Genomic prediction in hybrid breeding: I. Optimizing the training set design

Key message Training sets produced by maximizing the number of parent lines, each involved in one cross, had the highest prediction accuracy for H0 hybrids, but lowest for H1 and H2 hybrids. Abstract Genomic prediction holds great promise for hybrid breeding but optimum composition of the training set (TS) as determined by the number of parents ( n TS ) and crosses per parent ( c ) has received little attention. Our objective was to examine prediction accuracy ( r_a ) of GCA for lines used as parents of the TS (I1 lines) or not (I0 lines), and H0, H1 and H2 hybrids, comprising crosses of type I0 × I0, I1 × I0 and I1 × I1, respectively, as function of n TS and c . In the theory, we developed estimates for r_a of GBLUPs for hybrids: (i) r̂_a based on the expected prediction accuracy, and (ii) r̃_a based on r_a of GBLUPs of GCA and SCA effects. In the simulation part, hybrid populations were generated using molecular data from two experimental maize data sets. Additive and dominance effects of QTL borrowed from literature were used to simulate six scenarios of traits differing in the proportion ( τ SCA = 1