A Bayesian Genomic Regression Model with Skew Normal Random Errors. Issue 5 (1st May 2018)

• Record Type: Journal Article
• Authors: Pérez-Rodríguez, Paulino; Acosta-Pech, Rocío; Pérez-Elizalde, Sergio; Cruz, Ciro Velasco; Espinosa, Javier Suárez; Crossa, José
• Is Part Of: G3. Volume 8:Issue 5(2018)
• Page Start: 1771
• Page End: 1785
• Publication Date: 2018-05-01
• Subjects: Genomic Selection -- data augmentation -- asymmetric distributions -- GBLUP -- Ridge regression -- GenPred -- Shared Data Resources; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1534/g3.117.300406
• Languages: English
• ISSNs: 2160-1836

Abstract

Abstract: Genomic selection (GS) has become a tool for selecting candidates in plant and animal breeding programs. In the case of quantitative traits, it is common to assume that the distribution of the response variable can be approximated by a normal distribution. However, it is known that the selection process leads to skewed distributions. There is vast statistical literature on skewed distributions, but the skew normal distribution is of particular interest in this research. This distribution includes a third parameter that drives the skewness, so that it generalizes the normal distribution. We propose an extension of the Bayesian whole-genome regression to skew normal distribution data in the context of GS applications, where usually the number of predictors vastly exceeds the sample size. However, it can also be applied when the number of predictors is smaller than the sample size. We used a stochastic representation of a skew normal random variable, which allows the implementation of standard Markov Chain Monte Carlo (MCMC) techniques to efficiently fit the proposed model. The predictive ability and goodness of fit of the proposed model were evaluated using simulated and real data, and the results were compared to those obtained by the Bayesian Ridge Regression model. Results indicate that the proposed model has a better fit and is as good as the conventional Bayesian Ridge Regression model for prediction, based on the DIC criterion and cross-validation, respectively. A computing program coded in the R statistical package and C programming language to fit the proposed model is available as supplementary material.