Best Linear Unbiased Prediction of Maize Single-Cross Performance
In preliminary studies, best linear unbiased prediction (BLUP) has been found useful for identifying high-yielding maize (Zea mays L.) single crosses prior to field evaluation. In this study, the effectiveness of BLUP for large-scale prediction of yield, moisture, stalk lodging, and root lodging was investigated. Multilocation data from 1990 to 1994 were obtained from the hybrid testing program of Limagrain Genetics. For each of 16 heterotic patterns, the performance of m untested single crosses was predicted from the performance of n tested single crosses as YM = CMP Cpp-1 YP, where YM = m × 1 vector of predicted performance of the untested single crosses; CMP = m × n matrix of genetic covariances between the untested single crosses and the tested single crosses; Cpp = n × n phenotypic covariance matrix among the tested single crosses; and YP = n × 1 vector of average performance of the tested single crosses, corrected for yield trial effects. Correlations between predicted and observed performance were obtained with a delete-one cross-validation procedure. For heterotic patterns with large (>100) numbers of tested single crosses, the correlations ranged from 0.426 to 0.762 for yield, 0.754 to 0.933 for moisture, 0.300 to 0.739 for stalk lodging, and 0.164 to 0.532 for root lodging. The correlations, especially for lodging traits, increased as larger numbers of tested single crosses were available. The results in this study were obtained from large and diverse data sets (600 inbreds, 15 183 data points, and 4099 tested single crosses across 16 heterotic patterns) and provide strong evidence that BLUP is useful for routine identification of superior single crosses prior to field testing.