106174 Genomic Selection for Forage Quality Traits: A Case Study in a Forage Wheat Breeding Program.
Poster Number 107
Tuesday, October 24, 2017
Tampa Convention Center, East Exhibit Hall
Phenotyping forage quality traits is time-consuming in forage wheat breeding because it is done multiple times during a grazing season. The objectives of this case study were to 1) assess the potential of using genomic selection (GS) approach to predict forage quality traits in forage wheat breeding, 2) determine the effect of training population (TP) size on prediction accuracy, and 3) determine the effect of marker-density on prediction accuracy. An association mapping panel, consisted of 298 hard red winter wheat lines from the Triticeae Coordinated Agricultural Project (TCAP), was used in this study. The panel was genotyped using the wheat iselect 90K SNP genotyping array and phenotyped in two field environments for crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), total digestible nutrients (TDN), water soluble carbohydrates (WSC), sugars (SUG), lignin content (LC), and in vitro dry matter digestibility (IVTDMD). Prediction accuracies of RRBLUP, GAUSS and Bayesian LASSO GS models for forage quality traits were compared using random and stratified sampling methods. In addition, the effect of TP size and marker-density on prediction accuracy was explored. Generally, moderate to high prediction accuracies were observed, depending on the trait. Among traits studied, LC had the highest accuracy (0.61), followed by CP (0.59) and WSC had the lowest (0.28). The GS models produced similar accuracies for all traits, and sampling method had no effect on accuracy, except stratified sampling which produced higher accuracies of WSC and IVTDMD with Bayesian LASSO. Furthermore, increasing TP size and marker-density increased accuracies of all traits, and increasing the TP size was more effective than marker-density. However, the sampling method of marker-density did not have any significant effect on accuracy. Given field limitation in measuring forage quality traits, GS provides an alternative approach to facilitate selection of forage quality traits during forage wheat breeding.