Figure 1.

The two optimization schemes used in the article. The two scenarios in this figure are referred to as U-Opt and T-Opt: U-Opt describes the case where the data consists of the matrix of genomic scores for a candidate set, X_Candidate; in T-Opt, we also know the genomic scores for a test set, X_TS. Based on a design criterion, a training set of a given size is selected from the candidate set, the genomic score matrix for these individuals is denoted by X_TRS. The genotypes in candidate set but not selected in the training set constitute the remaining set, with the score matrix X_Remaining. We observe the phenotypes after the training set is identified and using these trait values in the training set and X_TRS a genomic prediction model will be built. The algorithms A- and D- Opt cannot use the genomic scores of the test set in T-Opt.