Genomic selection of forage agronomic traits in winter wheat. Issue 1 (14th December 2020) - Maulana, Frank; Kim, Ki‐Seung; Anderson, Joshua D.; Sorrells, Mark E.; Butler, Twain J.; Liu, Shuyu; Baenziger, P. Stephen; Byrne, Patrick F.; Ma, Xue‐Feng
Abstract: Genomic selection (GS) can improve genetic gain of complex traits in plant breeding. Phenotyping agronomic traits of winter wheat ( Triticum aestivum L.) for dual‐purpose use is expensive and time‐consuming. In this study, we compared the prediction accuracies of four GS models (RR‐BLUP, GBLUP, GAUSS, and BL) for forage yield (FY), plant height (PH) and heading date (HD) of the hard winter wheat diversity panel ( n = 298) using random and stratified sampling methods. In addition, we determined the appropriate training population (TP) size and marker density for GS of the traits. Moderate to high prediction accuracies ranging from 0.66 to 0.69 for FY, 0.46 to 0.49 for PH, and 0.71 to 0.74 for HD were observed for the GS models. However, the sampling method had little or no impact on prediction accuracy. The RR‐BLUP, GBLUP, and GAUSS models produced slightly greater prediction accuracies than BL for all traits studied. Prediction accuracies increased with increasing TP size and marker density in all the GS models tested. However, increase of prediction accuracy started to plateau at n TP = 180 lines and 1, 000; 1, 500; or 3, 000 SNPs suggesting that the minimum TP size and marker density were about 180 lines and 1, 000 or more SNPs, depending on the model and trait. The impact of TP size on prediction accuracy was greater for RR‐BLUP, GAUSS, and GBLUP than for BL model. This study suggests that RR‐BLUP, GBLUP, and GAUSS are viable models for selecting the forage agronomic traits during dual‐purpose wheat breeding. Core Ideas: Genomic selection models were compared for forage agronomic traits of winter wheat. Prediction accuracies were estimated by varying TP sizes and marker densities. Moderate accuracies were observed for models trained with limited phenotypic data.