Abstract
Marker-based prediction of hybrid performance facilitates the
identification of untested single-cross hybrids with superior yield
performance. Our objectives were to (1) determine the haplotype block
structure of experimental germplasm from a hybrid maize breeding
program, (2) develop models for hybrid performance prediction based on
haplotype blocks, and (3) compare hybrid performance prediction based
on haplotype blocks with other approaches, based on single AFLP markers
or general combining ability (GCA), under a validation scenario
relevant for practical breeding. In total, 270 hybrids were evaluated
for grain yield in four Dent x Flint factorial mating experiments.
Their parental inbred lines were genotyped with 20 AFLP primer-enzyme
combinations. Adjacent marker loci were combined into haplotype blocks.
Hybrid performance was predicted on basis of single marker loci and
haplotype blocks. Prediction based on variable haplotype block length
resulted in an improved prediction of hybrid performance compared with
the use of single AFLP markers. Estimates of prediction efficiency
(R-2) ranged from 0.305 to 0.889 for marker-based prediction and from
0.465 to 0.898 for GCA-based prediction. For inter-group hybrids with
predominance of general over specific combining ability, the hybrid
prediction from GCA effects was efficient in identifying promising
hybrids. Considering the advantage of haplotype block approaches over
single marker approaches for the prediction of inter-group hybrids, we
see a high potential to substantially improve the efficiency of hybrid
breeding programs.
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