Genomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression.
Most genomic-enabled prediction models developed so far assume that the response variable is continuous and normally distributed. The exception is the probit model, developed for ordered categorical phenotypes. In statistical applications, because of the easy implementation of the Bayesian probit ordinal regression (BPOR) model, Bayesian logistic ordinal regression (BLOR) is implemented rarely in the context of genomic-enabled prediction [sample size (n) is much smaller than the number of parameters (p)]. For this reason, in this paper we propose a BLOR model using the Pólya-Gamma data augmentation approach that produces a Gibbs sampler with similar full conditional distributions of the BPOR model and with the advantage that the BPOR model is a particular case of the BLOR model. We evaluated the proposed model by using simulation and two real data sets. Results indicate that our BLOR model is a good alternative for analyzing ordinal data in the context of genomic-enabled prediction with the probit or logit link.
Facultad de Telemática, Universidad de Colima, C.P. 28040 Colima, Colima, México.
Osval A
OA
Montesinos-López
Departamento de Estadística, Centro de Investigación en Matemáticas (CIMAT), 36240 Guanajuato, México.
Abelardo
A
Montesinos-López
Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), 06600 México, Distrito Federal, México j.crossa@cgiar.org.
José
http://orcid.org/0000-0001-9429-5855
J
Crossa
Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), 06600 México, Distrito Federal, México.
Juan
J
Burgueño
University of Nebraska, Statistics Department, Lincoln, Nebraska 68583-0963.
Kent
K
Eskridge
D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D066264:Datasets as Topic; D023281:Genomics / Q000379:methods*; D016015:Logistic Models; D008957:Models, Genetic; D010935:Plant Diseases / Q000235:genetics; D014908:Triticum / Q000235:genetics; D003313:Zea mays / Q000235:genetics
Bayesian ordinal regression; GenPred; Gibbs sampler; genomic selection; logit; probit; shared data resource