Deep learning for spatial population genetics

项目摘要/摘要 了解空间遗传变异对于医学遗传学、了解人类 人口历史，确定样本的地理来源，以及疾病媒介的管理。然而， 虽然自然种群中的生物体分散在离其出生地有限的距离内，但大多数种群 遗传模型没有解释这种空间上的遗传隔离，而是将种群视为 由离散的德姆组成的。此外，扩散速度和人口密度往往在整个景观中有所不同 由于环境条件、人口结构的不同，或者仅仅是地理上的原因。这些建模 违规行为具有现实意义，例如，在纠正全基因组范围内隐藏的关联方面 人口结构(Berg等人，2019年；Sohail等人，2019年；Battey等人，2019年。2020a；Zaidi和Mathieson，2020)。在……里面 在这项建议中，我们的目标是开发一个基因组学工具包，用于通过以下方式推断空间种群遗传参数 深度学习的使用。 处理地理参考基因组数据的一种策略是训练深度神经网络(DNN)以 以自动方式识别数据中的有用信息。DNN可以根据模拟数据进行训练，这 不需要获取用于培训的经验数据。在本提案中，我们介绍了DNN的首次使用 空间种群遗传参数的推断。该提案有三个具体目标：1)我们将制定一项 使用DNN从地理参考DNA样本中估计空间变化的扩散速率的方法，2)我们 将修改我们的深度学习工具，以推断人口密度的附加人口参数 空间和3)最后，我们将应用我们的方法来推断两个重要的经验中的扩散速率和密度 可获得地理参照基因组数据的系统：冈比亚按蚊和人类。我们的 推断空间人口统计过程的方法将直接为经验应用提供信息，例如 全基因组关联研究和病媒控制，以及为其他空间研究奠定基础 人口遗传学调查。 博士后将接受有关尖端计算技术的严格培训 空间种群遗传学中的深度学习、统计和定量方法。赞助实验室 拥有充足的计算资源来支持拟议的研究。此外，加州大学 俄勒冈州有一个由NSF支持的机器学习中心，它将为 那个家伙。其他培训将包括编写赠款提案和第一作者手稿， 在会议上发表演讲，为本科生论文项目提供建议，并指导课堂教学。

Project Summary/Abstract Understanding spatial genetic variation is tremendously valuable for medical genetics, understanding human population history, identifying the geographic origin of samples, and management of disease vectors. However while organisms in natural populations disperse a limited distance from their birth location, most population genetic models do not account for such genetic isolation over space, and instead treat populations as composed of discrete demes. Moreover, dispersal rate and population density often vary across the landscape due to heterogeneous environmental conditions, population structure, or simply geography. These modeling violations have real world implications, for example, in correcting genome-wide association for hidden population structure (Berg et al., 2019; Sohail et al., 2019; Battey et al. 2020a; Zaidi and Mathieson, 2020). In this proposal we aim to develop a genomics toolset for inferring spatial population genetic parameters through the use of deep learning. One strategy for dealing with geo-referenced genomic data is to train a deep neural network (DNN) to identify useful information in the data in an automated fashion. DNNs can be trained on simulated data, which bypasses the need to obtain empirical data for training. In this proposal we present the first use of DNNs for inference of spatial population genetic parameters. The proposal has three Specific Aims: 1) we will develop a method that uses DNNs to estimate spatially varying dispersal rates from geo-referenced DNA samples, 2) we will modify our deep learning tool to infer the additional demographic parameter of population density across space and 3) lastly, we will apply our method to infer dispersal rate and density in two important empirical systems for which geo-referenced genomic data are available: Anopheles gambiae, and in humans. Our approach for inferring spatial demographic processes will directly inform empirical applications such as genome wide association studies and disease vector control, as well as lay the groundwork for other spatial population genetic inquiries. The postdoctoral fellow will receive rigorous training in cutting edge computational techniques relevant to deep learning and statistical and quantitative methods in spatial population genetics. The sponsoring labs have abundant computational resources to support the proposed research. In addition, the University of Oregon houses an NSF-supported center for machine learning which will provide incredible opportunities for the fellow. Additional training will include preparation of grant proposals and first author manuscripts, presenting at conferences, advising undergraduate thesis projects, and mentored teaching in-classroom.