Genomic Selection for Predicting Fusarium Head Blight Resistance in a Wheat Breeding Program

Area of Science:

• Plant breeding
• Quantitative genetics
• Agricultural science

Background:

• Fusarium head blight (FHB) poses a significant threat to wheat production.
• Genomic selection (GS) offers a powerful approach to improve crop resistance through marker-trait associations.
• Developing accurate GS models is crucial for accelerating breeding programs.

Purpose of the Study:

• To develop and evaluate genomic selection (GS) models for predicting Fusarium head blight (FHB) resistance in wheat (Triticum aestivum L.).
• To assess the impact of various factors, including imputation methods, statistical models, marker density, training population size, and relationship matrices, on prediction accuracy.

Main Methods:

• Genotyping-by-sequencing (GBS) was employed to identify 5054 single-nucleotide polymorphisms (SNPs).
• Five genotypic imputation methods, three statistical models (RR-BLUP, LASSO, elastic net), varying marker densities, and training population sizes were compared.
• Marker-based and pedigree-based relationship matrices were evaluated, with and without controlling for relatedness.

Main Results:

• No significant differences in prediction accuracy were found among the five imputation methods.
• The ridge-regression best linear unbiased predictor (RR-BLUP) model consistently outperformed other statistical models.
• Prediction accuracy decreased with reduced marker numbers (below 3000 SNPs), smaller training populations (below 192 individuals), and the use of pedigree-based relationship matrices.

Conclusions:

• Genomic selection demonstrates considerable promise for enhancing Fusarium head blight resistance in wheat breeding programs.
• Optimizing marker density, training population size, and utilizing marker-based relationship matrices are critical for maximizing GS prediction accuracy.
• The RR-BLUP model is a robust choice for developing GS models for FHB resistance in wheat.