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Optimizing design to estimate genetic correlations between environments with common environmental effects.

Maria Lozano-Jaramillo1, Hans Komen1, Yvonne C J Wientjes1

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Accurate genetic correlation estimation requires accounting for common environmental effects. Ignoring these effects causes bias, necessitating adjusted mating ratios in breeding programs for species like pigs and chickens.

Keywords:
breeding programsgenetic correlationgenotype by environment interactionpopulation structure

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Area of Science:

  • Animal breeding and genetics
  • Quantitative genetics
  • Genotype by environment interaction

Background:

  • Breeding programs test full-sib (FS) and half-sib (HS) families across environments to improve performance.
  • Genotype by environment (G × E) interaction occurs when genotypes respond differently to environmental changes.
  • Common environmental effects within families create covariance, impacting genetic correlation estimates.

Purpose of the Study:

  • To determine the optimal population structure for accurately estimating genetic correlation between environments.
  • To investigate the impact of common environmental effects on genetic correlation estimates.
  • To provide recommendations for breeding program designs, particularly for species with significant G × E.

Main Methods:

  • Stochastic simulation was employed to model different population structures (FS and HS groups).
  • Various levels of common environmental effects were simulated to assess their influence.
  • Mating ratios and offspring numbers per sire per environment were varied to find optimal designs.

Main Results:

  • Ignoring common environmental effects led to an average upward bias of 0.3 in genetic correlation when the true value was 0.5.
  • Without common environmental effects, the lowest standard error (SE) was achieved with 1 dam per sire and 10 offspring per sire per environment.
  • With common environmental effects included, the lowest SE required at least 5 dams per sire and 10 offspring per sire per environment.

Conclusions:

  • Common environmental effects significantly bias genetic correlation estimates if not accounted for.
  • Optimal mating ratios and family structures are crucial for accurate G × E interaction studies.
  • Recommendations are provided for optimizing breeding designs in pigs, chickens, and fish to manage G × E and common environmental effects.