Prediction of Maize Single‐Cross Performance Using RFLPs and Information from Related Hybrids

Publisher Website
Google Scholar
Abstract
Methods for predicting hybrid yield would facilitate the identification of superior maize (Zea mays L.) single crosses. Best linear unbiased prediction of the performance of single crosses, based on (i) restriction 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 YM = C V−1 yP, where: yM = m × 1 vector of predicted yields of missing (i.e., no yield data available) single crosses; C = m × n matrix of genetic covariances between the missing and predictor hybrids; V = n × n matrix of phenotypic variances and covariances among predictor hybrids; and yP = nn × 1 vector of predictor hybrid yields corrected for trial effects. From a set of 54 single crosses, made between six Iowa Stiff Stalk Synthetic (SSS) and nine non‐SSS inbreds, 100 different sets of n = 10, 15, 20, 25, or 30 predictor hybrids were chosen at random. Pooled correlations between predicted and observed yields of the remaining (54 − n) hybrids ranged from 0.654 to 0.800. The correlations were slightly higher when dominance variance was included in the model or when coefficients of coancestry were determined from RFLP rather than pedigree data. The correlations remained relatively stable across different, arbitrary values of genetic variances. The results suggested that single‐cross yield can be predicted effectively based on parental RFLP data and yields of a related set of hybrids.

This publication has 0 references indexed in Scilit: