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Area of Science:

  • Quantitative genetics
  • Statistical genomics
  • Animal and plant breeding

Background:

  • Genomic selection (GS) is crucial for breeding programs, often assuming normal distributions for quantitative traits.
  • Selection processes can introduce skewness into trait distributions, challenging standard models.
  • The skew normal distribution offers a flexible alternative by incorporating a skewness parameter.

Purpose of the Study:

  • To extend Bayesian whole-genome regression models to accommodate skew normal distributions in genomic selection.
  • To evaluate the performance of the proposed model for both simulated and real breeding data.
  • To compare the proposed model against the conventional Bayesian Ridge Regression.

Main Methods:

  • Developed a Bayesian whole-genome regression model incorporating skew normal distributions.
  • Utilized a stochastic representation of skew normal variables for efficient Markov Chain Monte Carlo (MCMC) fitting.
  • Assessed model fit using the Deviance Information Criterion (DIC) and predictive ability via cross-validation.

Main Results:

  • The proposed skew normal model demonstrated superior goodness of fit compared to Bayesian Ridge Regression.
  • The skew normal model exhibited comparable predictive ability to Bayesian Ridge Regression.
  • A computational program in R and C is available for implementing the proposed methodology.

Conclusions:

  • Bayesian whole-genome regression extended to skew normal distributions provides a robust framework for genomic selection.
  • This approach effectively handles skewed trait distributions common in breeding populations.
  • The model offers improved fit without compromising predictive performance, enhancing selection efficiency.