Characterizing Allelic Effects and Leveraging Genomic Prediction to Advance Soybean Breeding

本行动资助2017财年美国国家科学基金会国家植物基因组计划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。该奖学金的主办机构是明尼苏达大学，赞助科学家是亚伦·洛伦兹博士。提高大豆产量和质量的努力受到每个成熟区内可利用的遗传多样性有限的挑战。然而，如果能够开发基因组方法和工具来利用以前通过成熟度差异分离的精英遗传变异，则可以实现这种多样性的增加。现有遗传多样性的扩大将使粮食生产得以增加，这是维持不断增长的世界人口所必需的。该项目的培训将使研究员具备统计基因组学和数量遗传学方面的专门知识。在本地，研究员将访问几所初中和高中，以促进学生对科学的参与。在全国范围内，该项目将推动基因组学辅助育种领域的发展，将开发工具和数据库，指导大豆遗传多样性的利用，并将有助于大豆基因组的基础知识。对大豆种质结构对籽粒产量和其他农艺性状的理解的发展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，以表征大豆性能的种质结构。然后将开发计算和信息工具，以方便育种者使用这些基因组信息。该项目将加强大豆基因组的基础研究，并通过使育种者能够利用成熟区外有价值的遗传变异，推进大豆的应用育种。生成的数据和计算工具将在SoyBase.org上公开。

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.