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Response to Early Generation Genomic Selection for Yield in Wheat.

David Bonnett^1,2, Yongle Li³, Jose Crossa^1,4

¹International Maize and Wheat Improvement Center, Texcoco, Mexico.

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|January 28, 2022

Summary

Genomic selection (GS) shows potential in early wheat breeding. Different statistical models for genomic estimated breeding values (GEBVs) impact predictive ability, with Gaussian Kernel and GBLUP performing best in early generations.

Keywords:

breeding methodology early generation genomic selection linear and non-linear kernels genomic matrices response to selection wheat breeding

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Area of Science:

Plant Breeding and Genetics
Agricultural Science
Quantitative Genetics

Background:

Accelerating genetic gain in grain yield is crucial for wheat improvement.
Early generation genomic selection (GS) offers a promising approach to enhance breeding efficiency.
Accurate prediction of breeding values in early generations is essential for successful GS implementation.

Purpose of the Study:

To investigate the effectiveness of GS in early generations for increasing genetic gain in wheat grain yield.
To evaluate different statistical models for calculating genomic estimated breeding values (GEBVs) in early generations.
To compare the predictive ability of various GEBV calculation methods for grain yield.

Main Methods:

A training set of 1,334 elite wheat lines was used to generate GEBVs for grain yield.
Three prediction methods were applied: Genomic Best Linear Unbiased Predictor (GBLUP), rrGBLUP with imputation, and Reproducing Kernel Hilbert Space (RKHS/Gaussian Kernel).
Early generation (F2) GEBVs were used to select individuals for subsequent generations and to compare predictive abilities using F2:4 lines.

Main Results:

GEBVs from different prediction methods in F2 individuals were not correlated.
Experiment 1 showed no significant yield difference between genomically selected and conventionally selected lines.
Experiment 2 revealed significant positive correlations between observed F2:4 line yields and F2 GEBVs from Gaussian Kernel (0.248) and GBLUP (0.195), but not rrGBLUP_imp.

Conclusions:

Genomic selection holds potential for application in early generations of wheat breeding.
The choice of statistical model for GEBV calculation is critical and may differ between early generations and inbred lines.
Gaussian Kernel and GBLUP demonstrated predictive ability for grain yield in early generations, highlighting the importance of model selection.