Person: Eskridge, K.
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Eskridge
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K.
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Eskridge, K.
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0000-0002-0582-4899 6 results
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- Turfgrass performance of diploid buffalograss [buchloe dactyloides (Nutt.) Engelm.] half-sib populations(American Society for Horticultural Science, 2012) Serba, D.D.; Gulsen, O.; Abeyo Bekele Geleta; Amundsen, K.L.; Lee, D.J.; Baenziger, P.S.; Heng-Moss, T.M.; Eskridge, K.; Shearman, R.C.
Publication - A regression model for pooled data in a two-stage survey under informative sampling with application for detecting and estimating the presence of transgenic corn(Cambridge University Press, 2016) Montesinos-Lopez, O.A.; Eskridge, K.; Montesinos-López, A.; Crossa, J.; Cortés-Cruz, M.; Dong Wang
Publication - Genomic prediction models for count data(Springer Verlag, 2015) Montesinos-Lopez, O.A.; Montesinos-López, A.; Pérez-Rodríguez, P.; Eskridge, K.; Xinyao He; Juliana, P.; Singh, P.; Crossa, J.Whole genome prediction models are useful tools for breeders when selecting candidate individuals early in life for rapid genetic gains. However, most prediction models developed so far assume that the response variable is continuous and that its empirical distribution can be approximated by a Gaussian model. A few models have been developed for ordered categorical phenotypes, but there is a lack of genomic prediction models for count data. There are well-established regression models for count data that cannot be used for genomic-enabled prediction because they were developed for a large sample size (n) and a small number of parameters (p); however, the rule in genomic-enabled prediction is that p is much larger than the sample size n. Here we propose a Bayesian mixed negative binomial (BMNB) regression model for counts, and we present the conditional distributions necessary to efficiently implement a Gibbs sampler. The proposed Bayesian inference can be implemented routinely. We evaluated the proposed BMNB model together with a Poisson model, a Normal model with untransformed response, and a Normal model with transformed response using a logarithm, and applied them to two real wheat datasets from the International Maize and Wheat Improvement Center. Based on the criteria used for assessing genomic prediction accuracy, results indicated that the BMNB model is a viable alternative for analyzing count data.
Publication - A genomic bayesian multi-trait and multi-environment model(Genetics Society of America, 2016) Montesinos-Lopez, O.A.; Montesinos-López, A.; Crossa, J.; Toledo, F.H.; Pérez-Hernández, O.; Eskridge, K.; Rutkoski, 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.
Publication - Genomic bayesian prediction model for count data with genotype X environment interaction(Genetics Society of America, 2016) Montesinos-López, A.; Montesinos-Lopez, O.A.; Crossa, J.; Burgueño, J.; Eskridge, K.; Falconi, E.E.; Xinyao He; Singh, P.K.; Cichy, K.Genomic tools allow the study of the whole genome and are facilitating 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×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.
Publication - Sample size under inverse negative binomial group testing for accuracy in parameter estimation(Public Library of Science, 2012) Montesinos-Lopez, O.A.; Montesinos-López, A.; Crossa, J.; Eskridge, K.Background. The group testing method has been proposed for the detection and estimation of genetically modified plants (adventitious presence of unwanted transgenic plants, AP). For binary response variables (presence or absence), group testing is efficient when the prevalence is low, so that estimation, detection, and sample size methods have been developed under the binomial model. However, when the event is rare (low prevalence <0.1), and testing occurs sequentially, inverse (negative) binomial pooled sampling may be preferred. Methodology/Principal Findings. This research proposes three sample size procedures (two computational and one analytic) for estimating prevalence using group testing under inverse (negative) binomial sampling. These methods provide the required number of positive pools (), given a pool size (k), for estimating the proportion of AP plants using the Dorfman model and inverse (negative) binomial sampling. We give real and simulated examples to show how to apply these methods and the proposed sample-size formula. The Monte Carlo method was used to study the coverage and level of assurance achieved by the proposed sample sizes. An R program to create other scenarios is given in Appendix S2. Conclusions. The three methods ensure precision in the estimated proportion of AP because they guarantee that the width (W) of the confidence interval (CI) will be equal to, or narrower than, the desired width (), with a probability of . With the Monte Carlo study we found that the computational Wald procedure (method 2) produces the more precise sample size (with coverage and assurance levels very close to nominal values) and that the samples size based on the Clopper-Pearson CI (method 1) is conservative (overestimates the sample size); the analytic Wald sample size method we developed (method 3) sometimes underestimated the optimum number of pools.
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