Figure 1.
Figure 1.

Description of four different prediction designs (Case A, Case B, Case C, and Case D) that split the data into training set (TRN) and testing set (TST). For the four cases, the TRN had 90% of the total number of lines whereas the TST had 10%. BL, Bayesian least absolute shrinkage and selection operator (LASSO); RR, ridge regression; SVR-linear, support vector regression with linear kernel; SVR-RBBF, support vector regression with radial basis function kernel.

 


Figure 2.
Figure 2.

Scatterplot of the average correlations for each population × environment combination obtained with the Bayesian least absolute shrinkage and selection operator (LASSO) model versus broad-sense heritability when the algorithm was trained with information from both main season plus off-season for stem rust (SR) and from both Njoro plus Toluca for yellow rust (YR). Populations are (1) PBW343 × Juchi, (2) PBW343 × Kingbird, (3) PBW343 × K-Nyangumi, (4) PBW343 × Muu, and (5) PBW343 × Pavon76.

 


Figure 3.
Figure 3.

Average correlation of stem rust for four genomic selection models (ridge regression, Bayesian least absolute shrinkage and selection operator (LASSO), support vector regression (SVR) with radial basis function (RBF) and SVR with linear kernel vs. the relatedness between populations as estimated from the G matrix (see Supplemental Fig. S3).