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This study introduces a novel random regression method for analyzing nematode infections in livestock. The new approach accurately estimates heritability of untransformed fecal egg counts, improving selective breeding programs.

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Area of Science:

  • Animal genetics
  • Quantitative genetics
  • Parasitology

Background:

  • Faecal egg counts (FEC) are key indicators of nematode infection and heritable traits for selective breeding.
  • Heritability of FEC changes with immune system development, necessitating dynamic analytical approaches.
  • Traditional log transformations of FEC can be statistically inappropriate and mask important biological information.

Purpose of the Study:

  • To develop and present a method for estimating the heritability of untransformed FEC over a grazing season using random regression.
  • To address limitations of traditional univariate and log-transformed analyses in FEC heritability estimation.
  • To implement a multivariate random regression model for correlated traits.

Main Methods:

  • Employed reduced rank random regression to handle computational challenges.
  • Modeled untransformed FEC data using a negative binomial distribution.
  • Utilized a Metropolis Hastings algorithm to fit a generalized reduced rank random regression model with additive genetic, permanent environmental, and maternal effects.

Main Results:

  • Demonstrated that heritability estimates are dependent on the chosen data transformation.
  • Identified temporal correlations, underscoring the need for random regression.
  • The proposed model, incorporating additive genetic, permanent environmental, and maternal effects, explained over 80% of phenotypic variation in untransformed FEC, significantly more than log-transformed data. Heritability increased from 0.25 to 0.40 during the grazing season.

Conclusions:

  • Random regression models effectively quantify temporal variation in FEC heritability.
  • The developed Markov chain Monte Carlo (MCMC) algorithm offers a flexible method for fitting random regression models to non-normal, overdispersed data like FEC.
  • This approach is adaptable for various overdispersed biological datasets.