Accelerating crop genetic gains with genomic selection

生物 遗传增益 生物技术 选择(遗传算法) 粮食安全 牲畜 植物育种 遗传多样性 作物 农业 基因组选择 遗传学 遗传变异 农学 基因型 计算机科学 生态学 人口学 单核苷酸多态性 人工智能 社会学 基因 人口
作者
Kai P. Voss‐Fels,Mark Cooper,Ben J. Hayes
出处
期刊:Theoretical and Applied Genetics [Springer Nature]
卷期号:132 (3): 669-686 被引量:258
标识
DOI:10.1007/s00122-018-3270-8
摘要

Genomic prediction based on additive genetic effects can accelerate genetic gain. There are opportunities for further improvement by including non-additive effects that access untapped sources of genetic diversity. Several studies have reported a worrying gap between the projected global future demand for plant-based products and the current annual rates of production increase, indicating that enhancing the rate of genetic gain might be critical for future food security. Therefore, new breeding technologies and strategies are required to significantly boost genetic improvement of future crop cultivars. Genomic selection (GS) has delivered considerable genetic gain in animal breeding and is becoming an essential component of many modern plant breeding programmes as well. In this paper, we review the lessons learned from implementing GS in livestock and the impact of GS on crop breeding, and discuss important features for the success of GS under different breeding scenarios. We highlight major challenges associated with GS including rapid genotyping, phenotyping, genotype-by-environment interaction and non-additivity and give examples for opportunities to overcome these issues. Finally, the potential of combining GS with other modern technologies in order to maximise the rate of crop genetic improvement is discussed, including the potential of increasing prediction accuracy by integration of crop growth models in GS frameworks.
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