NSF Postdoctoral Fellowship in Biology: Interplay of Ploidy and the Distribution of Fitness Effects in Brassica

本行动资助2022财年美国国家科学基金会植物基因组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。Justin Conover博士的研究和培训计划的标题是“油菜倍性的相互作用和适合度效应的分布”。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。驯化野生植物以培育农作物导致其基因组发生了许多变化。了解这些变化可以加深我们对农业历史的理解，并可能指导未来的作物改良。DNA突变可以影响个体的进化适应性（生存和繁殖的能力），其后果从致命到没有影响，到非常有益。然而，我们对作物突变适应度效应的理解并不完整，这主要是因为许多作物有两套以上的完整染色体，这使得基因组分析变得复杂，并可能影响突变如何改变适应度。该项目将开发新的方法来了解植物突变的适应度效应，并将其应用于多种经济上重要的芸苔属作物，包括油菜、西兰花、花椰菜和芜菁。开发的工具和获得的见解将适用于许多其他作物，包括棉花、小麦、花生和藜麦。该项目的广泛影响将通过付费研究机会和免费公共研讨会，增加来自历史上代表性不足群体的学生在STEM领域的参与度，包括阿拉斯加原住民和美国原住民，以及LGBTQ+社区成员，以准备研究生院和外部奖学金的申请材料。培训目标包括获得将计算和统计方法应用于人口水平的基因组数据和基因表达数据分析方面的专门知识。新突变的适应度效应分布（DFE）是群体遗传学中的一个基本概念，对于理解和预测自然选择如何在群体中起作用具有重要意义。人们对动物模型系统中不同种群间DFE特性的差异给予了相当大的关注，但对植物DFE的研究却相对被忽视。尽管许多作物系统都经历了最近和反复发生的全基因组重复（多倍体）事件，通常伴随着种间杂交，但人们对多倍体水平的变化影响植物DFE的方式知之甚少。本项目利用异源多倍体甘蓝型油菜及其二倍体祖先油菜和甘蓝来表征古代和现代多倍体事件如何影响种群的DFE，并建立单个异源多倍体种群亚基因组之间DFE的相关性模型。这项工作将主要通过使用和开发Python包dadi（人口统计推断的扩散近似）和公开可用的转录组和全基因组重测序数据来实现。所有的数据、脚本和先进的方法都将在公共存储库中提供，所有的软件都将是开源的，免费提供。该奖项反映了美国国家科学基金会的法定使命，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 Plant Genome Postdoctoral Research Fellowship in Biology for FY 2022. 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. Justin Conover is “Interplay of Ploidy and the Distribution of Fitness Effects in Brassica”. The host institution for the fellowship is the University of Arizona and the sponsoring scientists are Drs. Michael Barker and Ryan Gutenkunst.The domestication of wild plants to create agricultural crops resulted in many changes in their genomes. Understanding those changes can deepen our understanding of agricultural history and potentially guide future crop improvements. DNA mutations can affect an individual’s evolutionary fitness (ability to survive and reproduce), with consequences ranging from lethality, to having no effect, to being highly beneficial. However, our understanding of mutation fitness effects in crops is incomplete, largely because many crops have more than two complete sets of chromosomes, which complicates genomic analyses and may affect how mutations change fitness. This project will develop new approaches to understand the fitness effects of mutations in plants and apply them to multiple economically important Brassica crops, including canola, broccoli, cauliflower, and turnips. The tools developed and insights gained will be applicable to many other crops including cotton, wheat, peanuts, and quinoa. Broader impacts from this project will increase the participation of students from historically underrepresented groups in STEM, including Alaskan Natives and Native Americans, as well as members of the LGBTQ+ community, through paid research opportunities and free public workshops to prepare application materials for graduate schools and external fellowships. Training objectives include obtaining expertise in the application of computational and statistical methods to population-level genomic data and the analysis of gene expression data.The distribution of fitness effects (DFE) of new mutations is a fundamental concept in population genetics that is important for understanding and predicting how natural selection operates in populations. Considerable attention has been given to understand how properties of the DFE can differ between populations in animal model systems, but DFE research has been relatively neglected in plants. Although many crop systems have undergone recent and recurrent whole genome duplication (polyploidy) events, often accompanied by interspecific hybridization, the ways in which shifts in ploidy levels affect the DFE in plants are poorly understood. This project uses allopolyploid Brassica napus, and its diploid progenitors B. rapa and B. oleracea to characterize how ancient and recent polyploidy events influences the DFE of a population and to model correlations in the DFE between the subgenomes of a single allopolyploid population. The work will be achieved primarily through the use and development of the Python package dadi (Diffusion Approximation for Demographic Inference) and publicly available transcriptomic and whole-genome resequencing data. All data, scripts, and methodological advances will be made available in public repositories, and all software will be open-source and freely available.This 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.