Zhang Qianqian, Zhang Qin, Jensen Just
Institute of Biotechnology, Beijing Academy of Agricultural and Forestry Sciences, Beijing, China.
College of Animal Science and Technology, Shandong Agricultural University, Taian, China.
Front Plant Sci. 2022 Jun 2;13:904230. doi: 10.3389/fpls.2022.904230. eCollection 2022.
To feed the fast growing global population with sufficient food using limited global resources, it is urgent to develop and utilize cutting-edge technologies and improve efficiency of agricultural production. In this review, we specifically introduce the concepts, theories, methods, applications and future implications of association studies and predicting unknown genetic value or future phenotypic events using genomics in the area of breeding in agriculture. Genome wide association studies can identify the quantitative genetic loci associated with phenotypes of importance in agriculture, while genomic prediction utilizes individual genetic value to rank selection candidates to improve the next generation of plants or animals. These technologies and methods have improved the efficiency of genetic improvement programs for agricultural production elite animal breeds and plant varieties. With the development of new data acquisition technologies, there will be more and more data collected from high-through-put technologies to assist agricultural breeding. It will be crucial to extract useful information among these large amounts of data and to face this challenge, more efficient algorithms need to be developed and utilized for analyzing these data. Such development will require knowledge from multiple disciplines of research.
为了利用有限的全球资源为快速增长的全球人口提供足够的食物，迫切需要开发和利用前沿技术并提高农业生产效率。在这篇综述中，我们特别介绍了农业育种领域中关联研究以及使用基因组学预测未知遗传价值或未来表型事件的概念、理论、方法、应用和未来意义。全基因组关联研究可以识别与农业中重要表型相关的数量遗传位点，而基因组预测则利用个体遗传价值对选择候选者进行排名，以改良下一代植物或动物。这些技术和方法提高了农业生产优良动物品种和植物品种的遗传改良计划的效率。随着新数据采集技术的发展，将有越来越多来自高通量技术的数据用于辅助农业育种。从这些大量数据中提取有用信息至关重要，为应对这一挑战，需要开发和利用更高效的算法来分析这些数据。这样的发展将需要多学科研究的知识。
