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Title: Identifying quantitative end-use quality traits through marker-trait associations

Author
item Morris, Craig
item GILL, KULVINDER - WASHINGTON STATE U
item KING, GARRISON - WASHINGTON STATE U
item BURNS, JOHN - WASHINGTON STATE U
item Engle, Douglas

Submitted to: Wheat Newsletter
Publication Type: Abstract Only
Publication Acceptance Date: 5/1/2009
Publication Date: 8/15/2009
Citation: Morris, C.F., Gill, K.S., King, G.E., Burns, J., Engle, D.A. 2009. Identifying quantitative end-use quality traits through marker-trait associations. Annual Wheat Newsletter 55:29.

Interpretive Summary: Abstract only -- summary not required.

Technical Abstract: End-use quality traits (grain, milling and baking) are generally expensive and difficult to measure. We are in the process of estimating phenotypic trait values for a wide range of Pacific Northwest wheat genotypes, including soft white spring, winter and club, hard red winter and spring, and hard white winter and spring. Phase 2 of the research will attempt to associate molecular markers with quantitative variation in end-use quality “phenotypes”. We currently have assembled four large data sets derived from a long-term (ca. 10 years) study known as the “G&E” (Genotype and Environment). The G&E study utilizes grain samples produced from the Washington State University Cereal Variety Testing Program, a multi-location replicated trial of advanced breeding lines and current cultivars. The G&E uses single-rep grain samples from 4-6 locations each year. Most lines and cultivars are included in the study for three years; long term checks are included for the entire life of the program. The four nurseries are soft spring, soft winter, hard spring and hard winter. For soft spring, the long term checks are Alpowa and Zak. For soft white winter, they are Eltan, Madsen, Stephens, Hiller and Rely. The soft spring data set is comprised of approximately 340 samples, the soft winter set has about 970 samples. Following is a list of the traits that are under study: grain yield, test weight, grain protein, NIR grain hardness, SKCS single kernel hardness, weight and size and their standard deviations, break flour yield, flour yield, milling score, flour ash, flour protein, Mixograph water absorption, flour SDS sedimentation volume, Flour Swelling Volume, Rapid ViscoAnalyzer peak hot paste viscosity, cookie diameter, sponge cake volume, and polyphenol oxidase L-DOPA absorbance. Currently, the issue at hand is, “What is the best estimate of each genotype’s phenotype?” The first two approaches involve i) simple calculations of arithmetic means and ii) calculation of least squares means. The latter has the advantage of accommodating year-to-year variation to adjust the marginal means. As an example, differences between means and LS means for milling score ranged from -2.0 to +1.1 (soft winter, see figure) and -3.3 to +3.6 (soft spring); and for cookie diameter -0.14 to +0.13 (soft winter) and -0.17 to +0.11 (soft spring). Overall means were milling score, 84.1 and 83.7, and cookie diameter, 9.37 and 9.47 cm, soft winter and spring, respectively.