Statistics, Department of
ORCID IDs
Document Type
Article
Date of this Version
2016
Citation
Genes Genomes Genetics, Volume 6, May 2016
doi: 10.1534/g3.116.028118
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 (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 x 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 provides a viable option for analyzing count data.
Comments
Copyright © 2016 Montesinos-López et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License