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Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
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Box-Cox Transformation and Random Regression Models for Fecal egg Count Data.

Marcos Vinícius Gualberto Barbosa da Silva1, Curtis P Van Tassell, Tad S Sonstegard

  • 1Bioinformatics and Animal Genomics Laboratory, Embrapa Dairy Cattle, Juiz de Fora Minas Gerais, Brazil.

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Summary

Accurate genetic evaluation of livestock requires appropriate modeling of fecal egg count (FEC) data. Random regression models and Box-Cox transformations effectively analyze skewed FEC measurements, improving heritability estimates in Angus cattle.

Keywords:
Box–Cox transformationREMLbovinefecal egg countgenetic parameters

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Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Parasitology

Background:

  • Accurate genetic evaluation in livestock relies on precise phenotypic measurements.
  • Fecal egg count (FEC) is a key indicator of nematode resistance in ruminants.
  • Standard logarithmic transformations often fail to normalize highly skewed FEC data.

Purpose of the Study:

  • To improve genetic evaluation models for nematode resistance in livestock.
  • To assess the effectiveness of Box-Cox transformations for FEC data normalization.
  • To apply random regression models (RRM) for analyzing repeated FEC measurements.

Main Methods:

  • Utilized 6375 FEC measurements from 410 Angus cattle (1992-2003).
  • Applied an extended Box-Cox transformation to normalize FEC data.
  • Employed random regression models (RRM) with Legendre polynomials (order 4) and restricted maximum likelihood (REML) for analysis.

Main Results:

  • Box-Cox transformation effectively reduced skewness and kurtosis in FEC data.
  • RRM with higher-order Legendre polynomials provided the best data fit.
  • Heritability estimates for FEC were significantly increased post-transformation.
  • FEC measurements between 12 and 26 weeks showed significant genetic correlations.

Conclusions:

  • Box-Cox transformation and RRM are effective tools for analyzing non-normally distributed FEC data.
  • Improved FEC data analysis enhances genetic parameter estimation for nematode resistance.
  • Genetic correlations in FEC suggest potential for selection against nematodes in Angus cattle.