Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions
Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions
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DOI：
10.1007/s00122-013-2231-5
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发表时间：
2014-02-01
影响因子：
5.4
通讯作者：
Jannink, Jean-Luc
中科院分区：
文献类型：
--
作者：
Heslot, Nicolas;Akdemir, Deniz;Jannink, Jean-Luc
关键信息开发模型来预测基因型与环境的相互作用，在未观察到的环境中，使用环境协变量，作物模型和基因组选择。基因型与环境互作（G*E）是分析表型的关键问题之一。长期以来，使用环境数据来建模G*E一直是人们感兴趣的主题，但受到与基因组选择方法所解决的问题相同的问题的限制：大量相关的预测因子，每个预测因子解释了少量的总方差。此外，基因型对压力的非线性反应预计将使分析进一步复杂化。使用作物模型，从每日天气数据预测作物发育阶段的应力协变量，我们提出了一个扩展的因子回归模型的基因组选择。该模型进一步扩展到标记水平，使环境互作（Q*E）的数量性状位点（QTL）的建模，在全基因组范围内。一个新开发的集成方法，软规则拟合，被用来改进这个模型和捕获非线性响应的QTL的压力。该方法使用一个大的冬小麦数据集进行测试，代表了大规模商业育种计划中可用的数据类型。在未观测到的环境中预测基因型表现的准确性平均提高了11.1%，预测准确性的变异性降低了10.8%。通过利用农学知识和育种计划生成的大型历史数据集，这个新模型可以深入了解基因型与环境相互作用的遗传结构，并可以根据过去和未来的天气情况预测基因型表现。
Key message Development of models to predict genotype by environment interactions, in unobserved environments, using environmental covariates, a crop model and genomic selection. Application to a large winter wheat dataset.Genotype by environment interaction (G*E) is one of the key issues when analyzing phenotypes. The use of environment data to model G*E has long been a subject of interest but is limited by the same problems as those addressed by genomic selection methods: a large number of correlated predictors each explaining a small amount of the total variance. In addition, non-linear responses of genotypes to stresses are expected to further complicate the analysis. Using a crop model to derive stress covariates from daily weather data for predicted crop development stages, we propose an extension of the factorial regression model to genomic selection. This model is further extended to the marker level, enabling the modeling of quantitative trait loci (QTL) by environment interaction (Q*E), on a genome-wide scale. A newly developed ensemble method, soft rule fit, was used to improve this model and capture non-linear responses of QTL to stresses. The method is tested using a large winter wheat dataset, representative of the type of data available in a large-scale commercial breeding program. Accuracy in predicting genotype performance in unobserved environments for which weather data were available increased by 11.1 % on average and the variability in prediction accuracy decreased by 10.8 %. By leveraging agronomic knowledge and the large historical datasets generated by breeding programs, this new model provides insight into the genetic architecture of genotype by environment interactions and could predict genotype performance based on past and future weather scenarios.