Browsing by Subject "Selection gain"

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Optimum allocation of test resources and comparison of alternative breeding schemes for hybrid maize breeding with doubled haploids
(2007) Longin, Carl Friedrich Horst; Melchinger, Albrecht E.
A major objective in hybrid maize breeding is the development of inbred lines with superior testcross performance. Inbred lines have commonly been derived in maize by recurrent selfing for five to six generations. The use of doubled haploids (DHs) enables the generation of completely homozygous lines in one step, representing a promising alternative to recurrent selfing. The implementation of the new DH technique in maize breeding requires an optimization of the entire breeding scheme in order to maximize progress from selection. The objectives of this study were to (i) compare selection gain (¢G) per breeding cycle with the probability of identifying superior genotypes with respect to the optimum allocation of test resources, (ii) evaluate several breeding schemes for an optimum use of the DH technique, (iii) determine the optimum number of test candidates and test locations as well the optimum type and number of testers for the different breeding schemes, and (iv) investigate the potential and limitations in the current DH technique in hybrid maize breeding. Monte Carlo simulations and numerical integration techniques were used to calculate the optimization criteria. The choice of G and the probability of identifying superior genotypes seems not to be crucial for the optimization of breeding schemes. The use of the new probability criterion supported the large optimum number of test locations determined by G. However, a larger impact of varying economic and quantitative-genetic parameters on the probability criterion than on ¢G was found, emphasizing their importance to maximize the chances of identifying a superior genotype. The use of Monte Carlo simulations for optimizing the allocation of test resources seems promising because of the possibility to calculate various optimization criteria for multi-stage selection in finite populations. However, the large computing power required for them can rapidly become prohibitive. Numerical integration techniques allow the calculation of G in multi-stage selection under the simplified assumption of infinite population size. The differences between finite and infinite population size were negligible for both, G and the optimum allocation of test resources. Thus, the simplifying assumption of infinite population size is justified as long as a tremendous reduction in computing time is warranted. Two-stage selection of DH lines was important to increase G and the probability of identifying superior genotypes, because it combines the evaluation of a large number of initial DH lines with the use of a large number of test locations. Consideration of an economic seed production indicated the necessity of separate breeding schemes for seed and pollen parent heterotic groups. For the pollen parent heterotic group, two-stage selection on testcross performance in both stages was most suitable, whereas for the seed parent heterotic group, line per se performance in the first stage followed by evaluation of testcross performance in the second stage was most appealing. The concentration of test resources on the most promising S1 families in early testing prior to DH production was superior to the evaluation of DH lines from the beginning of the selection process. The allocation of test resources was crucial to maximize G for a given scenario. Testers with broad genetic base allow a reduction of the number of testers in favor of an increased number of test locations and a largely increased G. An evaluation of progenies of each tester only in a single location instead of evaluating the progenies of each testers in all locations further increased G. With early testing prior to DH production, similar optimum numbers of testers and test locations were determined for evaluation of testcross performance of S1 families and DH lines within S1 families. This resulted in (i) a large optimum number of S1 families for the first stage and (ii) a small optimum number of S1 families but a large optimum number of DH lines within S1 families for the second stage. Current limitations in the DH technique with a low number of DH lines, which can be produced from a single maize plant, and high costs, affected the selection gain and the optimum allocation of test resources only marginally for breeding schemes with evaluation of DH lines from the beginning of the selection process. However, substantial improvements of the DH technique are required to realize the high potential of early testing prior to DH production in combination with a short cycle length. In conclusion, the optimum allocation of test resources is of utmost importance to increase selection gain under given economic resources. The implementation of DHs into maize breeding enables to shorten the length of the breeding cycle, but a careful evaluation of the breeding alternatives is required to maximize progress from selection.