TESTCROSS ADDITIVE AND DOMINANCE EFFECTS IN BEST LINEAR UNBIASED PREDICTION OF MAIZE SINGLE-CROSS PERFORMANCE

Best linear unbiased prediction (BLUP) has been found to be useful in maize (Zea mays L.) breeding. The advantage of including both testcros s additive and dominance effects (Intralocus Model) in BLUP, rather th an only testcross additive effects (Additive Model), has not been clea rly demonstrated. The objective of this study was to compare the usefu lness of Intralocus and Additive Models for BLUP of maize single-cross performance. Multilocation data from 1990 to 1995 were obtained from the hybrid testing program of Limagrain Genetics. Grain yield, moistur e, stalk lodging, and root lodging of untested single crosses were pre dicted from (1) the performance of tested single crosses and (2) known genetic relationships among the parental inbreds. Correlations betwee n predicted and observed performance were obtained with a delete-one c ross-validation procedure. For the Intralocus Model, the correlations ranged from 0.50 to 0.66 for yield, 0.88 to 0.94 for moisture, 0.47 to 0.69 for stalk lodging, and 0.31 to 0.45 for root lodging. The BLUP p rocedure was consistently more effective with the Intralocus Model tha n with the Additive Model. When the Additive Model was used instead of the Intralocus Model, the reductions in the correlation were largest for root lodging (0.06-0.35), smallest for moisture (0.00-0.02), and i ntermediate for yield (0.02-0.06) and stalk lodging (0.02-0.08). The r atio of dominance variance (nu(D)) to total genetic variance (nu(G)) w as highest for root lodging (0.47) and lowest for moisture (0.10). The Additive Model may be used if prior information indicates that V-D fo r a given trait has little contribution to V-G. Otherwise, the continu ed use of the Intralocus Model for BLUP of single-cross performance is recommended.