Author ORCID Identifier
Year of Publication
Doctor of Philosophy (PhD)
Agriculture, Food and Environment
Plant and Soil Sciences
Dr. David A. Van Sanford
Genomic selection (GS) is a form of marker-assisted selection (MAS) that simultaneously estimates all locus, haplotype or marker effects across the entire genome to calculate genomic estimated breeding values (GEBVs). Since its inception, it has had the attention of breeders keen on finding tools to accelerate genetic gain and reduce phenotyping costs in the breeding program. A first objective of this study was to evaluate strategies to design the training population (TP) and validating population (VP) to estimate GEBVs for grain yield and agronomic traits for wheat breeding lines. Our results demonstrate that, despite the small family size, an approach that includes lines from the same family in both the TP and VP, together with half-sibs and more distant lines, and only phenotyping the lines included in the TP, could be a useful, efficient design for establishing a GS scheme to predict grain yield in lines entering first year yield trials. A second objective was to investigate the design of the training population (TP) to predict Fusarium head blight (FHB) traits, and in particular the usefulness of regional FHB nurseries as a sources of lines for the training population. Our results confirmed the usefulness of regional nurseries as a source of lines to predict GEBVs for local breeding programs and showed that an index that includes deoxynivalenol (DON), together with Fusarium damaged kernels (FDK) and FHB rating could be an excellent choice to identify lines with low DON content and an overall improved FHB resistance. A third objective was to investigate the effect of reducing the marker set size used to run the GS model. Marker sets with different marker numbers were obtained performing Genome-Wide Association Studies (GWAS) on the Uniform Northern (NUS) and Uniform Southern (SUS) soft red winter wheat scab nurseries to select significant SNPs for FHB resistance traits at different P-value levels. Our results confirmed that GWAS offers an excellent tool to select significant markers reducing significantly the number of markers used to predict FHB resistance traits, moving a step forward in selecting lines with good resistance to DON accumulation and other FHB traits before evaluating them in the field. Overall, these findings have the potential impact of reducing phenotyping and genotyping costs and accelerate the breeding process.
Digital Object Identifier (DOI)
Fund awarded to the author: Graduate Research Assistantship (Plant and Soil Sciences Department). (2017-2021)
Lab's grant: U.S. Department of Agriculture, through the US Wheat and Barley Scab Initiative under agreement no. 59-0206-9-054
Verges, Virginia Laura, "Genomic Selection Strategies to Predict Grain Yield and Disease Resistance Traits in a Wheat Breeding Program" (2021). Theses and Dissertations--Plant and Soil Sciences. 145.