PDF(Pubmed)
Abstract：
Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.
摘要：
标记辅助选择（MAS）在作物育种中起着至关重要的作用，通过快速可靠地识别和选择具有所需性状的植物来提高常规育种计划的速度和精度。然而,MAS的功效取决于几个先决条件，精确的表型是任何植物育种计划的关键方面。高通量远程表型的最新进展，由无人驾驶飞行器与机器学习相结合，提供一种非破坏性和有效的替代传统，耗时,和劳动密集型方法。此外，MAS依赖于标记-特征关联的知识，通常通过全基因组关联研究(GWAS)获得，为了理解复杂的特征，如耐旱性，包括产量成分和物候。然而,GWAS具有人工智能（AI）已被证明可以部分克服的局限性。此外,AI及其可解释的变体，确保透明度和可解释性，在整个育种过程中，越来越多地被用作公认的解决问题的工具。鉴于这些快速的技术进步，这篇综述概述了每个MAS的最新方法和流程，从表型，基因分型和关联分析，以将可解释的人工智能整合到整个工作流程中。在这种情况下，我们特别解决了育种冬小麦以获得更高的耐旱性和稳定产量的挑战和重要性，由于关键发育阶段的区域性干旱对冬小麦生产构成威胁。最后,我们探索从科学进步到实际实施的过渡，并讨论弥合前沿发展与育种者之间差距的方法，加快基于MAS的冬小麦抗旱育种。
