Modified full-sib selection and best linear unbiased prediction of progeny performance in a European F2 maize population |
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Authors: | C. Flachenecker M. Frisch J. Muminovic K. C. Falke A. E. Melchinger |
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Affiliation: | Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, D-70593 Stuttgart, Germany;;Corresponding author, E-mail: |
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Abstract: | Four cycles of modified recurrent full‐sib (FS) selection were conducted in an intermated F2 population of European flint maize. The objectives of our study were to monitor trends across selection cycles in the estimates of population mean, inbreeding coefficients and variance components, and to investigate the usefulness of best linear unbiased prediction (BLUP) of progeny performance under the recurrent FS selection scheme applied. We used a selection rate of 25% for a selection index, based on grain yield and dry matter content. A pseudo‐factorial mating scheme was used for recombination. In this scheme, the selected FS families were divided into an upper‐ranking group of parents mated to the lower‐ranking group. Variance components were estimated with restricted maximum likelihood (REML). Average grain yield increased 1.2 t/ha per cycle, average grain moisture decreased 20.1 g/kg per cycle, and the selection index relative to the F2 check entries decreased 0.3% per cycle. For a more precise calculation of selection response, the four cycles should be tested together in multi‐environmental trials. We observed a significant decrease in additive variance in the selection index, suggesting smaller future selection response. Predictions of FS family performance in Cn + 1 based on mean performance of parental FS families in Cn were of equal precision as those based on the mean additive genetic BLUP of their parents, and corresponding correlations were of moderate size for grain moisture and selection index. |
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Keywords: | Zea mays additive genetic best linear unbiased prediction pseudo-factorial mating scheme recurrent selection selection response variance components |
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