Arvalis-Institut du végétal, Biopôle Clermont Limagne, 63360, Saint-Beauzire, France.
Centre de recherche de Chappes, Biogemma, Route d'Ennezat CS90216, 63720, Chappes, France.
Theor Appl Genet. 2019 Oct;132(10):2859-2880. doi: 10.1007/s00122-019-03393-2. Epub 2019 Jul 19.
Environmental clustering helps to identify QTLs associated with grain yield in different water stress scenarios. These QTLs could be useful for breeders to improve grain yields and increase genetic resilience in marginal environments. Drought is one of the main abiotic stresses limiting winter bread wheat growth and productivity around the world. The acquisition of new high-yielding and stress-tolerant varieties is therefore necessary and requires improved understanding of the physiological and genetic bases of drought resistance. A panel of 210 elite European varieties was evaluated in 35 field trials. Grain yield and its components were scored in each trial. A crop model was then run with detailed climatic data and soil water status to assess the dynamics of water stress in each environment. Varieties were registered from 1992 to 2011, allowing us to test timewise genetic progress. Finally, a genome-wide association study (GWAS) was carried out using genotyping data from a 280 K SNP chip. The crop model simulation allowed us to group the environments into four water stress scenarios: an optimal condition with no water stress, a post-anthesis water stress, a moderate-anthesis water stress and a high pre-anthesis water stress. Compared to the optimal water condition, grain yield losses in the stressed conditions were 3.3%, 12.4% and 31.2%, respectively. This environmental clustering improved understanding of the effect of drought on grain yields and explained 20% of the G × E interaction. The greatest genetic progress was obtained in the optimal condition, mostly represented in France. The GWAS identified several QTLs, some of which were specific of the different water stress patterns. Our results make breeding for improved drought resistance to specific environmental scenarios easier and will facilitate genetic progress in future environments, i.e., water stress environments.
环境聚类有助于确定与不同水分胁迫情景下的粒产量相关的 QTL。这些 QTL 可用于改良粒产量，增加边缘环境下的遗传弹性。干旱是限制全球冬小麦生长和生产力的主要非生物胁迫之一。因此，需要培育新的高产和耐旱品种，这需要提高对干旱抗性的生理和遗传基础的理解。利用 210 个欧洲优良品种进行了 35 个田间试验。在每个试验中都对粒产量及其构成进行了评分。然后利用详细的气候数据和土壤水分状况运行作物模型，以评估每个环境中的水分胁迫动态。品种登记时间从 1992 年到 2011 年，允许我们检验随时间推移的遗传进展。最后，使用来自 280K SNP 芯片的基因型数据进行了全基因组关联研究（GWAS）。作物模型模拟允许我们将环境分为四个水分胁迫情景：无水分胁迫的最优条件、花后水分胁迫、中期水分胁迫和高前期水分胁迫。与最优水分条件相比，胁迫条件下的粒产量损失分别为 3.3%、12.4%和 31.2%。这种环境聚类提高了对干旱对粒产量影响的理解，并解释了 20%的 G × E 互作。最大的遗传进展是在最优条件下获得的，主要代表法国。GWAS 鉴定了几个 QTL，其中一些是不同水分胁迫模式特有的。我们的研究结果使针对特定环境情景的改良耐旱性的选育变得更容易，并将促进未来环境（即水分胁迫环境）中的遗传进展。
