Genomic bayesian prediction model for count data with genotype X environment interaction

dc.creatorMontesinos-López, A.
dc.creatorMontesinos-López, O.A.
dc.creatorBurgueño, J.
dc.creatorEskridge, K.
dc.creatorFalconi, E.E.
dc.creatorXinyao He
dc.creatorSingh, P.K.
dc.creatorCichy, K.
dc.creatorCrossa, J.
dc.date2017-07-04T15:14:31Z
dc.date2017-07-04T15:14:31Z
dc.date2016
dc.date.accessioned2023-07-17T20:01:19Z
dc.date.available2023-07-17T20:01:19Z
dc.identifierhttp://hdl.handle.net/10883/18637
dc.identifier10.1534/g3.116.028118
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7510837
dc.descriptionGenomic tools allow the study of the whole genome and are facilitating 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 is a viable alternative for analyzing count data.
dc.description1165-1177
dc.formatPDF
dc.languageEnglish
dc.publisherGenetics Society of America
dc.relationhttp://hdl.handle.net/11529/10575
dc.relationhttp://hdl.handle.net/11529/10575
dc.rightsCIMMYT 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.
dc.rightsOpen Access
dc.source5
dc.source6
dc.sourceG3: Genes, Genomes, Genetics
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectBAYESIAN THEORY
dc.subjectGENOMICS
dc.subjectGENOTYPE ENVIRONMENT INTERACTION
dc.titleGenomic bayesian prediction model for count data with genotype X environment interaction
dc.typeArticle
dc.coverageBethesda, MD


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