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This article in CS

  1. Vol. 46 No. 3, p. 1323-1330
     
    Received: Sept 7, 2005
    Published: May, 2006


    * Corresponding author(s): mes12@cornell.edu
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doi:10.2135/cropsci2005.09-0305

Association Analysis as a Strategy for Improvement of Quantitative Traits in Plants

  1. Flavio Breseghelloa and
  2. Mark E. Sorrells *b
  1. a Embrapa Rice and Beans, Santo Antônio de Goiás, GO, Brazil, 75375
    b Dep. of Plant Breeding, Cornell University, 252 Emerson Hall, Ithaca, NY, 14853-1902

Abstract

Association analysis is a method potentially useful for detection of marker-trait associations based on linkage disequilibrium, but little information is available on the application of this technique to plant breeding populations. With appropriate statistical methods, valid association analysis can be done in plant breeding populations; however, the most significant marker may not be closest to the functional gene. Bias can arise from (i) covariance among markers and QTL, frequently related to population structure or intense selection and (ii) differences in initial frequencies of marker alleles in the population, such that exclusive alleles tend to be in higher association. The potentials and limitations of germplasm bank collections, synthetic populations, and elite germplasm are compared, as experimental materials for association analysis integrated with plant breeding practice. Synthetics offer a favorable balance of power and precision for association analysis and would allow mapping of quantitative traits with increasing resolution through cycles of intermating. A model to describe the association between markers and genes as conditional probabilities in synthetic populations under recurrent selection is proposed, which can be computed on the basis of assumptions related to the history of the population. This model is useful for predicting the potential of different populations for association analysis and forecasting the response to marker-assisted selection.

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Copyright © 2006. Crop Science Society of AmericaCrop Science Society of America