Accelerating wheat breeding for end-use quality through association mapping and multivariate genomic prediction

Area of Science:

• Agricultural Science
• Plant Breeding
• Genetics

Background:

• End-use quality evaluation in hard-winter wheat (Triticum aestivum L.) breeding is costly and time-consuming.
• Quality assessments are typically performed late in breeding programs after other traits are selected.

Purpose of the Study:

• Identify genetic variants for baking quality traits via genome-wide association study (GWAS).
• Develop improved genomic selection (GS) models for enhanced end-use quality in wheat breeding.

Main Methods:

• Genotyping-by-sequencing (GBS) was used on 462 advanced hard-winter wheat lines.
• Genome-wide association study (GWAS) and various genomic selection (GS) models were evaluated for baking quality traits.

Main Results:

• Significant associations for mixograph mixing time and bake mixing time were detected, linked to glutenin and gliadin loci.
• Candidate genes include phosphate-dependent decarboxylase and lipid transfer protein genes.
• Improved GS models, including univariate GS with covariates and multivariate GS, increased prediction accuracies compared to baseline models.

Conclusions:

• GWAS identified genetic variants suitable for marker-assisted breeding.
• Enhanced genomic prediction models can significantly accelerate wheat breeding for improved end-use quality.