Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops

选择(遗传算法) 生物 植物育种 非生物胁迫 非生物成分 标记辅助选择 计算生物学 基因组选择 作物 分子育种 数量性状位点 生物技术 遗传学 农学 计算机科学 基因 生态学 机器学习 基因型 单核苷酸多态性
作者
Neeraj Budhlakoti,Amar Kant Kushwaha,Anil Rai,Krishna Kumar Chaturvedi,Anuj Kumar,Anjan Kumar Pradhan,Uttam Kumar,Rajeev Ranjan Kumar,Philomin Juliana,Dwijesh Chandra Mishra,Sundeep Kumar
出处
期刊:Frontiers in Genetics [Frontiers Media]
卷期号:13 被引量:119
标识
DOI:10.3389/fgene.2022.832153
摘要

Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.
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