Browsing by Author "Huang, Mao"
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- The Accuracy of Genomic Prediction between Environments and Populations for Soft Wheat TraitsHuang, Mao; Ward, Brian P.; Griffey, Carl A.; Van Sanford, David A.; McKendry, Anne; Brown-Guedira, Gina L.; Tyagi, Priyanka; Sneller, Clay H. (2018-12)Genomic selection (GS) uses training population (TP) data to estimate the value of lines in a selection population. In breeding, the TP and selection population are often grown in different environments, which can cause low prediction accuracy when the correlation of genetic effects between the environments is low. Subsets of TP data may be more predictive than using all TP data. Our objectives were (i) to evaluate the effect of using subsets of TP data on GS accuracy between environments, and (ii) to assess the accuracy of models incorporating marker x environment interaction (MEI). Two wheat (Triticum aestivum L.) populations were phenotyped for 11 traits in independent environments and genotyped with single-nucleotide polymorphism markers. Within each population trait combination, environments were clustered. Data from one duster were used as the TP to predict the value of the same lines in the other cluster(s) of environments. Models were built using all TP data or subsets of markers selected for their effect and stability. The GS accuracy using all TP data was >0.25 for 9 of 11 traits. The between-environment accuracy was generally greatest using a subset of stable and significant markers; accuracy increased up to 48% relative to using all TP data. We also assessed accuracy using each population as the TP and the other as the selection population. Using subsets of TP data or the MEI models did not improve accuracy between populations. Using optimized subsets of markers within a population can improve GS accuracy by reducing noise in the prediction data set.
- Association Analysis of Baking and Milling Quality Traits in an Elite Soft Red Winter Wheat PopulationGaire, Rupesh; Huang, Mao; Sneller, Clay H.; Griffey, Carl A.; Brown-Guedira, Gina L.; Mohammadi, Mohsen (2019-05)Although grain yield is the most important trait for growers, milling and baking industries demand high-quality and scab-free grains for various end products. To accelerate breeding of wheat (Triticum aestivum L.) cultivars with high grain quality, genetic dissection of quality traits is necessary. Genome-wide association studies (GWAS) were conducted to identify genomic regions responsible for milling and baking quality traits in soft red winter wheat (SRWW). Seven quality traits were evaluated in two locations and 2 yr for 270 elite SRWW lines. These traits include flour yield, softness equivalent, flour protein, and four solvent (lactose, sodium carbonate, sucrose, and water) retention capacity measurements. In this study, 27,449 single nucleotide polymorphism (SNP) markers were developed by using both genotyping-by-sequencing and 90K SNP array technologies. A linear mixed model in GWAS, accounting for population structure and kinship, was fitted to identify 18 [-log(P) >= 4.0] genomic regions on 12 different chromosomes associated with the quality traits. Only one of these associations seems to be a previously identified quantitative trait locus, whereas others are novel associations. The most significant associations for quality traits were identified on chromosomes 1B, 2A, 2B, 4B, 5A, 7A, and 7D. Candidate gene searches, facilitated by the wheat genome assembly, led us to the identification of putative genes related to SRWW quality traits including fasciclin-like arabinogalactan. The results of this study can be used in developing high-quality SRWW varieties for the eastern region of the United States.