Search

Genomic methods including genome wide association analysis (GWAS), genomic selection (GS) and RNA-seq allow for faster selection of superior breeding lines and for identification and resolution of candidate genes. A panel of 240 soft red winter wheat (Triticum aestivum L.) cultivars and breeding lines were subjected to soil waterlogging stress over two seasons at Stuttgart, AR and St. Joseph, LA, US. Total concentrations of P, K, Ca, Mg, Mn, Fe, Al, B, Cu, Na, S and Zn were determined in wheat shoots post-waterlogging using inductively coupled plasma spectroscopy. Yield components kernel number per spike (KNPS), kernel weight per spike (KWS) and thousand kernel weight (TKW) were measured at plant maturity. Negative correlations between TKW and KWS with aluminum and iron concentrations indicated the impact of elemental toxicity on grain production. A ten-fold cross-validation (CV) analysis and ridge regression BLUP (RR-BLUP) model found GS prediction accuracies (rgs) of micro and macronutrient concentrations to range from rgs = 0.06 to 0.52 and improved as more site-years were included in the analysis. The ratio of genomic to phenotypic prediction accuracy (rgs /H1/2) was greater than 0.50 for eight of the twelve elements, indicating the potential for using GS to select for shoot micro and macronutrient concentrations in the absence of phenotypic data. GWAS identified forty-seven highly significant (p < 0.00001), twenty-three very significant and consistent (p < 0.0005) and eight significant and consistent (p < 0.001) marker trait associations (MTA) for the twelve micro and macronutrients measured. Lastly, RNA-seq was used for transcriptome and gene expression analysis under waterlogged and non-waterlogged conditions in wheat cultivars ‘Pioneer Brand 26R61’ and ‘AGS 2000’. Around 300 million pair-end reads were developed, covering approximately 16 Gb of the wheat transcriptome. In total, 64,911 (AGS200) and 60,414 (26R61) were obtained and 58,753 expressed genes were observed across both cultivars and treatments. Overall, the results of this study have and will enable genomics assisted breeding for waterlogging tolerance within the University of Arkansas Wheat Breeding Program by allowing for selection of materials with reduced micro and macronutrient concentrations in new breeding lines in the absence of phenotypic data