016 MTM (Multiple-Trait Model) package. Offered at: http://quantgen.github.io/MTM

Springer-Verlag, New York. L ez-Cruz, M. A., J. Crossa, D. Bonnet, S. Dreisigacker, J. Poland et al., 2015 Improved prediction accuracy in wheat breeding trials employing a marker ?atmosphere interaction genomic choice model. G3 (Bethesda) 5(4):569?82. Meuwissen, T. H. E., B. J. Hayes, and M. E. Goddard, 2001 Prediction of total genetic value applying genome-wide dense marker maps. Genetics 157: 1819?829. P ez-Elizalde, S., J. Cuevas, P. P ez- Rodr uez, and J. Crossa, 2015 Selection of the bandwidth parameter in a Bayesian Kernel regression model for genomic-enabled prediction. title= journal.pone.0054688 J. Agric. Biol. Environ. Stat. 5(four): 512?32. P ez-Rodr uez, P., D. Gianola, J. M. Gonz ez-Camacho, J. Crossa, Y. Manes et al., 2012 A comparison amongst linear and non-parametric regression models for genome-enabled prediction in wheat. G3 (Bethesda) 2: 1595?605. P ez-Rodr uez, P., and G. de los Campos, 2014 Genome-wide regression and prediction together with the BGLR statistical package. Genetics 198: 483?95. Piepho, H. P., 1998 Empirical very best linear unbiased prediction in cultivar trials using issue analytic variance covariance structure. Theor. Appl. Genet. 97: 195?01. Smith, A. B., B. R. Cullis, and R. Thompson. 2001 Exploring varietyenvironment data making use of random effects AMMI models with adjustments for spatial field trend: aspect 1: Necrostatin-1 theory, pp. 323?35 in Quantitative Genetics, Genomics and Plant Breeding, edited by Kang, M. S.. CABI Publishing, Wallingford. Smith, A. B., B. R. Cullis, and R. Thompson, 2005 The analysis of crop cultivar breeding and evaluation trials: an overview of present mixed model approaches. J. Agric. Sci. 143: 449?62. VanRaden, P. M., 2007 Genomic measures of connection and inbreeding. Interbull Bull 37: 33?six. VanRaden, P. M., 2008 Efficient approaches to compute genomic predictions. J. Dairy Sci. 91: 4414?423. Yates, F., and W. G. Cochran, 1938 The analysis of groups of experiments. J. Agric. Sci. 28: 556?80.Communicating editor: D. J. de Koning|J. Cuevas et al.APPENDIX A ### Example for fitting multi-environment title= ajim.22419 model (three) ### Call for MTM package (de los NG25 Campos and Gr eberg, 2016) ##### Require BGLR package (de los Campos and P ez-Rodr uez, 2014) ##### Requiere function CV2 (L ez-Cruz et al., 2015) ###Data library(BGLR) library(MCMCpack) information(wheat) X,-wheat.X Y,-wheat.Y X,-scale(X,center=TRUE,scale=TRUE) Y,-scale(Y,center=TRUE,scale=TRUE) env,-c(1,two,three,4); y,-Y[,env] G,-X ?t(X)/ncol(X) In,-diag(1,nrow(Y),nrow(Y)) set.seed(12345) ##Simulation of missing data CV2, see L ez-Cruz et al.016 MTM (Multiple-Trait Model) package. Readily available title= AEM.01433-15 at: http://quantgen.github.io/MTM/vignette.html. Accessed May 3rd, 2016. de los Campos, G., D. Gianola, G. J. M. Rosa, K. Weigel, and J. Crossa, 2010 Semi-parametric genomic-enabled prediction of genetic values employing reproducing kernel Hilbert spaces approaches. Genet. Res. 92: 295?308. Fisher, R. A., and W. A. Mackensie, 1923 Research in crop variation II. The manurial response of unique potato varieties. J. Agric. Sci. 13(03): 311?20 .ten.1017/S0021859600003592 Jarqu , D., J. Crossa, X. Lacaze, P.