Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions
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- 作者：Nicolas Heslot (1) (3)
Deniz Akdemir (1)
Mark E. Sorrells (1)
Jean-Luc Jannink (1) (2) - 刊名：Theoretical and Applied Genetics
- 出版年：2014
- 出版时间：February 2014
- 年：2014
- 卷：127
- 期：2
- 页码：463-480
- 全文大小：689 KB
- 作者单位：Nicolas Heslot (1) (3)
Deniz Akdemir (1)
Mark E. Sorrells (1)
Jean-Luc Jannink (1) (2)
1. Department of Plant Breeding and Genetics, Cornell University, 240 Emerson Hall, Ithaca, NY, 14853, USA
3. Limagrain Europe, CS3911, 63720, Chappes, France
2. USDA-ARS, R.W. Holley Center for Agriculture and Health, Cornell University, Ithaca, NY, 14853, USA - ISSN：1432-2242
文摘
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. Abstract 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.