Location: Crop Improvement and Genetics Research
Project Number: 2030-21430-014-000-D
Project Type: In-House Appropriated
Start Date: Mar 12, 2018
End Date: Mar 11, 2023
One goal of this five-year research project is to characterize prolamin diversity in several different wheat varieties. For commercial hard red spring wheat cultivars Butte 86 and Summit, allergen- and quality-associated molecular markers for specific prolamin genes will be developed. Deep DNA sequencing of prolamin genes expressed in Butte 86 seeds will enable refinement of its proteomic map and assessment of off-target effects of genome editing on wheat flour. In addition, new germplasm will be developed with reduced levels of pre-harvest sprouting (PHS) and lower immunogenic potential. Other goals are to characterize the genetic mechanisms of cold tolerance in wheat and cuticular wax (CW) variation in switchgrass. Collaborations will be pursued with perennial grass breeders to genotype populations for the purposes of applying genomic selection (GS) and increasing breeding efficiency. Objective 1: Develop novel wheat lines with improved end-use quality and decreased immunogenic potential that can be rapidly deployed into wheat breeding programs. Subobjective 1A: Reduce the immunogenic potential of wheat flour through genome editing. Subobjective 1B: Reduce PHS of a white wheat by decreasing thioredoxin h (Trx h) gene expression in developing seeds. Subobjective 1C: Improve gluten strength and reduce immunogenic potential of wheat flour through conventional mutation breeding. Objective 2: Develop new genomic and proteomic tools to assess variability of genes and proteins involved in flour end-use quality and immunogenic potential of U.S. wheat cultivars. Subobjective 2A: Characterize diversity of gluten protein genes among U.S. wheat lines. Subobjective 2B: Refine proteomic map of Butte 86 flour using new DNA sequence information. Subobjective 2C: Develop gel-free targeted proteomic methods to measure the levels of unique peptides in wheat flour. Subobjective 2D: Develop molecular markers that are linked to gluten strength and/or associated with gluten protein genes with high immunogenic potential. Objective 3: Characterize genetic mechanisms of wheat and bioenergy grasses’ responses to abiotic stress for enhanced crop improvement. Subobjective 3A: Identify genetic factors critical to the development of wheat cold temperature tolerance. Subobjective 3B: Determine extent of natural variation for CW in switchgrass and its association with leaf glaucousness and tolerance to water-stress. Objective 4: Generate novel genomic sequence information for pedigree reconstruction and genomic selection in bioenergy grasses to improve breeding. Subobjective 4A: Use reduced representation sequencing to genotype switchgrass and big bluestem for pedigree inference and to obtain kinship matrices. Subobjective 4B: Determine GS accuracies for seed dormancy, cell wall properties, and winter hardiness and map their Quantitative Trait Loci (QTL).
Objective 1: Wheat lines with improved end use quality or decreased immunogenic potential will be developed that can be rapidly incorporated into breeding efforts. Targeted genome editing using the clustered regularly-interspaced short palindromic repeats (CRISPR) system will be used to create mutations in wheat genes encoding immunogenic proteins such as omega-5 gliadins, omega-1,2 gliadins and alpha-gliadins. Immunogenic potential of selected lines will be determined using sera from patients with confirmed wheat allergies, celiac disease or non-celiac wheat sensitivity. The targeted genome editing approach will also be used reduce preharvest sprouting in wheat by inactivating thioredoxin genes expressed in developing endosperm. In addition, wheat lines deficient in proteins responsible for dough technological properties and/or for inducing gluten-related disorders will be identified using gel electrophoresis to screen a fast-neutron radiation mutagenized population of ‘Summit’. Lines that lack the targeted proteins will be evaluated for their flour quality and allergenic potential. Objective 2: Develop genomic and proteomic tools to assess variation of genes and proteins involved in flour end-use quality and immunogenicity. Sequence and expression diversity of prolamin and thioredoxin genes in different U.S. wheat cultivars will be determined through targeted sequencing and transcriptome analysis. Bioinformatics analysis will identify structural variations. In depth transcriptome sequencing data will be used to refine a proteomic map of ‘Butte 86’ flour. Allele-specific primer assays targeting prolamin gene regions will be developed to enable marker-assisted selection of wheat lines with differing gluten strength and reduced immunogenic potential. Objective 3: Elucidate genetic mechanisms of wheat and bioenergy grasses’ responses to abiotic stress. The underlying genetic factors involved in wheat cold tolerance will be identified by genetic mapping of three doubled haploid populations that exhibit variability in their ability to survive in cold temperatures. Using transcriptomic data, wheat candidate genes whose expression correlates with cold temperature tolerance will be identified. In switchgrass, mapping and diversity populations have been developed and planted across multiple locations. Measurements of epicuticular wax composition, crystal structure and leaf reflectance will be used to map Quantitative Trait Loci (QTL) and perform genome-wide association studies. Objective 4: Generate novel genomic sequence information for pedigree reconstruction and genomic selection (GS) in bioenergy grasses to improve breeding potential of switchgrass and big bluestem. Pedigree reconstruction will be performed in several experimental populations that will be genotyped using genomic DNA sequencing. Simulations will allow estimation of the number of markers required for accurate pedigree reconstruction. QTL will be identified and GS accuracy determined for seed dormancy, cell wall properties, and winter hardiness. Breeding values will be predicted using the method of ridge regression incorporating population and environment effects.