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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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A hierarchical Bayesian model for a novel sparse partial diallel crossing design.

Anthony J Greenberg1, Sean R Hackett, Lawrence G Harshman

  • 1Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA. ajg67@cornell.edu

Genetics
|February 17, 2010
PubMed
Summary

We introduce an augmented round-robin design to maximize sampled lines in partial diallel crosses. This method accurately estimates quantitative genetic parameters like heritability and genetic correlations using a hierarchical Bayesian model.

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Area of Science:

  • Evolutionary genetics
  • Quantitative genetics
  • Breeding science

Background:

  • Partial diallel crossing designs are widely used in evolutionary genetics and breeding.
  • Maximizing the number of sampled lines is crucial for population-level inferences with a fixed number of crosses.

Purpose of the Study:

  • To propose an augmented round-robin design for partial diallel crosses.
  • To develop a hierarchical Bayesian model for estimating quantitative genetic parameters from this design.

Main Methods:

  • An augmented round-robin design was developed to increase line sampling efficiency.
  • A hierarchical Bayesian model was employed to estimate genetic parameters, including specific and general combining abilities.
  • The model handles complex and unbalanced designs, estimating heritability, dominance, and genetic correlations.

Main Results:

  • The proposed design effectively maximizes sampled lines within a set number of crosses.
  • The hierarchical Bayesian model accurately and precisely assesses main genetic effects.
  • Genetic variances were slightly overestimated, but key parameters were reliably estimated.

Conclusions:

  • The augmented round-robin design combined with a hierarchical Bayesian model offers an efficient approach for quantitative genetic studies.
  • This method enables robust estimation of heritability, dominance, and genetic correlations.
  • The approach facilitates the construction of posterior distributions for parameter combinations, including narrow-sense heritability.