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

这一行动为NSF国家植物基因组计划2019财年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。凯利·伊丽莎白·布朗的这项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》。该奖学金的主办机构是加州大学河滨分校，赞助科学家是丹尼尔·科尼希博士。大麦是世界上产量第四高的谷物，是从尼泊尔山区到北非低地等许多不同环境的主要作物。大麦产量预计将受到气候变化的巨大影响，这不仅可能导致世界各地的粮食短缺，还可能导致更多发达国家的保健品和麦芽等奢侈品价格上涨。该项目旨在通过使用1929年开始的正在进行的实验性进化研究的种子，更好地了解大麦如何在遗传水平上对环境变化做出反应。这位研究员将使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如何，因为它的基因构成。该项目的更广泛影响包括为来自不同背景的本科生提供基本研究培训，并在临时社区外联环境中让公众参与关于气候变化的科学讨论。培训目标包括获得大型基因组数据集的基因组学和生物信息学分析方面的专门知识，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理论意义和实际意义的问题。该项目将重点研究时间环境波动，以此作为大麦(大麦、禾本科)保持变异的机制。该项目将通过利用一组大麦复合杂交系(CCS)进行一系列平行进化实验来解决这个问题。始于1929年的这些实验和结果数据集提供了一个在各种环境条件下将表型与基因联系起来的独一无二的机会。利用现有的基因组数据集，这位研究员将绘制在不同气候下(例如，蒙大拿州和加利福尼亚州)保持异常高水平遗传多样性的基因，以及年度环境变化如何驱动遗传多样性的保持。该项目还将考虑非生物环境因素(如温度或降雨)和生物因素(如引起大麦烫伤的真菌病原体Rhynchosporium secalis的压力变化)的时间波动。该分析将利用CC种群的关键特征--复制，以确定在局部适应期间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馆(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，表型数据将存档在像Dryad这样的公共存储库中。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 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.