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

Abelardo Montesinos-López, Osval A. Montesinos-López, José Crossa, Juan Burgueño, Kent M. Eskridge, Esteban Falconi-Castillo, Xingyao He, Pawan Singh, Karen Cichy
doi: https://doi.org/10.1101/034967
Abelardo Montesinos-López
1Departamento de Estadística, Centro de Investigación en Matemáticas (CIMAT), Guanajuato, Guanajuato, 36240, México.
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Osval A. Montesinos-López
2International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, México, D.F., México.
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José Crossa
2International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, México, D.F., México.
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  • For correspondence: j.crossa@cgiar.org
Juan Burgueño
2International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, México, D.F., México.
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Kent M. Eskridge
3University of Nebraska, Statistics Department, Lincoln, Nebraska, 68583-0963, USA.
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Esteban Falconi-Castillo
4Instituto Nacional Autónomo de Investigaciones Agropecuarias (INIAP), Panamericana Sur Km 1, Quito, Ecuador.
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Xingyao He
2International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, México, D.F., México.
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Pawan Singh
2International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, México, D.F., México.
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Karen Cichy
5Sugar beet and Bean Research Unit, USDA-ARS, East Lansing, MI, 48824, USA.
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Abstract

Genomic tools allow the study of the whole genome and are facilitating the study of genotype-environment combinations and their relationship with the 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 (n) 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 (n). 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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 20, 2015.
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Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction
Abelardo Montesinos-López, Osval A. Montesinos-López, José Crossa, Juan Burgueño, Kent M. Eskridge, Esteban Falconi-Castillo, Xingyao He, Pawan Singh, Karen Cichy
bioRxiv 034967; doi: https://doi.org/10.1101/034967
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Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction
Abelardo Montesinos-López, Osval A. Montesinos-López, José Crossa, Juan Burgueño, Kent M. Eskridge, Esteban Falconi-Castillo, Xingyao He, Pawan Singh, Karen Cichy
bioRxiv 034967; doi: https://doi.org/10.1101/034967

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