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

本行动资助2023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。萨曼莎·斯诺德格拉斯奖学金的研究和培训计划的标题是“人类驯化玉米作为生物文化共同进化”。该奖学金的主办机构是加州大学戴维斯分校（UC Davis）。杰弗里·罗斯·伊巴拉和格雷厄姆·库普博士。植物驯化和农业是过去一万年人类文明的关键创新。驯化改变了植物造福人类的方式，这可能导致紧密依赖。例如，玉米离开人类种植就无法生存，但由于它是人类重要的食物来源，因此在全球范围内种植的环境差异极大。虽然考古学和基因研究已经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在早期农民社区中传播的，以及在早期和后期的过程中哪些特征是重要的。该项目利用群体遗传学方法来解决玉米和其他作物中这些长期存在的问题。答案可能会揭示一些物种难以驯化的原因，并将指导驯化新作物的努力。培训目标包括获得统计种群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公开的本科课程，重点是向生物学专业的学生介绍群体遗传学领域，以及大数据在测序和性状测量方面所带来的可能性。鉴于农民与驯化动物之间的密切关系，一些研究人员提出了驯化的理论模型，即生物文化共同进化。目前研究这类系统进化的方法通常集中在单个物种上。这个项目将通过把人类和玉米作为共同进化的物种来扩展这些方法。具体而言，该项目的目标包括：1)估算联合外合图，以更好地了解玉米如何在早期农民群体中移动，以及性状选择的时间和强度；2)估计200多个玉米性状的多基因选择特征，并将它们放置在特定的混合图中，以显示在考古记录中没有保留但对适应很重要的多基因性状（如开花时间）的选择模式；3)将研究结果和方法应用于其他美洲驯化品种的研究，以检验玉米驯化模式的普遍性。所有脚本都将在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，或可在NCBI和/或CyVerse上提供，而汇总的性状数据将在CyVerse和/或Dryad等数据平台上提供。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 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.