Browsing by Author "Loeb, Amelia"

Now showing 1 - 1 of 1
  • Thumbnail Image
    The Genetic Architecture of Grain Quality and its Temporal Relationship with Growth and Development in Winter Malting Barley (Hordeum vulgare)
    Loeb, Amelia (Virginia Tech, 2023-06-26)
    This thesis explores the genetic architecture of malting quality within the Virginia Tech barley breeding program, and discusses implications for imposing selection on complex traits that are difficult to phenotype. Malting quality measures are destructive, and can not be performed before selection must be made for advancement of breeding lines in winter barley. A growing body of evidence suggests that malt quality is influenced by malting regime, growing environment, line genotype, and the interactions between them. We aim to better understand the genetic effect on malt quality in two manners: first, as it relates to the genetic architecture regulating malt quality parameters, and second the relationship between genetic growth patterns to end-use malting traits. This study included two years of breeding trial data of two and six-row winter malt barley across two locations. Results of a genome-wide association scan and genomic prediction of malt quality traits indicated that they are largely quantitative traits with complex inheritance. Previous studies have identified quantitative trait loci and genes regulating malt quality traits in markedly different germplasm. Heritability of traits ranged from 0.27 to 0.72, while mean predictive abilities ranged from 0.45 to 0.74. Thus, selection on genomic estimated breeding values (gEBVs) should perform similarly to selection on single phenotypic observations of quality, but can be done within the same season. This indicates that genomic selection may be a viable method to accelerate genetic improvement of malting quality traits. The use of gEBVs requires that lines be genotyped with genome-wide markers, somewhat limiting the number of candidate individuals. Selection on growth and development traits genetically correlated with quality measures could allow for selection among a much greater number of candidates if high-throughput phenotypes can be collected on many ungenotyped indivduals. Growth and development was quantified by the near-infrared vegetation index (NDVI) extracted from aerial images captured from multiple time points throughout the growing season. Estimates of genetic correlation identified time points throughout the season when quality traits are related to growth and development. We demonstrated that aerial imagery can discern growth patterns in barley and suggest ways it can be incorporated into the breeding pipeline.