NSF Postdoctoral Fellowship in Biology FY 2019: Trait Stability and Broad Adaptation over Forty Years of Selection in a Global Wheat Breeding Program

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。玛格丽特·R·克劳斯的这项研究和培训计划的标题是“全球小麦育种计划40年的选择中的性状稳定和广泛适应”。该奖学金的主办机构是国际玉米和小麦改良中心(CIMMYT)/堪萨斯州立大学，赞助科学家是马修·雷诺兹博士和杰西·波兰。在一系列环境压力下保持稳定产量的作物品种对农民来说很有价值，特别是那些资源贫乏、从作物损失中恢复能力有限的农民。了解不同环境条件下性状稳定性的遗传基础将促进广泛适应气候变化的作物品种的发展。然而，测量性状稳定性是昂贵和费力的，因为需要进行广泛的多环境试验来产生可靠的估计。自1979年以来，墨西哥国际玉米小麦改良中心(CIMMYT)每年都会将小麦育种材料发送给世界各地的合作者进行评估。这些试验提供了一个难得的机会来提高我们对以下问题的理解：1)小麦性状稳定性的遗传控制；2)小麦基因组经过40年的广泛适应选择是如何进化的。更广泛的影响包括为发展中国家的植物育种者开发数据分析培训模块。培训目标包括在堪萨斯州立大学获得基因组学和生物信息学方面的专业知识，以及在位于中国的河南农业大学CIMMYT研究中心获得天气数据集分析方面的专业知识。该项目将整合基因组信息、天气数据和CIMMYT国际试验40年来的表型性状记录，以了解表型稳定性的遗传结构，以及长期人工选择对小麦基因组的广泛适应的影响。该项目将从从挪威到新西兰的500多个地点收集的数据记录中，对产量和其他性状的稳定性进行强有力的估计，这些数据的海拔跨度超过3500米。该项目还将对CIMMYT的创立者品系和与国际试验地点相关的天气数据集进行全基因组重新测序。数据和方法将在同行评议的期刊文章、CIMMYT的研究数据和软件存储库网络(https://data.cimmyt.org/)，)上以及通过开源公共数据存储库(Dataverse.org)公开提供。这一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 Margaret R. Krause is "Trait Stability and Broad Adaptation over Forty Years of Selection in a Global Wheat Breeding Program". The host institutions for the fellowship are the International Maize and Wheat Improvement Center (CIMMYT)/Kansas State University, and the sponsoring scientists are Drs. Matthew Reynolds and Jesse Poland. Crop varieties that maintain consistent yields across a range of environmental stresses are of value to farmers, particularly those who are resource-poor with limited ability to recover from crop losses. Understanding the genetic basis of trait stability across a gradient of environmental conditions will enhance the development of broadly adapted, climate-resilient crop varieties. However, trait stability is expensive and laborious to measure due to the extensive multi-environment trials required to produce reliable estimates. Since 1979, the International Maize and Wheat Improvement Center (CIMMYT) in Mexico has sent wheat breeding material to collaborators around the world for evaluation each year. These trials provide a rare opportunity to improve our understanding of 1) the genetic controls underlying trait stability in wheat and 2) how the wheat genome has evolved over forty years of selection for broad adaptation. Broader impacts include developing data analytics training modules for plant breeders from developing countries. Training objectives includes acquiring expertise in genomics and bioinformatics at Kansas State University, and in the analysis of weather datasets at CIMMYT's research center at Henan Agricultural University in China. The project will integrate genomic information, weather data, and phenotypic trait records from forty years of CIMMYT's international trials to understand the genetic architecture of phenotypic stability and the effects of long-term artificial selection for broad adaptation on the wheat genome. The project will develop robust estimates of stability for yield and other traits from data records that were collected at more than 500 locations ranging from Norway to New Zealand and spanning more than 3,500 meters in elevation. The project will also generate whole genome re-sequencing of CIMMYT's founder lines and weather datasets linked to the international trial locations. Data and methodologies will be made publically available in peer reviewed journal articles, on CIMMYT's Research Data & Software Repository Network (https://data.cimmyt.org/), and through Dataverse, an open source public data repository (dataverse.org).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.