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Genetic Analysis of Bread Making Quality Stability in Wheat using a Halberd X Len Recombinant Inbred Line PopulationPoudel, Ashima 2012 May 1900 (has links)
Wheat grain quality has a complex genetic architecture heavily influenced by the growing environment. Consistency in wheat quality not only affects the efficiency of milling and baking but also the quality of end-use products. The objectives of this study were to 1) analyze the different wheat quality parameters in Recombinant Inbred Lines (RILs) grown under different environments, and 2) to identify Quantitative Trait Loci (QTLs) associated with quality stability in RILs grown under different environments. A set of 180 RILs derived from two spring wheat lines 'Halberd' and 'Len' were grown at Uvalde and College Station TX, in the 2009/2010 growing season and at Chillicothe and College Station TX, in 2010/2011 growing seasons. The experiment was laid out in Randomized Complete Block Design (RCBD) with four replications within each location. Each line was tested for multiple quality traits that included grain hardness, protein content, dough mixing properties and bread baking quality using Single Kernel Characterization System (SKCS), Near-Infrared Reflectance Spectrometry (NIRS) analysis, mixograph and the Sodium Dodecyl Sulfate Sedimentation (SDSS) test. Genetic linkage map construction was carried out with 116 single nucleotide polymorphism (SNP) markers in the RILs. Then composite interval mapping was carried out to identify QTLs associated with quality traits.
The SDSS column height was positively correlated across four environments. Similarly, it was found to have significant positive correlation with mixing tolerance and peak time within and also across locations. However, the SDSS was negatively correlated with the hardness index. The protein percent was not significant with any of the quality traits within and across environments. We were able to detect many QTLs for different quality traits but most of them were site specific. Only a few QTLs were consistent across environments. Most of the QTLs for quality traits i.e., SDSS, peak time, mixing tolerance and hardness index were identified on chromosome 1B. We were able to detect overlapped QTLs for SDSS column height and mixing tolerance on chromosome 1B. Furthermore, overlapping QTLs for mixing tolerance and peak time were detected on an unknown chromosome. We also detected overlapping QTLs for hardness index on chromosome 1B. We identified one stable QTL for SDSS column height on chromosome 4B. This QTL was detected based on the coefficient of variation (CV) for SDSS in four different environments.
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