NSF Postdoctoral Fellowship in Biology: Human Domestication of Maize as Bio-cultural Coevolution

这一行动为2023财年NSF植物基因组生物学博士后研究奖学金提供了资金。该奖学金支持在东道主实验室为该研究员制定的研究和培训计划，该研究员还提出了扩大生物学参与度的计划。萨曼莎·斯诺德格拉斯的这项研究和培训计划的标题是“人类驯化玉米，作为生物文化的共同进化”。该奖学金的主办机构是加州大学戴维斯分校，赞助科学家是Jeffrey Ross-Ibarra博士和Graham Coop.博士。植物驯化和农业是过去10,000年人类文明的关键创新。驯化改变了植物造福人类的方式，这可能导致紧密的依赖。例如，玉米不能离开人类的种植而存活，但由于它是人类的重要食物来源，所以在全球范围内种植的环境截然不同。虽然考古学和基因研究揭示了玉米驯化发生的时间和地点，但尚不清楚玉米是如何与早期农民群体一起和在早期农民社区之间转移的，以及哪些特征在这个过程的早期和后期是重要的。该项目利用群体遗传学创造方法来解决玉米和其他作物中的这些长期存在的问题。答案可能会揭示为什么有些物种很难驯化，并将指导驯化新作物的努力。培训目标包括获得统计人口遗传学方面的专业知识，管理大型跨学科团队的专业发展，以及与加州大学戴维斯分校的进化生物学家、人类学家和中美洲食品学者建立专业联系，以促进未来的合作。更广泛的影响包括开发可公开提供的本科课程，重点是向生物学专业的学生介绍种群遗传学领域，以及大数据在测序和特征测量工作中打开的可能性。鉴于农民和家养动物之间的密切关系，一些研究人员提出了驯化为生物文化共同进化的理论模型。目前研究这类系统进化的方法通常集中在单一物种上。该项目将通过将人类和玉米视为共同进化的物种来扩展这些方法。具体地说，项目目标包括1)估计联合混合曲线图，以更好地了解玉米如何随着早期农民群体以及在早期农民群体之间移动，以及对性状进行选择的时间和强度；2)估计玉米200多个性状的多基因选择特征，并将它们放置在混合曲线图中的特定时间段，以显示考古记录中没有保留但对适应性很重要的多基因性状的选择模式，如开花时间；以及3)将结果和方法应用于其他美国家养动物的研究，以测试玉米驯化模式的普遍性。所有脚本将在GitHub上公开提供，并与出版物相关联。所有基因组数据将来自NCBI和/或CyVerse，或将在NCBI和/或CyVerse上提供，聚合的特征数据将在诸如CyVerse和/或Dryad的数据平台上获得。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势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 2023. 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 Samantha Snodgrass is "Human Domestication of Maize as Bio-cultural Coevolution". The host institution for the fellowship is the University of California (UC) Davis and the sponsoring scientists are Drs. Jeffrey Ross-Ibarra and Dr. Graham Coop.Plant domestication and agriculture are key innovations underlying the past 10,000 years of human civilization. Domestication alters how plants benefit humans, which can result in tight dependencies. For example, maize cannot survive outside of human cultivation, yet is planted across the globe in extremely different environments because it is a vital food source for humans. While archaeology and genetic research have revealed much about when and where domestication of maize occurred, it is unclear how maize was moved with and among early farmer communities and which traits were important early versus later in the process. This project uses population genetics to create methods addressing these long-standing questions in maize and other crops. Answers may shed light on why some species are hard to domesticate and will guide efforts to domesticate new crops. Training objectives include gaining expertise in statistical population genetics, professional development for managing large, interdisciplinary teams and establishing professional ties with evolutionary biologists, anthropologists, and Mesoamerican foodway scholars at UC Davis to facilitate future collaboration. Broader impacts include developing publicly available undergraduate curricula focused on introducing biology students to the field of population genetics and the possibilities opened by big data in both sequencing and trait-measuring efforts. Given the close relationship between farmers and domesticates, several researchers have proposed theoretical models of domestication as bio-cultural coevolution. Current methods to investigate evolution in such systems typically focus on a single species. This project will expand upon those approaches by treating humans and maize as coevolving species. Specifically, project objectives include 1) estimating joint admixture graphs to better understand how maize moved with and among early farmer communities and the timing and strength of selection on traits; 2) estimating signatures of polygenic selection across 200+ traits scored in maize and placing them at specific time periods within the admixture graph to show selection patterns on polygenic traits not retained in the archeological record but important to adaptation such as flowering time; and, 3) applying the results and methods to the study of other American domesticates to test the generality of domestication patterns found in maize. All scripts will be publicly available on GitHub and associated with a publication. All genomic data will come from or be made available on NCBI and/or CyVerse and aggregated trait data will be available on a data platform such as CyVerse and/or Dryad.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.