Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat.
Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat.
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DOI：
10.1111/tpj.14150
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发表时间：
2019-01
期刊：
影响因子：
--
通讯作者：
Uauy C
中科院分区：
文献类型：
--
作者：
Borrill P;Harrington SA;Uauy C
由于其庞大的多倍体基因组、有限的遗传多样性和田间表型限制，小麦性状的改良历来具有挑战性。然而，近年来，这些障碍中的许多已经降低。小麦基因组染色体水平组装的可用性现在促进了小麦遗传学的一步变化，并为资源提供了一个通用平台，包括变异数据，基因表达数据和遗传标记。测序突变群体和基因编辑技术的发展现在可以直接快速评估小麦中的基因功能。以有针对性的方式改变基因功能的能力将揭示同源冗余的影响，并允许发现这种多倍体基因组的隐藏潜力。识别和利用小麦野生亲缘种遗传多样性的新技术现在使小麦育种者能够利用这些额外的变异来源来应对粮食生产面临的挑战。最后，表型组学的进步已经解锁了在温室和田间对许多感兴趣的性状进行快速筛选的群体。展望未来，整合不同的数据类型，包括基因组、表观遗传和表型组学数据，将利用包括机器学习在内的大数据方法，以前所未有的细节了解小麦的性状生物学。本文旨在探讨基因组学和表型学的最新进展如何应用于多倍体小麦农艺性状的生物学研究。我们描述了该领域的新资源和技术发展，并讨论了如何将它们整合起来，以提高小麦基因的发现和验证。
Improving traits in wheat has historically been challenging due to its large and polyploid genome, limited genetic diversity and in‐field phenotyping constraints. However, within recent years many of these barriers have been lowered. The availability of a chromosome‐level assembly of the wheat genome now facilitates a step‐change in wheat genetics and provides a common platform for resources, including variation data, gene expression data and genetic markers. The development of sequenced mutant populations and gene‐editing techniques now enables the rapid assessment of gene function in wheat directly. The ability to alter gene function in a targeted manner will unmask the effects of homoeolog redundancy and allow the hidden potential of this polyploid genome to be discovered. New techniques to identify and exploit the genetic diversity within wheat wild relatives now enable wheat breeders to take advantage of these additional sources of variation to address challenges facing food production. Finally, advances in phenomics have unlocked rapid screening of populations for many traits of interest both in greenhouses and in the field. Looking forwards, integrating diverse data types, including genomic, epigenetic and phenomics data, will take advantage of big data approaches including machine learning to understand trait biology in wheat in unprecedented detail. The objective of this review was to discuss how the latest developments in genomics and phenomics can be applied to understand the biology of agronomic traits in polyploid wheat. We describe the new resources and technological developments in the field, and discuss how they can be integrated to improve gene discovery and validation in wheat.
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