Genome-wide association study and genomic prediction for end-use qualities in hard winter wheat
Date
2024
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Abstract
Wheat (Triticum aestivum L.) is a widely cultivated crop used primarily for human food, animal feed, and industrial products. Numerous wheat-based products have unique nutritional and functional requirements. In the global market, wheat quality is one of the determining factors of wheat's price and baked product characteristics. Thus, after grain yield, improving these qualities is one of the major breeding objectives in wheat. Chapter One: This chapter outlines wheat's origins and global production. It explores major quality traits like water absorption and dough rheological properties, plus their measurement methods. Factors impacting wheat quality and pertinent genes are discussed. Finally, key challenges and opportunities around breeding for improved wheat quality are addressed. Chapter Two: This chapter presents a genome-wide association study of water absorption capacity in hard winter wheat. Lines were phenotyped using the solvent retention capacity test and genotyped via genotyping-by-sequencing. Forty-three marker-trait associations were identified across 17 chromosomes, especially on chromosome 1B, indicating polygenic influence. Co-localization between identified marker-trait associations and the genes that have effects on water absorption was done, and some quantitative trait nucleotides (QTNs) were located near gluten glutenin, gliadin, and glycosyltransferase genes, confirming water absorption is a complex trait affected by different flour components. Chapter Three: This chapter presents genome-wide prediction models to predict water absorption capacity using a training population of 497 hard winter wheat genotypes. Univariate models were compared to multivariate genomic prediction models using two validation approaches - cross-validation with 100 permutations and a 20-80 split and forward validation utilizing three years of data (2019-2021) from the CSU ELITE Trial. Multivariate genomic prediction models integrating highly correlated traits like break flour yield or all traits as covariates showed improved accuracy compared to univariate models in both validation approaches, demonstrating that incorporating related phenotypic traits as covariates in multivariate models can substantially improve the accuracy of predicting water absorption capacity. Chapter Four: This chapter evaluates genomic prediction models for bread-baking quality traits in 790 wheat genotypes over the 2014-2022 growing seasons. Marker-trait associations identified via genome-wide association study (GWAS) were incorporated as fixed effects. Three models were compared using cross-validation and forward validation: a model without fixed effect, with Glu-B1al allele (Bx7OE + 8 subunit) kompetitive allele-specific PCR (KASP) marker data as a fixed effect, and with GWAS-identified markers as fixed effects. Overall, the model with GWAS-identified markers as fixed effects showed the highest prediction accuracy. However, prediction accuracy decreased for bake loaf volume prediction specifically, suggesting that trait-specific tuning is needed to optimize genomic prediction models for different baking quality traits. These chapters reinforce the genetic complexity of water absorption capacity and baking quality traits in wheat. Polygenic inheritance was revealed for water absorption capacity. Genomic prediction that incorporates phenotypic covariates and GWAS-derived markers is the best approach to selecting water absorption and baking traits.
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Subject
genome-wide association study
marker trait association
water absorption capacity
genomic prediction
end-use quality
prediction accuracy