NSF Postdoctoral Fellowship in Biology FY 2019: Fluctuating Selection in Barley Driven by Biotic and Abiotic Factors

这项行动为2019财年的NSF国家植物基因组计划生物学博士后研究奖学金提供资金。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。该奖学金的研究和培训计划的标题是“生物和非生物因素驱动的大麦波动选择”。该研究金的主办机构是加州大学滨江分校，赞助科学家是丹尼尔Koenig博士。大麦是世界上产量第四高的谷物，是从尼泊尔山区到北方低地地区许多不同环境中的主要作物。大麦产量预计将受到气候变化的巨大影响，这不仅可能导致全球粮食短缺，还可能导致较发达国家的保健品和麦芽等奢侈品成本上升。该项目旨在通过使用1929年开始的正在进行的实验进化研究中的种子，更好地了解大麦如何在遗传水平上对环境变化做出反应。这位研究员将利用历史天气数据来识别进化模式，这将帮助我们预测一种植物在特定环境中的表现，考虑到它的基因组成。该项目更广泛的影响包括为来自不同背景的本科生提供基础研究培训，并在休闲社区外联环境中让公众参与关于气候变化的科学讨论。培训目标包括获得基因组学和大型基因组数据集的生物信息学分析方面的专业知识，以及农业相关研究方面的培训。 如何在作物中保持遗传变异是一个具有重大知识和实践意义的问题。本计画将着重于时间环境波动作为维持大麦变异的机制。该项目将通过利用一系列大麦复合杂交系（CC）的平行进化实验来解决这个问题。这些实验始于1929年，所产生的数据集提供了一个独一无二的机会，可以在各种环境条件下将表型与基因型联系起来。利用现有的基因组数据集，研究员将绘制在不同气候下保持异常高水平遗传多样性的基因（例如，蒙大拿州与加州的差异），以及每年的环境变化如何驱动遗传多样性的保留。该项目还将考虑非生物环境因素（如温度或降雨量）和生物因素（如导致大麦枯萎的真菌病原体Rhynchosporium secalis的不同压力）的时间波动。该分析将利用CC群体的关键特征，复制，以确定在局部适应过程中等位基因频率变化的环境驱动因素。该项目的测序数据将通过国家生物技术信息短读档案中心（NCBI SRA）存档并公开提供。用于分析的Python或R代码将通过Github提供，表型数据将存档在Dryad等公共存储库中。该奖项反映了NSF的法定使命，并通过使用基金会的智力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 2019. 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 Keely Elizabeth Brown is "Fluctuating Selection in Barley Driven by Biotic and Abiotic Factors". The host institution for the fellowship is the University of California, Riverside and the sponsoring scientist is Dr. Daniel Koenig.Barley is the fourth most highly produced grain in the world and is a staple crop across many varied environments, from the mountains of Nepal to lowland regions in Northern Africa. Barley yield is expected to be dramatically impacted by climate change, potentially resulting not only in food shortages across the world, but in higher cost of luxury goods like health products and malt in more developed countries. This project aims to better understand how barley responds on a genetic level to changes in the environment by using seed from an ongoing experimental evolution study that began in 1929. The fellow will use historical weather data to identify patterns of evolution that will help us to predict how well a plant will do in a particular environment, given its genetic makeup. The broader impacts of the project include providing basic research training for undergraduates from diverse backgrounds and engaging the public in scientific discussions about climate change in casual community outreach settings. Training objectives include acquiring expertise in genomics and bioinformatic analysis of large genomic data sets and training in agriculturally-relevant research. How genetic variation is maintained in crop plants is a question of great intellectual and practical importance. This project will focus on temporal environmental fluctuations as a mechanism to maintain variation in barley (Hordeum vulgare, Poaceae). The project will address this question by leveraging a set of parallel evolution experiments using a collection of barley Composite Cross lines (CCs). Initiated in 1929, these experiments and the resulting datasets provides a one-of-a-kind opportunity to link phenotype to genotype under a wide variety of environmental conditions. Using existing genomic datasets, the Fellow will map genes that maintain unusually high levels of genetic diversity in different climates (e.g., that in Montana versus California), and also how yearly environmental variability drives the retention of genetic diversity. The project will also consider temporal fluctuations in both abiotic environmental factors like temperature or rainfall and biotic factors like varying pressure from the fungal pathogen Rhynchosporium secalis, which causes barley scald. The analysis will exploit the key feature of the CC populations, replication, to identify environmental drivers of allele frequency change during local adaptation. Sequencing data from this project will be archived and publicly available through the National Center for Biotechnology Information Short Read Archive (NCBI SRA). Python or R code used for analysis will be made available through Github, and phenotype data will be archived in a public repository like 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.