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Area of Science:

  • Quantitative genetics
  • Animal breeding

Background:

  • Recursive models propose causal relationships between traits.
  • Statistical identification in recursive models requires parameter space restrictions.
  • Likelihood equivalence exists between recursive and multiple trait models under certain conditions.

Purpose of the Study:

  • To demonstrate the utility of LDL' or block-LDL' transformations for variance component estimation.
  • To simplify inference in recursive models, especially with missing data.

Main Methods:

  • Applied LDL' or block-LDL' transformations to variance component estimates from a multiple-trait mixed model.
  • Utilized a Bayesian approach with a Gibbs sampler for variance component estimation.
  • Tested the procedure on a large dataset of Pirenaica beef cattle traits.

Main Results:

  • Variance components from a multiple-trait mixed model were successfully converted to estimates under recursive models.
  • The transformations facilitated inference across multiple recursive models.
  • The method proved effective even with large datasets and missing phenotypic records.

Conclusions:

  • LDL' or block-LDL' transformations enable inference across multiple likelihood-equivalent recursive models.
  • These transformations offer a method to handle missing data in recursive model analyses.
  • The approach provides a flexible framework for genetic analyses using recursive models.