RT Dissertation/Thesis
T1 Prediction of hybrid performance in maize using molecular markers
A1 Schrag,Tobias
WP 2008/10/07
AB Maize breeders develop a large number of inbred lines in each breeding cycle, but, owing to resource constraints, evaluate only a small proportion of all possible crosses among these lines in field trials. Therefore, predicting the performance of hybrids by utilising the data available from related crosses to identify untested but promising hybrids is extremely important. The objectives of this thesis research were to develop and evaluate methods for marker-based prediction of hybrid performance (HP) in unbalanced data as typically generated in commercial maize hybrid breeding programs. 
For HP prediction, a promising approach uses the sum of effects across quantitative trait loci (QTL) as predictor. However, comparison of this approach with established prediction methods based on general combining ability (GCA) was lacking. In addition, prediction of specific combining ability (SCA) is also possible with this approach, but was so far not used for HP prediction. The objectives of the first study in this thesis were to identify QTL for grain yield and grain dry matter content, combine GCA with marker-based SCA estimates for HP prediction, and compare marker-based prediction with established methods. Hybrids from four Dent × Flint factorial mating experiments were evaluated in field trials and their parental inbreds were genotyped with amplified fragment length polymorphism (AFLP) markers. Efficiency for prediction of hybrids, of which both parents were testcross evaluated (Type 2), was assessed by leave-one-out cross-validation. The established GCA-based method predicted HP better than the approach exclusively based on markers. However, with greater relevance of SCA, combining GCA with marker-based SCA estimates was superior compared with HP prediction based on GCA only.
Linkage disequilibrium between markers was expected to reduce the prediction efficiency due to inflated QTL effects and reduced power. Thus, in the second study, multiple linear regression (MLR) with forward selection was employed for HP prediction. In addition, adjacent markers in strong linkage disequilibrium were combined into haplotype blocks. An approach based on total effects of associated markers (TEAM) was developed for multi-allelic haplotype blocks. Genome scans to search for significant QTL involve multiple testing of many markers, which increases the rate of false-positive associations. Thus, the TEAM approach was enhanced by controlling the false discovery rate. Considerable loss of marker information can be caused by few missing observations, if the prediction method depends on complete marker data. Therefore, the TEAM approach was improved to cope with missing marker observations. Modification of the cross-validation procedure reflected, that often only a subset of parental lines is crossed with all lines from the opposite heterotic group in a factorial mating design. The prediction approaches were evaluated with the same field data as in the previous study. The results suggested that with haplotype blocks instead of original marker data, similar or higher efficiencies for HP prediction can be achieved. 
Marker-based HP prediction of inter-group crosses between lines, which were marker genotyped but not testcross evaluated, was not investigated hitherto. Heterosis, which considerably contributes to maize grain yield, was so far not incorporated into marker-based HP prediction. Combined analyses of field trials from multiple experiments of a breeding program provide valuable data for HP prediction. With a mixed linear model analysis of such unbalanced data from nine factorial mating experiments, best linear unbiased prediction (BLUP) values for HP, GCA, SCA, line per se performance, and heterosis of 400 hybrids were obtained in the third study. The prediction efficiency was assessed in cross-validation for prediction of hybrids, of which none (Type 0) or one (Type 1) parental inbred was testcross evaluated. An extension of the established HP prediction method based on BLUP of GCA and SCA, but not using marker data, resulted in prediction efficiency intermediate for Type 1 and very low for Type 0 hybrids. Combining line per se with marker-based heterosis estimates (TEAM-LM) mostly resulted in the highest prediction efficiencies of grain yield and grain dry matter content for both Type 0 and Type 1 hybrids. For the heterotic trait grain yield, the highest prediction efficiencies were generally obtained with marker-based TEAM approaches.
In conclusion, this thesis research provided methods for the marker-based prediction of HP. The experimental results suggested that marker-based HP prediction is an efficient tool which supports the selection of superior hybrids and has great potential to accelerate commercial hybrid breeding programs in a very cost-effective manner. The significance of marker-based HP prediction is further enhanced by recent advances in production of doubled haploid lines and high-throughput technologies for rapid and inexpensive marker assays. 


K1 Heterosis
K1 Kombinationseignung
K1 Prognose
K1 Mais
K1 Ertrag
K1 Züchtung
K1 Pflanzenzüchtung
K1 AFLP
K1 DNS
K1 Marker
PP Hohenheim
PB Kommunikations-, Informations- und Medienzentrum der Universität Hohenheim
UL http://opus.uni-hohenheim.de/volltexte/2008/303