title: Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction.
abstract: 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.
authors.affiliation: Departamento de Estadística, Centro de Investigación en Matemáticas (CIMAT), Guanajuato, 36240, Mexico.
authors.forename: Abelardo
authors.initials: A
authors.lastname: Montesinos-López
authors.affiliation: International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, México, D.F., Mexico.
authors.forename: Osval A
authors.initials: OA
authors.lastname: Montesinos-López
authors.affiliation: International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, México, D.F., Mexico j.crossa@cgiar.org.
authors.forename: José
authors.initials: J
authors.lastname: Crossa
authors.affiliation: International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, México, D.F., Mexico.
authors.forename: Juan
authors.initials: J
authors.lastname: Burgueño
authors.affiliation: Department of Statistics, University of Nebraska, Lincoln, Nebraska 68583-0963.
authors.forename: Kent M
authors.initials: KM
authors.lastname: Eskridge
authors.affiliation: Instituto Nacional Autónomo de Investigaciones Agropecuarias (INIAP), Panamericana Sur Km 1, Quito, Ecuador.
authors.forename: Esteban
authors.initials: E
authors.lastname: Falconi-Castillo
authors.affiliation: International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, México, D.F., Mexico.
authors.forename: Xinyao
authors.initials: X
authors.lastname: He
authors.affiliation: International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, México, D.F., Mexico.
authors.forename: Pawan
authors.initials: P
authors.lastname: Singh
authors.affiliation: Sugarbeet and Bean Research Unit, USDA-ARS, East Lansing, Michigan 48824.
authors.forename: Karen
authors.initials: K
authors.lastname: Cichy
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D004777:Environment; D059647:Gene-Environment Interaction; D056726:Genetic Association Studies; D023281:Genomics / Q000379:methods; D005838:Genotype; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D014908:Triticum / Q000235:genetics
keywords: Bayesian model; GenPred; Gibbs sampler; count data; genome enabled prediction; genomic selection; shared data resource