Combining Crop Growth Modeling With Trait-Assisted Prediction Improved the Prediction of Genotype by Environment Interactions

Abstract

Plant breeders evaluate their selection candidates in multi-environment trials to estimate their performance in contrasted environments. The number of genotype/environment combinations that can be evaluated is strongly constrained by phenotyping costs and by the necessity to limit the evaluation to a few years. Genomic prediction models taking the genotype by environment interactions (GEI) into account can help breeders identify combination of (possibly unphenotyped) genotypes and target environments optimizing the traits under selection. We propose a new prediction approach in which a secondary trait available on both the calibration and the test sets is introduced as an environment specific covariate in the prediction model (trait-assisted prediction, TAP). The originality of this approach is that the phenotyping of the test set for the secondary trait is replaced by crop-growth model (CGM) predictions. So there is no need to sow and phenotype the test set in each environment which is a clear advantage over the classical trait-assisted prediction models. The interest of this approach, called CGM-TAP, is highest if the secondary trait is easy to predict with CGM and strongly related to the target trait in each environment (and thus capturing GEI). We tested CGM-TAP on bread wheat with heading date as secondary trait and grain yield as target trait. Simple CGM-TAP model with a linear effect of heading date resulted in high predictive abilities in three prediction scenarios (sparse testing, or prediction of new genotypes or of new environments). It increased predictive abilities of all reference GEI models, even those involving sophisticated environmental covariates.

Keywords: crop growth model; gene-based modeling; genomic selection; genotype × environment interaction; multi-environment trials; wheat.

FIGURE 1

Schematic representation of CGM-TAP. CGM-TAP is a trait-assisted prediction approach in which the secondary trait is predicted using crop-growth modeling instead of being phenotyped.

FIGURE 2

Linear and quadratic regressions of GY on HD in the 16 environments and the 220 bread wheat varieties. The linear and the quadratic adjustments are represented by a blue line and a red curve, respectively. Adjusted R² are indicated for the linear (in blue) and for the quadratic (in red) adjustments.

FIGURE 3

Scatter plot of the predicted and observed values of the regression coefficient of a linear regression (A) or of a quadratic regression (B,C) of GY on HD in scenario CVnewE. Observed α and β correspond to the estimates obtained from the linear (A) and quadratic (B,C) regressions of GY on HD in each environment. Predicted α and β correspond to the prediction of the α (A) or α and β (B,C) of the predicted environment (leave-one-environment-out scheme), using a multiple linear regression of the α (A) or α and β (B,C) estimated in the calibration environments on the environmental covariates. The black line corresponds to the line y = x.