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Likelihood-Based Inference on Genetic Variance Component with a Hierarchical Poisson Generalized Linear Mixed Model

  • Lee, C. (Laboratory of Statistical Genetics, Institute of Environment & Life Science, Hallym University)
  • Received : 1999.07.13
  • Accepted : 2000.01.05
  • Published : 2000.08.01

Abstract

This study developed a Poisson generalized linear mixed model and a procedure to estimate genetic parameters for count traits. The method derived from a frequentist perspective was based on hierarchical likelihood, and the maximum adjusted profile hierarchical likelihood was employed to estimate dispersion parameters of genetic random effects. Current approach is a generalization of Henderson's method to non-normal data, and was applied to simulated data. Underestimation was observed in the genetic variance component estimates for the data simulated with large heritability by using the Poisson generalized linear mixed model and the corresponding maximum adjusted profile hierarchical likelihood. However, the current method fitted the data generated with small heritability better than those generated with large heritability.

Keywords

Count Variable;Dispersion Parameter;Maximum Adjusted Profile Hierarchical Likelihood