Genomic prediction and selection response for grain yield in safflower - Zhao, Huanhuan; Lin, Zibei; Khansefid, Majid; Tibbits, Josquin F.; Hayden, Matthew J. (Frontiers in Genetics, 2023)
In plant breeding programs, multiple traits are recorded in each trial, and the traits are often correlated. Correlated traits can be incorporated into genomic selection models, especially for traits with low heritability, to improve prediction accuracy. In this study, we investigated the genetic correlation between important agronomic traits in safflower. We observed the moderate genetic correlations between grain yield (GY) and plant height (PH, 0.272 0.531), and low correlations between GY and days to flowering (DF, -0.157 0.201). A 4-20% prediction accuracy improvement for GY was achieved when PH was included in both training and validation sets with multivariate models. We further explored the selection responses for GY by selecting the top 20% of lines based on different selection indices. Selection responses for GY varied across sites. Simultaneous selection for GY and seed oil content (OL) showed positive gains across all sites with equal weights for both GY and OL. Combining g×E interaction into genomic selection (GS) led to more balanced selection responses across sites. In conclusion, GS is a valuable breeding tool for breeding high GY, OL, and highly adaptable safflower varieties.