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Simulation of marker assisted selection in hybrid populations

Published online by Cambridge University Press:  14 April 2009

A. Gimelfarb  and
R. Lande
A. Gimelfarb
Affiliation:
Department of Biology, University of Oregon, Eugene, Oregon 97403, USA
R. Lande*
Affiliation:
Department of Biology, University of Oregon, Eugene, Oregon 97403, USA
*
* Corresponding author.
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Summary

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A computer model is developed that simulates Marker Assisted Selection (MAS) in a population produced by a cross between two inbred lines. Selection is based on an index that incorporates both phenotypic and molecular information. Molecular markers contributing to the index and their relative weights are determined by multiple regression of individual phenotype on the markers. The model is applied to investigate the efficiency of MAS as affected by several factors including total number of markers in the genome, number of markers contributing to the index, population size and heritability of the character. It is demonstrated that selection based on genetic markers can effectively utilize the linkage disequilibrium between genetic markers and QTLs created by crossing inbred lines. Selection is more efficient if markers contributing to the index are re-evaluated each generation than if they are evaluated only once. Increasing the total number of markers in the genome as well as the number of markers contributing to the index does not necessarily result in a higher efficiency of selection. Moreover, too many markers may result in a weaker response to selection. Population size is shown to be the most important factor affecting the efficiency of MAS.

Type
Research Article
Information
Genetics Research , Volume 63 , Issue 1 , February 1994 , pp. 39 - 47
Copyright
Copyright © Cambridge University Press 1994

References

Draper, N. R. & Smith, H. (1981). Applied Regression Analysis. New York, Chichester, Toronto: Wiley.Google Scholar
Haley, C. S. & Knott, S. A. (1992). A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69, 315–324.CrossRefGoogle ScholarPubMed
Kennedy, B. W. & Sorensen, D. A. (1988). Properties of mixed-model methods for prediction of genetic merit. In Quantitative Genetics (ed. Weir, B. S., Eisen, E. J., Goodman, M. M. and Namkoong, G.), pp. 91–103. Massachusetts: Sinauer.Google Scholar
Lande, R. (1981). The minimum number of genes contributing to quantitative variation between and within populations. Genetics 99, 541–553.CrossRefGoogle ScholarPubMed
Lande, R. (1992). Marker-assisted selection in relation to traditional methods of plant breeding. In Plant Breeding in the 1990s (ed. Stalker, H. T. and Murphy, J. P.), pp. 437–451. Wallingford: CAB International.Google Scholar
Lande, R. & Thompson, R. (1990). Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124, 743–756.Google Scholar
Lander, E. S. & Botstein, D. (1988). Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121, 185–199.CrossRefGoogle Scholar
Paterson, A. H., Lander, E. S., Hewitt, J. D., Peterson, S., Lincoln, S. E. & Tanksley, S. D. (1988). Resolution of quantitative traits into Mendelian factors using a complete linkage map of restriction fragment length polymorphisms. Nature 335, 721–726.CrossRefGoogle ScholarPubMed
Paterson, A. H., DeVerna, J. W., Lanini, B. & Tanksley, S. D. (1990). Fine mapping of quantitative trait loci using selected overlapping recombinant chromosomes in an interspecies cross of tomato. Genetics 124, 735–742.Google Scholar
Zhang, W. & Smith, C. (1992). Computer simulation of marker-assisted selection utilizing linkage disequilibrium. Theoretical and Applied Genetics 83, 813–820.CrossRefGoogle ScholarPubMed