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

本行动资助NSF国家植物基因组计划2019财年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。这项奖学金的研究和培训计划的题目是“由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·柯尼格博士。大麦是世界上产量第四大的谷物，是许多不同环境的主要作物，从尼泊尔的山区到北非的低地地区。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，还可能导致较发达国家保健产品和麦芽等奢侈品价格上涨。该项目旨在通过使用始于1929年的一项正在进行的实验进化研究的种子，更好地了解大麦在遗传水平上对环境变化的反应。他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下的表现，考虑到它的基因构成。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，并在非正式的社区外展环境中让公众参与有关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门知识，以及农业相关研究方面的培训。农作物的遗传变异是如何保持的，这是一个具有重大智力和实际意义的问题。本项目将重点研究时间环境波动作为大麦（Hordeum vulgare, Poaceae）维持变异的机制。该项目将利用一组大麦复合杂交系（cc）的平行进化实验来解决这个问题。这些实验和由此产生的数据集始于1929年，为在各种环境条件下将表型与基因型联系起来提供了独一无二的机会。利用现有的基因组数据集，研究员将绘制在不同气候条件下（例如，蒙大拿州与加利福尼亚州）保持异常高水平遗传多样性的基因，以及每年的环境变化如何推动遗传多样性的保留。该项目还将考虑温度或降雨等非生物环境因素和生物因素（如引起大麦烫伤的真菌病原菌灰孢菌的压力变化）的时间波动。该分析将利用CC种群的关键特征——复制，来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。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 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.