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Estimation of genetic parameters using sampled data from populations undergoing selection.

J Jensen1, I L Mao

  • 1Department of Animal Science, Michigan State University, East Lansing 48824-1225.

Journal of Dairy Science
|October 1, 1991
PubMed
Summary
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Estimating genetic parameters requires complete data; incomplete data or simplified models can bias results in selection populations. Including all relationships and data in animal models provides unbiased estimates for breeding value prediction.

Area of Science:

  • Animal breeding and genetics
  • Quantitative genetics
  • Statistical genetics

Background:

  • Selection alters genetic (co)variances, making accurate estimation of genetic parameters challenging.
  • Complete data and pedigree information are crucial for unbiased genetic parameter estimation but often unavailable.
  • Simplified models and data sampling are common due to computational limitations, potentially introducing bias.

Purpose of the Study:

  • To investigate biases in genetic parameter estimation under different data and pedigree sampling strategies.
  • To evaluate the impact of selection intensity and mating structure on genetic parameter estimates.
  • To identify optimal analytical approaches for accurate genetic evaluation in selected populations.

Main Methods:

  • Utilized the multitrait derivative-free REML (Restricted Maximum Likelihood) method.

Related Experiment Videos

  • Investigated various data and pedigree sampling strategies within animal models.
  • Compared results from animal models versus sire models in dual-purpose cattle and dairy populations.

    • Main Results:

    • Heritabilities and additive genetic correlations were unbiased in dual-purpose cattle when all data and relationships were used in an animal model.
    • Using recent data with all relationships in an animal model also yielded unbiased heritability estimates.
    • Simplified models (e.g., sire models) or incomplete data led to biased genetic parameter estimates, particularly for milk yield in dairy populations.

    Conclusions:

    • Accurate estimation of genetic parameters necessitates the inclusion of all available data and relationships.
    • Animal models incorporating full data and pedigree are essential for unbiased genetic evaluation in selected populations.
    • Simplified analytical approaches can introduce significant bias, compromising breeding value predictions.