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

• Published in: G3 : genes - genomes - genetics Vol. 6; no. 5; pp. 1165 - 1177
• 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: Journal Article
• Language: English
• Published: United States Genetics Society of America 01.05.2016; Oxford University Press
• Subjects: Algorithms; Bayes Theorem; Bayesian model; count data; Environment; Gene-Environment Interaction; Genetic Association Studies; genome enabled prediction; Genomic Selection; Genomics - methods; Genotype; GenPred; Gibbs sampler; Models, Genetic; Models, Statistical; Phenotype; shared data resource; Triticum - genetics; Gibbs sampler; genomic selection; Bayesian model; genome enabled prediction; count data; shared data resource; GenPred
• ISSN: 2160-1836; 2160-1836
• DOI: 10.1534/g3.116.028118

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 ([Formula: see text]) 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 ([Formula: see text]). Here, we propose a Bayesian mixed-negative binomial (BMNB) genomic regression model for counts that takes into account genotype by environment [Formula: see text] 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.