Dissection of Soil Waterlogging Tolerance in Soft Red Winter Wheat through Integration of Genomic Approaches.

Soil waterlogging reduces wheat grain yield due to increased availability and uptake of microelements, resulting in toxicity. The objectives of this study were to identify marker-trait associations (MTA) for yield components and elemental concentrations under waterlogging stress using a genome wide association study (GWAS) and to determine their predictability using genomic selection (GS). A panel of 240 soft red winter wheat lines were subjected to soil waterlogging over two seasons at the RREC in Stuttgart, AR. Kernel number spike^-1, kernel weight spike^-1 and biomass spike^-1 were measured after harvest. Concentrations of macro and microelements were determined in plant shoots post-waterlogging using inductively coupled plasma atomic emission spectroscopy. After filtering, 92,717 single nucleotide polymorphism (SNP) markers were generated using genotype by sequencing. Significant genetic variation for yield components and elemental concentrations was observed in the lines. GWAS found significant (P < 0.00001) MTA for Al and Mn only, indicating quantitative inheritance for most traits. Genomic selection prediction accuracies (r) using 92,717 SNPs were low (r = -0.11 to 0.40) particularly for traits with low heritability. However, accuracy increased significantly, for example from r = -0.11 to 0.60 in the case of Al, when only a subset of significant markers (P < 0.05) identified through GWAS were used in the GS model. Overall, GS out performed phenotypic selection for traits with low heritability. These results suggest that an integrated genomics approach that combines the use of GWAS and GS is feasible for making genetic gains in complex, quantitatively inherited traits.