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.
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Language: | English |
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Genetics Society of America
2018
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Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, Genomic Selection, Data Augmentation, Assymetric Distributions, GBLUP, Ridge Regression, GenPred, Shared Data Resources, BAYESIAN THEORY, REGRESSION ANALYSIS, STATISTICAL METHODS, |
Online Access: | https://hdl.handle.net/10883/19493 |
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dig-cimmyt-10883-194932023-12-08T15:08:53Z A bayesian genomic regression model with skew normal random errors Perez-Rodriguez, P. Acosta-Pech, R. Pérez-Elizalde, S. Velasco Cruz, C. Suarez Espinosa, J. Crossa, J. AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Selection Data Augmentation Assymetric Distributions GBLUP Ridge Regression GenPred Shared Data Resources BAYESIAN THEORY REGRESSION ANALYSIS STATISTICAL METHODS 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. 1771-1785 2018-05-29T21:04:04Z 2018-05-29T21:04:04Z 2018 Article 2160-1836 (Online) https://hdl.handle.net/10883/19493 10.1534/g3.117.300406 English https://www.g3journal.org/highwire/filestream/489330/field_highwire_adjunct_files/0/FileS1.zip CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose. Open Access PDF Bethesda, Maryland, U.S. Genetics Society of America 5 8 G3: Genes, Genomes, Genetics |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Selection Data Augmentation Assymetric Distributions GBLUP Ridge Regression GenPred Shared Data Resources BAYESIAN THEORY REGRESSION ANALYSIS STATISTICAL METHODS AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Selection Data Augmentation Assymetric Distributions GBLUP Ridge Regression GenPred Shared Data Resources BAYESIAN THEORY REGRESSION ANALYSIS STATISTICAL METHODS |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Selection Data Augmentation Assymetric Distributions GBLUP Ridge Regression GenPred Shared Data Resources BAYESIAN THEORY REGRESSION ANALYSIS STATISTICAL METHODS AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Selection Data Augmentation Assymetric Distributions GBLUP Ridge Regression GenPred Shared Data Resources BAYESIAN THEORY REGRESSION ANALYSIS STATISTICAL METHODS Perez-Rodriguez, P. Acosta-Pech, R. Pérez-Elizalde, S. Velasco Cruz, C. Suarez Espinosa, J. Crossa, J. A bayesian genomic regression model with skew normal random errors |
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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. |
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Article |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Selection Data Augmentation Assymetric Distributions GBLUP Ridge Regression GenPred Shared Data Resources BAYESIAN THEORY REGRESSION ANALYSIS STATISTICAL METHODS |
author |
Perez-Rodriguez, P. Acosta-Pech, R. Pérez-Elizalde, S. Velasco Cruz, C. Suarez Espinosa, J. Crossa, J. |
author_facet |
Perez-Rodriguez, P. Acosta-Pech, R. Pérez-Elizalde, S. Velasco Cruz, C. Suarez Espinosa, J. Crossa, J. |
author_sort |
Perez-Rodriguez, P. |
title |
A bayesian genomic regression model with skew normal random errors |
title_short |
A bayesian genomic regression model with skew normal random errors |
title_full |
A bayesian genomic regression model with skew normal random errors |
title_fullStr |
A bayesian genomic regression model with skew normal random errors |
title_full_unstemmed |
A bayesian genomic regression model with skew normal random errors |
title_sort |
bayesian genomic regression model with skew normal random errors |
publisher |
Genetics Society of America |
publishDate |
2018 |
url |
https://hdl.handle.net/10883/19493 |
work_keys_str_mv |
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