Genomic selection and association mapping for wheat processing and end-use quality

Abstract

Globally, wheat (Triticum aestivum L.) is the second most widely grown cereal grain and is primarily used as a food crop. To meet the demands for human consumption, cultivars must possess suitable end-use quality for release and acceptability. However, breeding for quality traits is often considered a secondary goal, largely due to amount of seed needed and overall expense of such testing. Without testing and selection, many undesirable materials tend to be advanced. Here we demonstrate two methods, mega-genome-wide association mapping and genomic selection, to enhance selection accuracy for quality traits in the CIMMYT bread wheat breeding program. The methods were developed using high-density SNPs detected from genotyping-by-sequencing and processing and end-use quality evaluations from unbalanced yield trial entries (n = 4,095) during 2009 to 2014, at Ciudad Obregon, Sonora, Mexico. Genome-wide association mapping, with covariates for population structure and kinship, was applied for each trait to each site-year individually and results were combined across years in a mega-analysis using an inverse variance, fixed effect model in JMP-Genomics. This method presents a new way to detect genes of interest within a breeding program and develop markers for selection of these traits, which can then be used in earlier generations. Genomic selection prediction models were developed using ridge regression, Gaussian kernel, partial least squares, elastic net, and random forest models in R. With these predictions genomic selection (GS) can be applied at earlier stages and undesirable materials culled before implementing expensive yield and quality screenings. In general, prediction accuracy increased over time as more data was available to train the model. Based on these prediction accuracies, we conclude that genomic selection can be a useful tool to facilitate earlier generation selection for end-use quality in CIMMYT bread wheat breeding. Genomic selection was conducted for processing and end-use quality traits in the Kansas hard red winter wheat breeding unit. Genomic predictions demonstrate increases in accuracy with added data over time. These data demonstrate that current genomic selection models will need more data to continue improvement in prediction accuracy.