Genetic ResourcesGenetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IPhttp://hdl.handle.net/10883/32017-09-26T05:37:57Z2017-09-26T05:37:57ZMeasuring Intraspecific Genetic DiversityToledo, F.H.http://hdl.handle.net/10883/188712017-08-23T16:53:04Z2016-01-01T00:00:00ZMeasuring Intraspecific Genetic Diversity
Toledo, F.H.
2016-01-01T00:00:00ZA genomic bayesian multi-trait and multi-environment modelMontesinos-Lopez, O.A.Montesinos-López, A.Toledo, F.H.Pérez-Hernández, O.Eskridge, K.Rutkoski, J.Crossa, J.http://hdl.handle.net/10883/188702017-08-25T15:01:37Z2016-01-01T00:00:00ZA genomic bayesian multi-trait and multi-environment model
Montesinos-Lopez, O.A.; Montesinos-López, A.; Toledo, F.H.; Pérez-Hernández, O.; Eskridge, K.; Rutkoski, J.; Crossa, J.
When information on multiple genotypes evaluated in multiple environments is recorded, a multi-environment single trait model for assessing genotype · environment interaction (G · E) is usually employed. Comprehensive models that simultaneously take into account the correlated traits and trait · genotype · environment interaction (T · G · E) are lacking. In this research, we propose a Bayesian model for analyzing multiple traits and multiple environments for whole-genome prediction (WGP) model. For this model, we used Half-t priors on each standard deviation term and uniform priors on each correlation of the covariance matrix. These priors were not informative and led to posterior inferences that were insensitive to the choice of hyper-parameters. We also developed a computationally efficient Markov Chain Monte Carlo (MCMC) under the above priors, which allowed us to obtain all required full conditional distributions of the parameters leading to an exact Gibbs sampling for the posterior distribution. We used two real data sets to implement and evaluate the proposed Bayesian method and found that when the correlation between traits was high (.0.5), the proposed model (with unstructured variance–covariance) improved prediction accuracy compared to the model with diagonal and standard variance–covariance structures. The R-software package Bayesian Multi-Trait and Multi-Environment (BMTME) offers optimized C++ routines to efficiently perform the analyses.
2016-01-01T00:00:00ZA predetermined proportional gains eigen selection index methodCeron Rojas, J.J.Toledo, F.H.Sahagún-Castellanos, J.Crossa, J.http://hdl.handle.net/10883/188692017-08-25T15:02:51Z2016-01-01T00:00:00ZA predetermined proportional gains eigen selection index method
Ceron Rojas, J.J.; Toledo, F.H.; Sahagún-Castellanos, J.; Crossa, J.
The most general linear phenotypic selection index (PSI) is the predetermined proportional gains phenotypic selection index (PPG-PSI) that allows imposing restrictions on the trait expected genetic gain values to make some traits change their mean values based on a predetermined level, while the rest of the traits remain without restrictions. However, due to the increasing number of restricted traits: (i) PPG-PSI accuracy decreases; (ii) the proportional constant associated with this index can be negative, in which case, its results have no meaning in practice; and (iii) the PPG-PSI can shift the population means in the opposite direction to the predetermined desired direction. Based on the eigen selection index method (ESIM), we propose a PPG-ESIM that does not require a proportional constant, and due to the properties associated with eigen analysis, it is possible to use the theory of similar matrices to change the direction of the eigenvector values without affecting PPG-ESIM accuracy, which helps to eliminate the problem indicated in the third point above, associated with the standard PPG-PSI. The PPG-ESIM uses the first eigenvector as its vector of coefficients, and the first eigenvalue in the selection response. Two simulated and one real data set, each with four traits, were used to validate PPG-ESIM efficiency vs. PPG-PSI efficiency; the simulated and real results indicated that PPG-ESIM efficiency was higher than PPG-PSI efficiency. We concluded that PPG-ESIM is an efficient selection index that can be used in any selection program as a good alternative to PPG-PSI.
2016-01-01T00:00:00ZExploración de los recursos genéticos de maíz para alelos nuevos resistentes a la sequíaMolnar, T.L.http://hdl.handle.net/10883/188492017-08-17T08:00:44Z2016-01-01T00:00:00ZExploración de los recursos genéticos de maíz para alelos nuevos resistentes a la sequía
Molnar, T.L.
2016-01-01T00:00:00Z