Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction

Genomic tools allow the study of the whole genome, and facilitate the study of genotype-environment combinations and their relationship with phenotype. However, most genomic prediction models developed so far are appropriate for Gaussian phenotypes. For this reason, appropriate genomic prediction mo...

Full description

Bibliographic Details
Main Authors:	Montesinos-López, Abelardo, Montesinos-López, Osval A., Crossa, José, Burgueño, Juan, Eskridge, Kent M., Falconi-Castillo, Esteban, He, Xinyao, Singh, Pawan, Cichy, Karen
Format:	Online
Language:	English
Published:	Genetics Society of America 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4856070/

id	pubmed-4856070
recordtype	oai_dc
spelling	pubmed-48560702016-05-05 Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction Montesinos-López, Abelardo Montesinos-López, Osval A. Crossa, José Burgueño, Juan Eskridge, Kent M. Falconi-Castillo, Esteban He, Xinyao Singh, Pawan Cichy, Karen Genomic Selection Genomic tools allow the study of the whole genome, and facilitate the study of genotype-environment combinations and their relationship with phenotype. However, most genomic prediction models developed so far are appropriate for Gaussian phenotypes. For this reason, appropriate genomic prediction models are needed for count data, since the conventional regression models used on count data with a large sample size (nT) and a small number of parameters (p) cannot be used for genomic-enabled prediction where the number of parameters (p) is larger than the sample size (nT). Here, we propose a Bayesian mixed-negative binomial (BMNB) genomic regression model for counts that takes into account genotype by environment (G×E) interaction. We also provide all the full conditional distributions to implement a Gibbs sampler. We evaluated the proposed model using a simulated data set, and a real wheat data set from the International Maize and Wheat Improvement Center (CIMMYT) and collaborators. Results indicate that our BMNB model provides a viable option for analyzing count data. Genetics Society of America 2016-02-25 /pmc/articles/PMC4856070/ /pubmed/26921298 http://dx.doi.org/10.1534/g3.116.028118 Text en Copyright © 2016 Montesinos-López et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Montesinos-López, Abelardo Montesinos-López, Osval A. Crossa, José Burgueño, Juan Eskridge, Kent M. Falconi-Castillo, Esteban He, Xinyao Singh, Pawan Cichy, Karen
spellingShingle	Montesinos-López, Abelardo Montesinos-López, Osval A. Crossa, José Burgueño, Juan Eskridge, Kent M. Falconi-Castillo, Esteban He, Xinyao Singh, Pawan Cichy, Karen Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction
author_facet	Montesinos-López, Abelardo Montesinos-López, Osval A. Crossa, José Burgueño, Juan Eskridge, Kent M. Falconi-Castillo, Esteban He, Xinyao Singh, Pawan Cichy, Karen
author_sort	Montesinos-López, Abelardo
title	Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction
title_short	Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction
title_full	Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction
title_fullStr	Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction
title_full_unstemmed	Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction
title_sort	genomic bayesian prediction model for count data with genotype × environment interaction
description	Genomic tools allow the study of the whole genome, and facilitate the study of genotype-environment combinations and their relationship with phenotype. However, most genomic prediction models developed so far are appropriate for Gaussian phenotypes. For this reason, appropriate genomic prediction models are needed for count data, since the conventional regression models used on count data with a large sample size (nT) and a small number of parameters (p) cannot be used for genomic-enabled prediction where the number of parameters (p) is larger than the sample size (nT). Here, we propose a Bayesian mixed-negative binomial (BMNB) genomic regression model for counts that takes into account genotype by environment (G×E) interaction. We also provide all the full conditional distributions to implement a Gibbs sampler. We evaluated the proposed model using a simulated data set, and a real wheat data set from the International Maize and Wheat Improvement Center (CIMMYT) and collaborators. Results indicate that our BMNB model provides a viable option for analyzing count data.
publisher	Genetics Society of America
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4856070/
_version_	1613575199568953344

Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction

Similar Items