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On flexible finite polygenic models for multiple-trait evaluation.

Marco C A M Bink1

  • 1Biometris - Wageningen University and Research Centre, PO Box 100, 6700 AC Wageningen, The Netherlands. m.c.a.m.bink@plant.wag-ur.nl

Genetical Research
|April 12, 2003
PubMed
Summary
This summary is machine-generated.

Finite polygenic models offer an alternative for genetic evaluation. Combining finite and infinitesimal models with a random number of genes provides a robust method for variance component estimation in populations.

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Area of Science:

  • Quantitative genetics
  • Statistical genetics
  • Genomic evaluation

Background:

  • The infinitesimal model (TIM) is standard for genetic evaluation in populations.
  • Finite polygenic models (FPM) offer a potential alternative, especially for complex traits.
  • Evaluating the performance of FPM versus TIM is crucial for improving genetic prediction.

Purpose of the Study:

  • To develop and test a flexible Bayesian method for FPM that treats the number of genes as a random variable.
  • To compare FPM with TIM and combined models for variance component estimation.
  • To investigate the impact of prior distributions and selection strategies on model performance.

Main Methods:

  • Developed a Bayesian approach incorporating the number of genes as a random variable within FPM.
  • Utilized Markov-chain Monte Carlo (MCMC) techniques, including Gibbs sampling and reversible jump sampler.
  • Employed segregation indicators for sampling gene genotypes and tested various FPM and combined TIM-FPM models.

Main Results:

  • Positive correlation observed between genetic variance estimates and the number of genes in FPM under random selection.
  • Negative correlation found between genetic variance estimates and the number of genes for the selected trait under trait selection.
  • Prior distributions had minimal impact on posterior genetic variance estimates, but trait selection improved model distinction.

Conclusions:

  • Suggest combining TIM and FPM with a random number of genes for robust variance component estimation in pedigreed populations.
  • FPM alone may not replace TIM, but integration offers flexibility.
  • Further research is needed to explore the full potential of these combined models under diverse genetic assumptions.