PREDICTION OF MAIZE SINGLE-CROSS PERFORMANCE USING RFLPS AND INFORMATION FROM RELATED HYBRIDS

Methods for predicting hybrid yield would facilitate the identificatio n of superior maize (Zea mays L.) single crosses. Best linear unbiased prediction of the performance of single crosses, based on (i) restric tion fragment length polymorphism (RFLP) data on the parental inbreds and (ii) yield data on a related set of single crosses, was evaluated. Yields of m single crosses were predicted as y(M) = C V-1 y(P), where : y(M) = m x 1 vector of predicted yields of missing (i.e., no yield d ata available) single crosses; C = m x n matrix of genetic covariances between the missing and predictor hybrids; V = n x n matrix of phenot ypic variances and covariances among predictor hybrids; and y(P) = n x 1 vector of predictor hybrid yields corrected for trial effects. From a set of 54 single crosses, made between six Iowa Stiff Stalk Synthet ic (SSS) and nine non-SSS inbreds, 100 different sets of n = 10, 15, 2 0, 25, or 30 predictor hybrids were chosen at random. Pooled correlati ons between predicted and observed yields of the remaining (54 - n) hy brids ranged from 0.654 to 0.800. The correlations were slightly highe r when dominance variance was included in the model or when coefficien ts of coancestry were determined from RFLP rather than pedigree data. The correlations remained relatively stable across different, arbitrar y values of genetic variances. The results suggested that single-cross yield can be predicted effectively based on parental RFLP data and yi elds of a related set of hybrids.