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Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions

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Abstract

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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.

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Abbreviations

BLUP:

Best linear unbiased predictor

GBLUP:

Genomic estimated best linear unbiased predictor

GEBV:

Genomic estimated breeding value

G*E:

Genotype by environment interactions

GS:

Genomic selection

MET:

Multi-environment trials

QTL:

Quantitative trait locus

Q*E:

QTL by environment interaction

SGL:

Sparse group lasso

SNP:

Single nucleotide polymorphism

TPE:

Target population of environments

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Acknowledgments

We thank Pierre Martre for providing the crop model. The reviewers provided excellent comments that significantly improved the paper. JRC-MARS––Meteorological Data Base––EC––JRC provided access to the interpolated meteorological data. This research was supported in part by USDA-NIFA-AFRI grants, award numbers 2009-65300-05661, 2011-68002-30029, and 2005-05130 and by Hatch project 149-449. Limagrain Europe provided financial support for N. Heslot.

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The authors declare that they have no conflict of interest.

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The experiments comply with the current laws of the countries in which they were performed.

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Authors and Affiliations

  1. Department of Plant Breeding and Genetics, Cornell University, 240 Emerson Hall, Ithaca, NY, 14853, USA

    Nicolas Heslot, Deniz Akdemir, Mark E. Sorrells & Jean-Luc Jannink

  2. USDA-ARS, R.W. Holley Center for Agriculture and Health, Cornell University, Ithaca, NY, 14853, USA

    Jean-Luc Jannink

  3. Limagrain Europe, CS3911, 63720, Chappes, France

    Nicolas Heslot

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  1. Nicolas Heslot
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  2. Deniz Akdemir
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  4. Jean-Luc Jannink
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Correspondence to Jean-Luc Jannink.

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Communicated by A. E. Melchinger.

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Heslot, N., Akdemir, D., Sorrells, M.E. et al. Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions. Theor Appl Genet 127, 463–480 (2014). https://doi.org/10.1007/s00122-013-2231-5

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