A Bayesian Multiple-Trait and Multiple-Environment Model Using the Matrix Normal Distribution

Genomic selection (GS) is playing a major role in plant breeding for the selection of candidate individuals (animal or plants) early in time. However, for improving GS better statistical models are required. For this reason, in this chapter book we provide an improved version of the Bayesian multiple-trait and multiple-environment (BMTME) model of Montesinos-López et al. that takes into account the correlation between traits (genetic and residual) and between environments since allows general covariance’s matrices. This improved version of the BMTME model was derived using the matrix normal distribution that allows a more easy derivation of all full conditional distributions required, allows a more efficient model in terms of time of implementation. We tested the proposed model using simulated and real data sets. According to our results we have elements to conclude that this model improved considerably in terms of time of implementation and it is better than a Bayesian multiple-trait, multiple-environment model that not take into account general covariance structure for covariance’s of the traits and environments

Arnmonoids and trilobites from the Vidrieros Formation (Horcada del Oro, Palentine Domain, NW Spain)

[Resumen] Se analizan los arnmonoideos y trilobites de una parte de la Fm. Vidrieros, detectándose por primera vez faunas de arnmnoideos característicos del dolll. Faunas de cefalópodos pertenecientes a los géneros Maeneceras, Pseudoclymenia, Protoxoclymenia? y Protomoceras se citan por primera vez en la Cordillera Cantábrica. Se registra novedosamente la presencia de Tornoceras en capas del doN del Dominio Palentino así como, el primer hallazgo en España de una forma de trilobite del género Dianops.[Abstract] Arnmonoids and Trilobites from a part of the Vidrieros Formation are analized, detecting for the first time Arnmonoids forms characteristic of the doIl!. The f11'st reference about Cephalopod faunas belonging to the genera Maeneceras, Pseudoclymenia, Protoxoclymenia? y Protornoceras in the Cantabrian Mountains is given. The presence of Tomoceras is recorded for the first time in beds of dolV in the Palentine Domain, as well as the first finding in Spain of a Trilobite form belonging to the Dianops genus

Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments

It is of paramount importance in plant breeding to have methods dealing with large numbers of predictor variables and few sample observations, as well as efficient methods for dealing with high correlation in predictors and measured traits. This paper explores in terms of prediction performance the partial least squares (PLS) method under single-trait (ST) and multi-trait (MT) prediction of potato traits. The first prediction was for tested lines in tested environments under a five-fold cross-validation (5FCV) strategy and the second prediction was for tested lines in untested environments (herein denoted as leave one environment out cross validation, LOEO). There was a good performance in terms of predictions (with accuracy mostly > 0.5 for Pearson’s correlation) the accuracy of 5FCV was better than LOEO. Hence, we have empirical evidence that the ST and MT PLS framework is a very valuable tool for prediction in the context of potato breeding data

Threshold Models for Genome-Enabled Prediction of Ordinal Categorical Traits in Plant Breeding

Categorical scores for disease susceptibility or resistance often are recorded in plant breeding. The aim of this study was to introduce genomic models for analyzing ordinal characters and to assess the predictive ability of genomic predictions for ordered categorical phenotypes using a threshold model counterpart of the Genomic Best Linear Unbiased Predictor (i.e., TGBLUP). The threshold model was used to relate a hypothetical underlying scale to the outward categorical response. We present an empirical application where a total of nine models, five without interaction and four with genomic x environment interaction (G·E) and genomic additive x additive x environment interaction (GxGxE), were used. We assessed the proposed models using data consisting of 278 maize lines genotyped with 46,347 single-nucleotide polymorphisms and evaluated for disease resistance [with ordinal scores from 1 (no disease) to 5 (complete infection)] in three environments (Colombia, Zimbabwe, and Mexico). Models with GxE captured a sizeable proportion of the total variability, which indicates the importance of introducing interaction to improve prediction accuracy. Relative to models based on main effects only, the models that included GxE achieved 9–14% gains in prediction accuracy; adding additive x additive interactions did not increase prediction accuracy consistently across locations

A New Deep Learning Calibration Method Enhances Genome-Based Prediction of Continuous Crop Traits

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Bayesian multitrait kernel methods improve multienvironment genome-based prediction

When multitrait data are available, the preferred models are those that are able to account for correlations between phenotypic traits because when the degree of correlation is moderate or large, this increases the genomic prediction accuracy. For this reason, in this article, we explore Bayesian multitrait kernel methods for genomic prediction and we illustrate the power of these models with three-real datasets. The kernels under study were the linear, Gaussian, polynomial, and sigmoid kernels; they were compared with the conventional Ridge regression and GBLUP multitrait models. The results show that, in general, the Gaussian kernel method outperformed conventional Bayesian Ridge and GBLUP multitrait linear models by 2.2–17.45% (datasets 1–3) in terms of prediction performance based on the mean square error of prediction. This improvement in terms of prediction performance of the Bayesian multitrait kernel method can be attributed to the fact that the proposed model is able to capture nonlinear patterns more efficiently than linear multitrait models. However, not all kernels perform well in the datasets used for evaluation, which is why more than one kernel should be evaluated to be able to choose the best kernel

Genomic Bayesian Prediction Model for Count Data with Genotype x Environment Interaction

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

Effect of different interpolation methods on the accuracy of the reconstruction of spiral k-space trajectories in MRI

[Poster] 4th European Molecular Imaging Meeting, Barcelona, Spain, May 27 - 30, 2009This work is supported in part by the projects CdTeaM (CeniT-ingenio 2010), Ministerio de Ciencia e innovación, and Ciber Cb07/09/0031 CiberSaM, Ministerio de Sanidad y Consumo.Publicad

Process intensification through staggered herringbone micro-channels: Mass transfer enhancement to a reactive wall

In the present study, the flow behaviour through different micro-herringbone channels configurations (1-peak, 2-peak, 1–2 alternated peak herringbone channel and a flow inversion geometry) have been numerically analysed as a mean of intensifying mass transfer to a reactive boundary. Results showed that the mass transfer coefficients were higher for the 1–2 alternated herringbone structure than those with, either, 1-peak or 2-peak structures. Moreover, the flow inversion structure mass transfer coefficients were double those obtained for the staggered herringbone channel. The alternated herringbone channel combines a different set of herringbone structures that are efficient at removing the boundary layer at different parts of the channel. The combination of these structures provide an enhanced mass transfer performance as compared to a standard herringbone channel. The obtained results showed that a 2D simplified model which uses hydrodynamic data from CFD simulations is a reasonable substitute for full 3D particle tracking simulations in terms of the mass transfer behavior of the 1PSHC with a 97.5 % of accuracy related to the asymptotic Sherwood number. The mixing capacity of the herringbones was accounted for by an apparent effective diffusion coefficient. The agreement between the 3D and 2D simulation was reasonable