Thesis
QTL ANALYSIS AND GENOMIC SELECTION FOR PREHARVEST SPROUTING TOLERANCE IN WINTER WHEAT
Washington State University
Master of Science (MS), Washington State University
01/2022
DOI:
https://doi.org/10.7273/000004610
Handle:
https://hdl.handle.net/2376/125453
Abstract
Wheat (Triticum spp.) is a highly adapted and staple part of many diets worldwide, providing nearly 20% of the caloric intake for a large portion of the globe. The U.S. Inland Pacific Northwest specializes in soft white winter wheat, due to its ability to produce grain of exceptional quality while achieving high yields with less than 350 mm of rainfall in many areas. Ill-timed rain events prior to harvest can compromise the grain’s quality through preharvest sprouting when the grains germinate prior to harvest and starches in the grain are degraded due to the presence of alpha-amylase. The milling and baking industries use the Hagberg-Perten falling numbers method to measure alpha-amylase digestion of starch in grain. Finding solutions at the genetic level is crucial to minimize damage to farmers’ grain. The objectives of this thesis were to validate previously reported quantitative trait locus associated with preharvest sprouting tolerance, find potentially find new sources of large effect tolerance loci from the club wheat cultivar ‘Cara’, and to explore genomic prediction as a method for improving PHS selection in the winter wheat breeding program. We identified 19 QTL in our analysis of sprouting and seed dormancy traits in a biparental club x lax doubled haploid population. Four QTL were validated from previous association mapping studies of northwest U.S. germplasm. Of note, was QPHS.wsu-2D, which appeared to be a new source of physiological dormancy closely linked to the Compactum locus, the gene responsible for the club head type. Future work will need to determine this through either segregation analysis or mutagenesis. Additionally, we enhanced and simplified the complicated phenotyping methods for preharvest sprouting tolerance. Methods for screening alpha-amylase activity after misting were explored as an approach to predict FN after rain. Preliminary data suggest that alpha-amylase enzyme assays can better predict falling number than degree of visible sprouting. The accuracy of genomic prediction was improved by simplifying the current 1 to 10 sprouting scale. A binomial scoring system was able to increase mean genomic prediction accuracies to 0.74, compared to 0.60 with the original scoring scale.
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Details
- Title
- QTL ANALYSIS AND GENOMIC SELECTION FOR PREHARVEST SPROUTING TOLERANCE IN WINTER WHEAT
- Creators
- Jason Robert Wigen
- Contributors
- Camille M Steber (Advisor)Arron H Carter (Committee Member)Kimberly Garland-Campbell (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Crop and Soil Sciences, Department of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University
- Number of pages
- 121
- Identifiers
- 99900901336501842
- Language
- English
- Resource Type
- Thesis