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Related Experiment Videos

Likelihood estimation of quantitative genetic parameters when selection occurs: models and problems.

D Gianola1, R L Fernando, S Im

  • 1Department of Animal Sciences, University of Illinois, Urbana 61801.

Genome
|January 1, 1989
PubMed
Summary
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This study explores estimating genetic variance and covariance under selection using likelihood methods. Findings suggest some selection types do not impact point inferences from likelihood functions, aiding genetic analysis.

Area of Science:

  • Quantitative genetics
  • Statistical genetics

Background:

  • Estimating genetic parameters like variance and covariance is crucial for understanding trait inheritance.
  • Selection processes can complicate the accurate estimation of these genetic components.
  • Likelihood methods offer a powerful statistical framework for genetic analysis.

Purpose of the Study:

  • To discuss conceptual aspects of estimating genetic variance and covariance under selection.
  • To describe specific selection processes and alternative likelihoods for analysis.
  • To investigate the impact of selection on likelihood-based inferences.

Main Methods:

  • Review of conceptual frameworks for genetic parameter estimation.
  • Description of various selection processes.

Related Experiment Videos

  • Specification of alternative likelihood functions for data analysis.
  • Mathematical comparison of information content in different likelihoods.

    • Main Results:

    • Identified mathematical relationships between alternative likelihoods.
    • Demonstrated that some selection processes do not bias point inferences from likelihood functions.
    • Provided theoretical arguments and empirical evidence supporting the robustness of likelihood inferences.

    Conclusions:

    • Likelihood methods provide a robust approach for estimating genetic components even under certain selection pressures.
    • Understanding the interplay between selection and likelihood is key to accurate genetic inference.
    • Point inferences from likelihood functions can be reliable despite some forms of selection.