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  1. Vol. 40 No. 1, p. 91-98
     
    Received: Nov 30, 1998
    Published: Jan, 2000


    * Corresponding author(s): mhreyes@uaaan.mx
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doi:10.2135/cropsci2000.40191x

A Model for Marker-Based Selection in Gene Introgression Breeding Programs

  1. M. Humberto Reyes-Valdés *a
  1.  aUniversidad Autónoma Agraria Antonio Narro, Departamento de Fitomejoramiento. Buenavista, Saltillo, Coah. C.P. 25315. Mexico

Abstract

Marker-based breeding can be useful to expedite introgression of specific genetic material from a donor parent into the background of an elite variety, through backcrossing. A model is proposed to predict the probability of donor parent genetic material being present in specific regions of the genome, and its proportion at the chromosome-specific or whole genome levels, as a result of marker-based introgression. Furthermore, formulas are provided to calculate the variance of the predicted values. Two kinds of markers are considered: donor parent specific and recurrent parent specific. The first type serves to introgress the desired fraction of donor genome, and the second one to recover the recurrent parent background genome. In all cases, the probabilities and genomic proportions are calculated on a genetic map basis. This model permits any localization of markers through the genome, but requires knowledge of their map positions and the map lengths of the chromosomes. It is robust to mapping functions, and admits any one based on the assumption of coincidence being equal to the kth power of twice the recombination fraction. Two widely used mapping functions gave fairly different predictions of global chromosome introgression. Monte Carlo simulations for several circumstances allowed the testing of the model, and no significant statistical deviations from the theoretical predictions were found. The results indicate that the formulas presented herein can be useful for planning and prediction in a backcross breeding program.

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Copyright © 2000. Crop Science Society of AmericaCrop Science Society of America