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Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data
Journal of Plant Physiology ( IF 4.3 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.jplph.2020.153354
Hao Tong , Zoran Nikoloski

Highly efficient and accurate selection of elite genotypes can lead to dramatic shortening of the breeding cycle in major crops relevant for sustaining present demands for food, feed, and fuel. In contrast to classical approaches that emphasize the need for resource-intensive phenotyping at all stages of artificial selection, genomic selection dramatically reduces the need for phenotyping. Genomic selection relies on advances in machine learning and the availability of genotyping data to predict agronomically relevant phenotypic traits. Here we provide a systematic review of machine learning approaches applied for genomic selection of single and multiple traits in major crops in the past decade. We emphasize the need to gather data on intermediate phenotypes, e.g. metabolite, protein, and gene expression levels, along with developments of modeling techniques that can lead to further improvements of genomic selection. In addition, we provide a critical view of factors that affect genomic selection, with attention to transferability of models between different environments. Finally, we highlight the future aspects of integrating high-throughput molecular phenotypic data from omics technologies with biological networks for crop improvement.

中文翻译:

作物改良的机器学习方法:利用表型和基因型大数据

高效和准确地选择优良基因型可以显着缩短与维持当前对食物、饲料和燃料的需求相关的主要作物的育种周期。与强调在人工选择的所有阶段都需要资源密集型表型的经典方法相比,基因组选择显着减少了对表型的需求。基因组选择依赖于机器学习的进步和基因分型数据的可用性来预测农艺相关的表型性状。在这里,我们系统回顾了过去十年中应用于主要作物单个和多个性状基因组选择的机器学习方法。我们强调需要收集中间表型的数据,例如代谢物、蛋白质和基因表达水平,随着建模技术的发展,可以进一步改进基因组选择。此外,我们对影响基因组选择的因素提供了批判性观点,并关注模型在不同环境之间的可转移性。最后,我们强调了将来自组学技术的高通量分子表型数据与用于作物改良的生物网络相结合的未来方面。
更新日期:2021-02-01
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