title: A Bayesian Poisson-lognormal Model for Count Data for Multiple-Trait Multiple-Environment Genomic-Enabled Prediction.
abstract: When a plant scientist wishes to make genomic-enabled predictions of multiple traits measured in multiple individuals in multiple environments, the most common strategy for performing the analysis is to use a single trait at a time taking into account genotype × environment interaction (G × E), because there is a lack of comprehensive models that simultaneously take into account the correlated counting traits and G × E. For this reason, in this study we propose a multiple-trait and multiple-environment model for count data. The proposed model was developed under the Bayesian paradigm for which we developed a Markov Chain Monte Carlo (MCMC) with noninformative priors. This allows obtaining all required full conditional distributions of the parameters leading to an exact Gibbs sampler for the posterior distribution. Our model was tested with simulated data and a real data set. Results show that the proposed multi-trait, multi-environment model is an attractive alternative for modeling multiple count traits measured in multiple environments.
authors.affiliation: Facultad de Telemática, Universidad de Colima, 28040, México.
authors.forename: Osval A
authors.initials: OA
authors.lastname: Montesinos-López
authors.affiliation: Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, 44430 Jalisco, México.
authors.forename: Abelardo
authors.initials: A
authors.lastname: Montesinos-López
authors.affiliation: International Maize and Wheat Improvement Center (CIMMYT), 06600 México, D.F., México j.crossa@cgiar.org.
authors.forename: José
authors.initials: J
authors.lastname: Crossa
authors.affiliation: International Maize and Wheat Improvement Center (CIMMYT), 06600 México, D.F., México.
authors.forename: Fernando H
authors.initials: FH
authors.lastname: Toledo
authors.affiliation: Departamento de Estadística, Centro de Investigación en Matemáticas (CIMAT), Guanajuato 36240, México.
authors.forename: José C
authors.initials: JC
authors.lastname: Montesinos-López
authors.affiliation: International Maize and Wheat Improvement Center (CIMMYT), 06600 México, D.F., México.
authors.forename: Pawan
authors.initials: P
authors.lastname: Singh
authors.affiliation: International Maize and Wheat Improvement Center (CIMMYT), 06600 México, D.F., México.
authors.forename: Philomin
authors.initials: P
authors.lastname: Juliana
authors.affiliation: Colegio de Postgraduados, Campus Córdoba, Km. 348 Carretera Federal Córdoba-Veracruz, Amatlán de los Reyes, 94946, México.
authors.forename: Josafhat
authors.initials: J
authors.lastname: Salinas-Ruiz
mesh_terms: D001499:Bayes Theorem; D059647:Gene-Environment Interaction; D008957:Models, Genetic; D000069600:Plant Breeding / Q000379:methods*; D016012:Poisson Distribution; D019655:Quantitative Trait, Heritable; D014908:Triticum / Q000235:genetics
keywords: Bayesian; GenPred; count phenotype; genomic selection; genomic-enabled prediction; multi-environment; multi-trait; shared data resource