptation. In this section we are particularly interested in developing deep learning methods that account for the geographic spread of an allele relative to its surrounding pattern of genomic variation to discover beneficial alleles. In addition we will develop methods for discovering selection that build upon recent improvements in our ability to infer population-scale genealogies (Kelleher et al., 2019, Speidel et al., 2019). Finally, 3) we propose new avenues of development of a community resource project which our group has been leading, the stdpopsim project, which aims to provide an open source, highly reproducible and accessible method for doing population genetic simulation in a number of common study systems. We outline plans towards more realistic simulation of genomes under selection, with a particular focus on implementing previously published estimates of selective parameters. Moreover we will use the stdpopsim library to benchmark commonly used methods in demographic and selective inference