Improving Plant Breeding in Polyploid Crops Using Models for Demography and Selection

本行动资助NSF国家植物基因组计划2018年度生物学博士后研究奖学金。该奖学金支持奖学金获得者在主办实验室的研究和培训计划，该奖学金获得者还提出了扩大生物学参与的计划。Paul Blischak博士的研究和培训计划的标题是“利用人口统计学和选择模型改进多倍体作物的植物育种”。该奖学金的主办机构是亚利桑那大学，赞助科学家是dr。Michael Barker和Ryan Gutenkunst。驯化过程通过选择具有理想特征的个体来影响遗传多样性水平。因此，由于最初的种群瓶颈，发现哪些基因与这些重要的农业性状相关可能是困难的。许多农业物种也有额外的染色体组作为全基因组复制（多倍体）的结果，进一步复杂的遗传分析。本项目的重点是开发计算工具来识别多倍体作物品种的驯化相关基因。这些方法将应用于油菜（Brassica napus），这是一种经济上重要的多倍体作物，负责生产蔬菜和合成油、牲畜饲料和生物燃料。培训目标包括统计和计算方法的发展及其在多倍体作物育种方面的应用。更广泛的影响包括开发开源软件、教程和可供植物育种者和教育者使用的教学材料。用于分析多倍体作物驯化过程的全基因组DNA序列数据的工具普遍缺乏。本研究的主要目标是：(1)建立多倍体作物历史人口统计学分析的统计模型；(2)利用这些方法推断甘蓝型油菜的人口统计学历史；(3)利用这些人口统计学信息更好地识别驯化相关基因。这将通过在Python包DaDi （Diffusion Approximation for Demographic Inference）中实现新模型来实现，将这些模型应用于B. napus，并使用结果执行基因组规模的模拟，以识别正选择下的基因。该项目的所有数据、脚本和协议都将在公共存储库中提供，所有软件都将免费提供，并且是开源的。关键词：芸苔，油菜，人口学，驯化，多倍体，选择该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This action funds an NSF National Plant Genome Initiative Postdoctoral Research Fellowship in Biology for FY 2018. The fellowship supports a research and training plan in a host laboratory for the Fellow who also presents a plan to broaden participation in biology. The title of the research and training plan for this fellowship to Dr. Paul Blischak is "Improving Plant Breeding in Polyploid Crops Using Models for Demography and Selection." The host institution for the fellowship is the University of Arizona and the sponsoring scientists are Drs. Michael Barker and Ryan Gutenkunst.The process of domestication impacts levels of genetic diversity through the selection of individuals with desirable characteristics. Discovering what genes are associated with these agriculturally important traits can therefore be difficult as a result of this initial population bottleneck. Many agricultural species also have additional sets of chromosomes as a result of whole genome duplication (polyploidy), further complicating genetic analyses. This project focuses on the development of computational tools to identify domestication-related genes for crop improvement in polyploid species. These methods will be applied to canola (Brassica napus), an economically important polyploid crop responsible for the production of vegetable and synthetic oils, livestock feed, and biofuels. Training objectives include the development of statistical and computational methods and their application to the improvement of plant breeding efforts in polyploid crops. Broader impacts include the development of open-source software, tutorials, and teaching materials that can be used by plant breeders and educators.Tools for the analysis of genome-wide DNA sequence data that aim to understand the process of domestication in polyploid crops are generally lacking. The main goals of this research are (1) to develop statistical models for the analysis of historical demography in polyploid crops, (2) to use these methods to infer the demographic history of B. napus, and (3) to leverage this demographic information to better identify domestication-related genes. This will be achieved through the implementation of new models in the Python package DaDi (Diffusion Approximation for Demographic Inference), applying these models to B. napus, and using the results to perform genome-scale simulations for the identification of genes under positive selection. All data, scripts, and protocols for this project will be made available in public repositories, and all software will be freely available and open source. Keywords: Brassica, canola, demography, domestication, polyploidy, selectionThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.