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Title:Genomic selection for glume blotch resistance and milling and baking quality traits in soft red winter wheat
Author(s):Jones, Olivia Clare
Advisor(s):Kolb, Frederic L.
Contributor(s):Mideros, Santiago X; Butts-Wilmsmeyer, Carolyn J.
Department / Program:Crop Sciences
Discipline:Crop Sciences
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Glume Blotch, Milling and Baking Quality
Genomic Selection
Disease Resistance
Abstract:Wheat (Triticum aestivum L.) is a major cereal crop with global importance, responsible for providing 20% of human calorie intake, commonly in the form of flour. Maintaining superior milling and baking quality while improving disease resistance are key objectives in a breeding program. Selection for milling and baking quality is critical for the acceptance of new wheat varieties to end users - millers and bakers. Fungal pathogens present a significant biotic threat to the quality and quantity of the wheat crop annually. Necrotrophic fungus Parastagonospora nodorum (syn. Stagonospora nodorum, Septoria nodorum) is a leading fungal threat to wheat production in humid regions. A P. nodorum infection results in leaf blotch and glume blotch in wheat and related grass species. The development of varieties possessing resistance to P. nodorum infections is essential to minimize the fungal threat. Glume blotch infections result in shriveled low weight kernel production, with losses as high as 30 to 50 percent under optimal conditions for P. nodorum. Genomic selection (GS) offers a promising avenue for the improvement of quantitative traits, especially those difficult to improve through traditional breeding methods. GS is a statistical genomics tool that combines all molecular marker information for an individual to calculate genomic estimated breeding values (GEBVs) that can be used for advancement selections. GS models provide more comprehensive estimates of quantitative traits than marker-assisted selection, as it captures small and large effect loci contributing to the phenotype. The implementation of GS models permits the prediction of an individual’s performance even before phenotyping has occurred. The utilization of GS models has the potential to accelerate the improvement of quantitative traits, including those that are difficult to phenotype, that are measured on an irregular basis, or those that are not assessed until late stages of development, in the breeding of wheat varieties. In this research, genotypic data already available for a panel of soft red winter wheat breeding lines representative of the University of Illinois’ breeding program was leveraged by collecting phenotypic data on glume blotch resistance and several milling and baking quality traits. Glume blotch resistance and milling and baking quality traits are known to be quantitative in nature. The objective was to determine if genomic selection could be used to select for these quantitative traits. Glume blotch resistance is often difficult to phenotype, and milling and baking quality parameters usually are not evaluated until a breeding line has been assessed agronomically for several years. As such these traits are attractive targets for genomic selection.
Issue Date:2018-04-20
Rights Information:Copyright 2018 Olivia Jones
Date Available in IDEALS:2018-09-04
Date Deposited:2018-05

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