Characterizing Allelic Effects and Leveraging Genomic Prediction to Advance Soybean Breeding

表征等位基因效应并利用基因组预测推进大豆育种

• 批准号: 1710790
• 负责人: Benjamin Campbell
• 金额: $ 21.6万
• 依托单位: Campbell Benjamin W
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1710790&HistoricalAwards=false
• 关键词: Characterizing; Allelic; Effects; Leveraging; Genomic

This action funds an NSF National Plant Genome Initiative Postdoctoral Research Fellowship in Biology for FY 2017. 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 host institution for the fellowship is the University of Minnesota, and the sponsoring scientist is Dr. Aaron Lorenz.Efforts to increase soybean yield and quality are challenged by the limited genetic diversity available within each maturity zone. However, an increase in this diversity could be achieved if genomic methodologies and tools were developed to enable the utilization of elite genetic variation that has previously been isolated by maturity differences. The broadened available genetic diversity will enable an increase in food production, necessary to sustain the growing world population. The project training will equip the Fellow with expertise in statistical genomics and quantitative genetics. Locally, the Fellow will visit several middle and high schools to promote student involvement in the sciences. Nationally, this project will advance the field of genomics-assisted breeding, will develop tools and databases that will guide the utilization of soybean genetic diversity, and will contribute to basic knowledge of the soybean genome.The development of an understanding of soybean germplasm architecture for grain yield and other agronomic traits will enhance soybean breeding efforts. Yield trial data and genetic marker data from thousands of lines will be analyzed using GWAS and genomic prediction approaches to characterize the germplasm architecture of soybean performance. Computational and informatic tools will then be developed to facilitate the use of this genomic information by breeders. This project will enhance basic research on the soybean genome and will advance applied soybean breeding by enabling breeders to use valuable genetic variation from outside of their maturity zone. The generated data and computational tools will be made publically available at SoyBase.org.

这一行动为NSF国家植物基因组计划2017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。该奖学金的主办机构是明尼苏达大学，赞助科学家是亚伦·洛伦斯博士。提高大豆产量和质量的努力受到每个成熟区有限的遗传多样性的挑战。然而，如果开发基因组方法和工具，能够利用以前因成熟度差异而被隔离的精英遗传变异，这种多样性可能会增加。扩大的现有遗传多样性将使粮食产量增加，这是维持不断增长的世界人口所必需的。该项目培训将使该研究员具备统计基因组学和数量遗传学方面的专业知识。在当地，这位研究员将访问几所初中和高中，以促进学生参与科学。在全国范围内，该项目将推进基因组辅助育种领域，将开发指导大豆遗传多样性利用的工具和数据库，并将有助于了解大豆基因组的基本知识。了解大豆种质结构对粮食产量和其他农艺性状的影响将加强大豆育种工作。来自数千个品系的产量试验数据和遗传标记数据将使用GWAS和基因组预测方法进行分析，以表征大豆表现的种质结构。然后将开发计算和信息学工具，以促进育种者使用这些基因组信息。该项目将加强对大豆基因组的基础研究，并将通过使育种者能够利用其成熟区以外的有价值的遗传变异来推进应用大豆育种。生成的数据和计算工具将在SoyBase.org上公开提供。