<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Intrapopulation recurrent selection (IRS) has proven to be a promising breeding method in eucalyptus, mainly through being easier to carry out when compared to reciprocal recurrent selection (RRS). However, the recombination strategies in IRS that have not yet been compared. Thus, the purpose of this study was to verify the efficiency of different recombination methods in IRS. To do so, computer simulation was used considering different heritabilities (0.1, 0.5, 1.0), different initial allelic frequencies (0.2, 0.8) and allelic interactions without dominance and with complete dominance. The initial population consisted of 1000 individuals, which were selected at random for the beginning of cycle zero. These individuals were interbred two by two. Three selection strategies were carried out and, consequently, three recombination methods: recombine the best individuals selected within the best progenies; the best individuals phenotypically selected regardless of their genealogy; or selection in the mean value of the best progenies selected. It was observed that recombination of the best individuals regardless of their genealogy and of the best individuals within the best progenies provided for gains superior to recombination having only the mean of the progenies as reference. The average degree of dominance and the heritability of the trait should be considered at the time of choosing the method of selection followed by recombination.</p></abstract>

Intrapopulation recurrent selection (IRS) has proven to be a promising breeding method in eucalyptus, mainly through being easier to carry out when compared to reciprocal recurrent selection (RRS). However, the recombination strategies in IRS that have not yet been compared. Thus, the purpose of this study was to verify the efficiency of different recombination methods in IRS. To do so, computer simulation was used considering different heritabilities (0.1, 0.5, 1.0), different initial allelic frequencies (0.2, 0.8) and allelic interactions without dominance and with complete dominance. The initial population consisted of 1000 individuals, which were selected at random for the beginning of cycle zero. These individuals were interbred two by two. Three selection strategies were carried out and, consequently, three recombination methods: recombine the best individuals selected within the best progenies; the best individuals phenotypically selected regardless of their genealogy; or selection in the mean value of the best progenies selected. It was observed that recombination of the best individuals regardless of their genealogy and of the best individuals within the best progenies provided for gains superior to recombination having only the mean of the progenies as reference. The average degree of dominance and the heritability of the trait should be considered at the time of choosing the method of selection followed by recombination.

Computer Simulation for the Evaluation of Recombination Strategies in Intrapopulation Recurrent Selection in Eucalyptus

Silvae Genetica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Intrapopulation recurrent selection (IRS) has proven to be a promising breeding method in eucalyptus, mainly through being easier to carry out when compared to...

Computer Simulation for the Evaluation of Recombination Strategies in Intrapopulation Recurrent Selection in Eucalyptus

Published Online: Jul 01, 2017

Page range: 68 - 79

Received: Aug 14, 2012

DOI: https://doi.org/10.1515/sg-2013-0009

Keywords
Forest breeding, Quantitative Genetics, Monte Carlo, Breeding Strategies, Genetic Gain

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Computer Simulation for the Evaluation of Recombination Strategies in Intrapopulation Recurrent Selection in Eucalyptus

Published Online: Jul 01, 2017

Page range: 68 - 79

Received: Aug 14, 2012

DOI: https://doi.org/10.1515/sg-2013-0009

KeywordsForest breeding, Quantitative Genetics, Monte Carlo, Breeding Strategies, Genetic Gain

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
Forest breeding, Quantitative Genetics, Monte Carlo, Breeding Strategies, Genetic Gain