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

这项行动资助了2018财年的NSF国家植物基因组计划生物学博士后研究奖学金。该研究金支持研究员在东道实验室的研究和培训计划，研究员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“使用人口统计和选择模型改进多倍体作物的植物育种”。“该研究金的主办机构是亚利桑那大学，赞助科学家是Michael Barker博士和Ryan Gutenkunst博士。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的水平。因此，由于这种初始种群瓶颈，发现与这些农业上重要的性状相关的基因可能很困难。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，进一步复杂化遗传分析。该项目的重点是开发计算工具，以识别多倍体物种中与作物改良相关的驯化基因。这些方法将应用于油菜（甘蓝型油菜），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。培训目标包括发展统计和计算方法，并将其应用于提高多倍体作物的植物育种工作。更广泛的影响包括开发可供植物育种者和教育者使用的开源软件、教程和教学材料。普遍缺乏旨在了解多倍体作物驯化过程的全基因组DNA序列数据分析工具。本研究的主要目的是（1）建立多倍体作物历史人口统计分析的统计模型，（2）利用这些方法推断B的人口统计历史。油菜，和（3）利用这些人口统计信息，以更好地确定驯化相关的基因。这将通过在Python包DaDi（人口统计推断的扩散近似）中实现新模型来实现，并将这些模型应用于B。napus，并使用结果进行基因组规模的模拟，以确定积极选择下的基因。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和开源。保留字：芸苔属，油菜，人口统计学，驯化，多倍体，选择这个奖项反映了NSF的法定使命，并已被认为是值得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的支持。

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.