A Bayesian Genomic Regression Model with Skew Normal Random Errors

Publication Date:
2018-05-05
Publisher:
Genetics Society of America (GSA)
Electronic ISSN:
2160-1836
Topics:
Biology
Published by:
http://www.genetics-gsa.org/
_version_ 1836398918825410561
autor Perez-Rodriguez, P., Acosta-Pech, R., Perez-Elizalde, S., Cruz, C. V., Espinosa, J. S., Crossa, J.
beschreibung 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.
citation_standardnr 6251450
datenlieferant ipn_articles
feed_id 169615
feed_publisher Genetics Society of America (GSA)
feed_publisher_url http://www.genetics-gsa.org/
insertion_date 2018-05-05
journaleissn 2160-1836
publikationsjahr_anzeige 2018
publikationsjahr_facette 2018
publikationsjahr_intervall 7984:2015-2019
publikationsjahr_sort 2018
publisher Genetics Society of America (GSA)
quelle G3: Genes, Genomes, Genetics
relation http://www.g3journal.org/cgi/content/short/8/5/1771?rss=1
search_space articles
shingle_author_1 Perez-Rodriguez, P., Acosta-Pech, R., Perez-Elizalde, S., Cruz, C. V., Espinosa, J. S., Crossa, J.
shingle_author_2 Perez-Rodriguez, P., Acosta-Pech, R., Perez-Elizalde, S., Cruz, C. V., Espinosa, J. S., Crossa, J.
shingle_author_3 Perez-Rodriguez, P., Acosta-Pech, R., Perez-Elizalde, S., Cruz, C. V., Espinosa, J. S., Crossa, J.
shingle_author_4 Perez-Rodriguez, P., Acosta-Pech, R., Perez-Elizalde, S., Cruz, C. V., Espinosa, J. S., Crossa, J.
shingle_catch_all_1 A Bayesian Genomic Regression Model with Skew Normal Random Errors
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.
Perez-Rodriguez, P., Acosta-Pech, R., Perez-Elizalde, S., Cruz, C. V., Espinosa, J. S., Crossa, J.
Genetics Society of America (GSA)
2160-1836
21601836
shingle_catch_all_2 A Bayesian Genomic Regression Model with Skew Normal Random Errors
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.
Perez-Rodriguez, P., Acosta-Pech, R., Perez-Elizalde, S., Cruz, C. V., Espinosa, J. S., Crossa, J.
Genetics Society of America (GSA)
2160-1836
21601836
shingle_catch_all_3 A Bayesian Genomic Regression Model with Skew Normal Random Errors
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.
Perez-Rodriguez, P., Acosta-Pech, R., Perez-Elizalde, S., Cruz, C. V., Espinosa, J. S., Crossa, J.
Genetics Society of America (GSA)
2160-1836
21601836
shingle_catch_all_4 A Bayesian Genomic Regression Model with Skew Normal Random Errors
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.
Perez-Rodriguez, P., Acosta-Pech, R., Perez-Elizalde, S., Cruz, C. V., Espinosa, J. S., Crossa, J.
Genetics Society of America (GSA)
2160-1836
21601836
shingle_title_1 A Bayesian Genomic Regression Model with Skew Normal Random Errors
shingle_title_2 A Bayesian Genomic Regression Model with Skew Normal Random Errors
shingle_title_3 A Bayesian Genomic Regression Model with Skew Normal Random Errors
shingle_title_4 A Bayesian Genomic Regression Model with Skew Normal Random Errors
timestamp 2025-06-30T23:34:43.406Z
titel A Bayesian Genomic Regression Model with Skew Normal Random Errors
titel_suche A Bayesian Genomic Regression Model with Skew Normal Random Errors
topic W
uid ipn_articles_6251450