Molecular Markers, Forest Genetics, and Tree Breeding

  • D. M. O’Malley
  • D. Grattapaglia
  • J. X. Chaparro
  • P. L. Wilcox
  • H. V. Amerson
  • B.-H. Liu
  • R. Whetten
  • S. McKeand
  • E. G. Kuhlman
  • S. McCord
  • B. Crane
  • R. Sederoff
Part of the Stadler Genetics Symposia Series book series (SGSS)

Abstract

Several years ago, Strauss et al. (1992) thoughtfully evaluated the application of molecular markers in forest tree breeding for marker aided selection. The purpose of their paper was to emphasize the limitations and shortcomings of marker-aided selection particularly in conifers. They argued that studies of quantitative trait loci identified in agronomic crops, which have significant utility (e.g. Stuber, 1992; Stuber et al., 1992), are of little relevance to assessing the potential for marker aided selection in populations of forest trees, and that the near term usefulness of molecular markers for forest tree breeding will be limited. The major barriers to application included cost, the lack of association of markers with traits across breeding populations due to linkage equilibrium, variation in expression of loci affecting quantitative traits due to differences in genetic background, genotype environment interactions, and stability of marker-trait associations over multiple generations. In addition, Strauss et al. (1992) noted that marker-aided selection would be most useful for within family selection, where the economic values of the traits are high, the trait heritabilities are low, and where markers are able to explain much of the genetic variance. However, they argued that important traits in forest trees such as wood volume, are likely to be controlled by large numbers of genes with small effects, and therefore, are unlikely to have useful marker trait associations.

Keywords

Quantitative Trait Locus Forest Tree RAPD Marker Linkage Equilibrium Tree Breeding 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • D. M. O’Malley
    • 1
    • 2
  • D. Grattapaglia
  • J. X. Chaparro
    • 1
    • 4
  • P. L. Wilcox
    • 1
    • 2
  • H. V. Amerson
    • 1
    • 2
  • B.-H. Liu
    • 1
    • 5
  • R. Whetten
    • 1
    • 2
  • S. McKeand
    • 2
  • E. G. Kuhlman
    • 6
  • S. McCord
    • 1
    • 2
  • B. Crane
    • 1
    • 2
  • R. Sederoff
    • 1
    • 2
    • 3
  1. 1.Forest Biotechnology GroupNorth Carolina State UniversityRaleighUSA
  2. 2.Department of ForestryNorth Carolina State UniversityRaleighUSA
  3. 3.Department of GeneticsNorth Carolina State UniversityRaleighUSA
  4. 4.Department of HorticultureNorth Carolina State UniversityRaleighUSA
  5. 5.Department of StatisticsNorth Carolina State UniversityRaleighUSA
  6. 6.U.S.D.A. Forest Service LaboratoriesAthensGreece

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