de Dorlodot Sophie, Forster Brian, Pagès Loïc, Price Adam, Tuberosa Roberto, Draye Xavier
Unité d'Ecophysiologie et d'Amélioration végétale, Université catholique de Louvain, Croix du Sud 2-11, B-1348 Louvain la Neuve, Belgium.
Trends Plant Sci. 2007 Oct;12(10):474-81. doi: 10.1016/j.tplants.2007.08.012. Epub 2007 Sep 5.
Abiotic stresses increasingly curtail crop yield as a result of global climate change and scarcity of water and nutrients. One way to minimize the negative impact of these factors on yield is to manipulate root system architecture (RSA) towards a distribution of roots in the soil that optimizes water and nutrient uptake. It is now established that most of the genetic variation for RSA is driven by a suite of quantitative trait loci. As we discuss here, marker-assisted selection and quantitative trait loci cloning for RSA are underway, exploiting genomic resources, candidate genes and the knowledge gained from Arabidopsis, rice and other crops. Nonetheless, efficient and accurate phenotyping, modelling and collaboration with breeders remain important challenges, particularly when defining ideal RSA for different crops and target environments.
由于全球气候变化以及水和养分的稀缺，非生物胁迫日益限制作物产量。将根系结构（RSA）朝着优化土壤中根系分布的方向进行调控，从而优化水分和养分吸收，是将这些因素对产量的负面影响降至最低的一种方法。现在已经确定，RSA的大部分遗传变异是由一系列数量性状基因座驱动的。正如我们在此所讨论的，利用基因组资源、候选基因以及从拟南芥、水稻和其他作物中获得的知识，正在开展针对RSA的标记辅助选择和数量性状基因座克隆工作。尽管如此，高效准确的表型分析、建模以及与育种者的合作仍然是重大挑战，尤其是在为不同作物和目标环境定义理想的RSA时。
