Selecting common bean breeding populations via mixed models

  • Ramon Gonçalves de Paula Universidade Federal de Viçosa
  • Igor Gonçalves de Paula
  • Ana Laura Nicomedes Carneiro
  • Felipe Vicentino Salvador
  • José Eustáquio de Souza Carneiro
  • Pedro Crescêncio Souza Careiro
Keywords: Phaseolus vulgaris, Selection index, Recurrent selection, Self-pollinated crop

Abstract

Choosing breeding populations in a common bean (Phaseolus vulgaris L.) breeding program by recurrent selection is a crucial step, since it maximize the effort to find superior inbred lines. The application of mixed models methodology (REML/BLUP) in predicting breeding values has shown good results in animal and perennial crops breeding programs, while in annual crops its utilization still needs further results. We aimed to use the REML/BLUP methodology in selecting breeding populations in a common bean breeding program by recurrent selection. We evaluated thirty-five F3 populations in which individual plants data were assessed for grain yield and hypocotyl diameter and estimated the genetic potential of the population through mixed models and Jinks and Pooni methodologies. A selection index was used in order to select among and within population considering both characters simultaneously, through the population and individual BLUP means. The REML/BLUP has shown feasible to predict the potential of breeding populations as well as to select those populations, considering more than one characters. Selecting individual plants within population allows positive genetic gain estimates for both characters. BLUP breeding values are of great importance to choose the number of populations and single plants to be conducted in a common bean breeding program by recurrent selection.

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Published
2018-12-18
How to Cite
de Paula, R. G., de Paula, I. G., Carneiro, A. L. N., Salvador, F. V., Carneiro, J. E. de S., & Careiro, P. C. S. (2018). Selecting common bean breeding populations via mixed models. Bioscience Journal, 35(2). https://doi.org/10.14393/BJ-v35n2a20198-41909
Section
Agricultural Sciences