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Model selection in binary trait locus mapping.

Cynthia J Coffman1, R W Doerge, Katy L Simonsen

  • 1Institute for Clinical and Epidemiological Research Biostatistics Unit, Durham VA Medical Center (152), Durham, North Carolina 27705, USA.

Genetics
|April 19, 2005
PubMed
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This study introduces a new probability model for binary trait locus (BTL) mapping. The method effectively detects multiple genetic loci, including epistatic interactions, with high power in experimental populations.

Area of Science:

  • Genetics and Genomics
  • Statistical Genetics
  • Quantitative Genetics

Background:

  • Quantitative trait locus (QTL) mapping is well-established for continuous traits.
  • Binary trait locus (BTL) mapping in experimental populations is less explored.
  • BTL mapping faces challenges due to restricted penetrance parameters and genetic model selection.

Purpose of the Study:

  • To present a novel probability model for BTL mapping with an arbitrary number of loci.
  • To demonstrate high power for detecting genetic loci, including epistatic interactions, under various genetic models.
  • To introduce a new model selection strategy for efficient BTL mapping.

Main Methods:

  • Development of a probability model for binary traits.
  • Simulation studies to assess power and performance across diverse genetic models.

Related Experiment Videos

  • A novel model selection strategy based on selecting the best marker from each linkage group.
  • Reanalysis of existing experimental data for Oncorhynchus mykiss.

    • Main Results:

    • The proposed model demonstrates high power for detecting genetic loci, even with epistatic interactions, given adequate sample sizes.
    • The novel model selection strategy efficiently reduces the model space for searching epistatic loci.
    • The procedure successfully identifies unlinked epistatic BTLs, confirmed by simulations and data reanalysis.

    Conclusions:

    • The developed probability model and selection strategy offer a powerful approach for BTL mapping.
    • This methodology enhances the ability to identify complex genetic architectures underlying binary traits.
    • The findings have implications for genetic studies in experimental populations, particularly for traits with discrete outcomes.