Genomic prediction of synthetic hexaploid wheat upon tetraploid durum and diploid Aegilops parental pools.

Bread wheat (Triticum aestivum L.) is a globally important food crop, which was domesticated about 8-10,000 years ago. Bread wheat is an allopolyploid, and it evolved from two hybridization events of three species. To widen the genetic base in breeding, bread wheat has been re-synthesized by crossing durum wheat (Triticum turgidum ssp. durum) and goat grass (Aegilops tauschii Coss), leading to so-called synthetic hexaploid wheat (SHW). We applied the quantitative genetics tools of "hybrid prediction"-originally developed for the prediction of wheat hybrids generated from different heterotic groups - to a situation of allopolyploidization. Our use-case predicts the phenotypes of SHW for three quantitatively inherited global wheat diseases, namely tan spot (TS), septoria nodorum blotch (SNB), and spot blotch (SB). Our results revealed prediction abilities comparable to studies in 'traditional' elite or hybrid wheat. Prediction abilities were highest using a marker model and performing random cross-validation, predicting the performance of untested SHW (0.483 for SB to 0.730 for TS). When testing parents not necessarily used in SHW, combination prediction abilities were slightly lower (0.378 for SB to 0.718 for TS), yet still promising. Despite the limited phenotypic data, our results provide a general example for predictive models targeting an allopolyploidization event and a method that can guide the use of genetic resources available in gene banks.

Modeling within and between Sub-Genomes Epistasis of Synthetic Hexaploid Wheat for Genome-Enabled Prediction of Diseases.

Common wheat (Triticum aestivum) is a hexaploid crop comprising three diploid sub-genomes labeled A, B, and D. The objective of this study is to investigate whether there is a discernible influence pattern from the D sub-genome with epistasis in genomic models for wheat diseases. Four genomic statistical models were employed; two models considered the linear genomic relationship of the lines. The first model (G) utilized all molecular markers, while the second model (ABD) utilized three matrices representing the A, B, and D sub-genomes. The remaining two models incorporated epistasis, one (GI) using all markers and the other (ABDI) considering markers in sub-genomes A, B, and D, including inter- and intra-sub-genome interactions. The data utilized pertained to three diseases: tan spot (TS), septoria nodorum blotch (SNB), and spot blotch (SB), for synthetic hexaploid wheat (SHW) lines. The results (variance components) indicate that epistasis makes a substantial contribution to explaining genomic variation, accounting for approximately 50% in SNB and SB and only 29% for TS. In this contribution of epistasis, the influence of intra- and inter-sub-genome interactions of the D sub-genome is crucial, being close to 50% in TS and higher in SNB (60%) and SB (60%). This increase in explaining genomic variation is reflected in an enhancement of predictive ability from the G model (additive) to the ABDI model (additive and epistasis) by 9%, 5%, and 1% for SNB, SB, and TS, respectively. These results, in line with other studies, underscore the significance of the D sub-genome in disease traits and suggest a potential application to be explored in the future regarding the selection of parental crosses based on sub-genomes.

Evaluation of Fillers for Magnesium Potassium Phosphate Cement (MKPC) for the Encapsulation of Low and Intermediate Level Metallic Radioactive Wastes.

This study investigates the effect of coal fly ash (FA), wollastonite (WO), pumice (PM), and metakaolin (MK) as filler materials in the rheological, mechanical, chemical, and mineralogical properties of a magnesium potassium phosphate cement (MKPC), designed for the encapsulation of low and intermediate level radioactive wastes containing reactive metals. Workability, compression strength, dimensional stability, pH, chemical composition, and mineralogical properties were studied in different pastes and mortars of MKPC with a fixed molar ratio of MgO/KH2PO4 = 1. No new mineral phases were found with the addition of the fillers, denoting their low chemical impact on the MKPC system. Moreover, all formulations with a water/cement mass ratio of <0.65 presented compressive strengths higher than 30 MPa after 90 days, and pH values lower than 8.5, corresponding to the passivation zone of aluminum corrosion.

Prediction of near-term climate change impacts on UK wheat quality and the potential for adaptation through plant breeding.

Wheat is a major crop worldwide, mainly cultivated for human consumption and animal feed. Grain quality is paramount in determining its value and downstream use. While we know that climate change threatens global crop yields, a better understanding of impacts on wheat end-use quality is also critical. Combining quantitative genetics with climate model outputs, we investigated UK-wide trends in genotypic adaptation for wheat quality traits. In our approach, we augmented genomic prediction models with environmental characterisation of field trials to predict trait values and climate effects in historical field trial data between 2001 and 2020. Addition of environmental covariates, such as temperature and rainfall, successfully enabled prediction of genotype by environment interactions (G × E), and increased prediction accuracy of most traits for new genotypes in new year cross validation. We then extended predictions from these models to much larger numbers of simulated environments using climate scenarios projected under Representative Concentration Pathways 8.5 for 2050-2069. We found geographically varying climate change impacts on wheat quality due to contrasting associations between specific weather covariables and quality traits across the UK. Notably, negative impacts on quality traits were predicted in the East of the UK due to increased summer temperatures while the climate in the North and South-west may become more favourable with increased summer temperatures. Furthermore, by projecting 167,040 simulated future genotype-environment combinations, we found only limited potential for breeding to exploit predictable G × E to mitigate year-to-year environmental variability for most traits except Hagberg falling number. This suggests low adaptability of current UK wheat germplasm across future UK climates. More generally, approaches demonstrated here will be critical to enable adaptation of global crops to near-term climate change.

Modeling genotype × environment interaction for single and multitrait genomic prediction in potato (Solanum tuberosum L.).

In this study, we extend research on genomic prediction (GP) to polysomic polyploid plant species with the main objective to investigate single-trait (ST) and multitrait (MT) multienvironment (ME) models using field trial data from 3 locations in Sweden [Helgegården (HEL), Mosslunda (MOS), Umeå (UM)] over 2 years (2020, 2021) of 253 potato cultivars and breeding clones for 5 tuber weight traits and 2 tuber flesh quality characteristics. This research investigated the GP of 4 genome-based prediction models with genotype × environment interactions (GEs): (1) ST reaction norm model (M1), (2) ST model considering covariances between environments (M2), (3) ST M2 extended to include a random vector that utilizes the environmental covariances (M3), and (4) MT model with GE (M4). Several prediction problems were analyzed for each of the GP accuracy of the 4 models. Results of the prediction of traits in HEL, the high yield potential testing site in 2021, show that the best-predicted traits were tuber flesh starch (%), weight of tuber above 60 or below 40 mm in size, and the total tuber weight. In terms of GP, accuracy model M4 gave the best prediction accuracy in 3 traits, namely tuber weight of 40-50 or above 60 mm in size, and total tuber weight, and very similar in the starch trait. For MOS in 2021, the best predictive traits were starch, weight of tubers above 60, 50-60, or below 40 mm in size, and the total tuber weight. MT model M4 was the best GP model based on its accuracy when some cultivars are observed in some traits. For the GP accuracy of traits in UM in 2021, the best predictive traits were the weight of tubers above 60, 50-60, or below 40 mm in size, and the best model was MT M4, followed by models ST M3 and M2.

Comparison of single-trait and multi-trait genomic predictions on agronomic and disease resistance traits in spring wheat.

KEY MESSAGE: This study performed comprehensive analyses on the predictive abilities of single-trait and two multi-trait models in three populations. Our results demonstrated the superiority of multi-traits over single-trait models across seven agronomic and four to seven disease resistance traits of different genetic architecture. The predictive ability of multi-trait and single-trait prediction models has not been investigated on diverse traits evaluated under organic and conventional management systems. Here, we compared the predictive abilities of 25% of a testing set that has not been evaluated for a single trait (ST), not evaluated for multi-traits (MT1), and evaluated for some traits but not others (MT2) in three spring wheat populations genotyped either with the wheat 90K single nucleotide polymorphisms array or DArTseq. Analyses were performed on seven agronomic traits evaluated under conventional and organic management systems, four to seven disease resistance traits, and all agronomic and disease resistance traits simultaneously. The average prediction accuracies of the ST, MT1, and MT2 models varied from 0.03 to 0.78 (mean 0.41), from 0.05 to 0.82 (mean 0.47), and from 0.05 to 0.92 (mean 0.67), respectively. The predictive ability of the MT2 model was significantly greater than the ST model in all traits and populations except common bunt with the MT1 model being intermediate between them. The MT2 model increased prediction accuracies over the ST and MT1 models in all traits by 9.0-82.4% (mean 37.3%) and 2.9-82.5% (mean 25.7%), respectively, except common bunt that showed up to 7.7% smaller accuracies in two populations. A joint analysis of all agronomic and disease resistance traits further improved accuracies within the MT1 and MT2 models on average by 21.4% and 17.4%, respectively, as compared to either the agronomic or disease resistance traits, demonstrating the high potential of the multi-traits models in improving prediction accuracies.

Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction.

Genomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E.

Genome-Based Genotype × Environment Prediction Enhances Potato (Solanum tuberosum L.) Improvement Using Pseudo-Diploid and Polysomic Tetraploid Modeling.

Potato breeding must improve its efficiency by increasing the reliability of selection as well as identifying a promising germplasm for crossing. This study shows the prediction accuracy of genomic-estimated breeding values for several potato (Solanum tuberosum L.) breeding clones and the released cultivars that were evaluated at three locations in northern and southern Sweden for various traits. Three dosages of marker alleles [pseudo-diploid (A), additive tetrasomic polyploidy (B), and additive-non-additive tetrasomic polyploidy (C)] were considered in the genome-based prediction models, for single environments and multiple environments (accounting for the genotype-by-environment interaction or G × E), and for comparing two kernels, the conventional linear, Genomic Best Linear Unbiased Prediction (GBLUP) (GB), and the non-linear Gaussian kernel (GK), when used with the single-kernel genetic matrices of A, B, C, or when employing two-kernel genetic matrices in the model using the kernels from B and C for a single environment (models 1 and 2, respectively), and for multi-environments (models 3 and 4, respectively). Concerning the single site analyses, the trait with the highest prediction accuracy for all sites under A, B, C for model 1, model 2, and for GB and GK methods was tuber starch percentage. Another trait with relatively high prediction accuracy was the total tuber weight. Results show an increase in prediction accuracy of model 2 over model 1. Non-linear Gaussian kernel (GK) did not show any clear advantage over the linear kernel GBLUP (GB). Results from the multi-environments had prediction accuracy estimates (models 3 and 4) higher than those obtained from the single-environment analyses. Model 4 with GB was the best method in combination with the marker structure B for predicting most of the tuber traits. Most of the traits gave relatively high prediction accuracy under this combination of marker structure (A, B, C, and B-C), and methods GB and GK combined with the multi-environment with G × E model.

Approximate Genome-Based Kernel Models for Large Data Sets Including Main Effects and Interactions.

The rapid development of molecular markers and sequencing technologies has made it possible to use genomic prediction (GP) and selection (GS) in animal and plant breeding. However, when the number of observations (n) is large (thousands or millions), computational difficulties when handling these large genomic kernel relationship matrices (inverting and decomposing) increase exponentially. This problem increases when genomic × environment interaction and multi-trait kernels are included in the model. In this research we propose selecting a small number of lines m(m < n) for constructing an approximate kernel of lower rank than the original and thus exponentially decreasing the required computing time. First, we describe the full genomic method for single environment (FGSE) with a covariance matrix (kernel) including all n lines. Second, we select m lines and approximate the original kernel for the single environment model (APSE). Similarly, but including main effects and G × E, we explain a full genomic method with genotype × environment model (FGGE), and including m lines, we approximated the kernel method with G × E (APGE). We applied the proposed method to two different wheat data sets of different sizes (n) using the standard linear kernel Genomic Best Linear Unbiased Predictor (GBLUP) and also using eigen value decomposition. In both data sets, we compared the prediction performance and computing time for FGSE versus APSE; we also compared FGGE versus APGE. Results showed a competitive prediction performance of the approximated methods with a significant reduction in computing time. Genomic prediction accuracy depends on the decay of the eigenvalues (amount of variance information loss) of the original kernel as well as on the size of the selected lines m.

On Hadamard and Kronecker products in covariance structures for genotype × environment interaction.

When including genotype × environment interactions (G × E) in genomic prediction models, Hadamard or Kronecker products have been used to model the covariance structure of interactions. The relation between these two types of modeling has not been made clear in genomic prediction literature. Here, we demonstrate that a certain model based on a Hadamard formulation and another using the Kronecker product lead to exactly the same statistical model. Moreover, we illustrate how a multiplication of entries of covariance matrices is related to modeling locus × environmental-variable interactions explicitly. Finally, we use a wheat and a maize data set to illustrate that the environmental covariance E can be specified easily, also if no information on environmental variables - such as temperature or precipitation - is available. Given that lines have been tested in different environments, the corresponding environmental covariance can simply be estimated from the training set as phenotypic covariance between environments. To achieve a high level of increase in predictive ability, the environmental covariance has to be defined appropriately and records on the performance of the lines of the test set under different environmental conditions have to be included in the training set.

Impact of a tire fire accident on soil pollution and the use of clay minerals as natural geo-indicators.

Following the occurrence of a fire at a tire landfill in the surrounding area of Madrid City (Spain), polycyclic aromatic hydrocarbons (PAHs) and trace elements present in soils were analyzed to assess the impact of the fire. The capacity of the soils' clay mineral fraction to reflect this air pollution incident was studied. Fourteen soil samples were collected at different distances under the smoke plume, and they were subjected to high-performance liquid chromatography-photodiode array detection, inductively coupled plasma mass spectrometry and X-ray diffraction analyses. Clay minerals content showed a strong correlation with the pollutants potentially released in the tire fire, acenaphthene, pyrene, benzo(a)pyrene and benzo(a)fluoranthene. Trace metals Zn and Se were related to the proximity of the tire fire without any relationship with clay minerals content. This work suggests the use of natural clay minerals as potential PAHs geo-indicators in response to air pollution, complementary to current air and biological analyses.

[National Health and Nutrition Survey 2018-19: methodology and perspectives]. / Encuesta Nacional de Salud y Nutrición (Ensanut 2018): metodología y perspectivas.

OBJECTIVE: Describe the methodological design of the National Health and Nutrition Survey 2018-19 (Ensanut 2018-19). MATERIALS AND METHODS: Ensanut 2018-19 is a probabilistic household survey. The following design elements are described: survey scope, sampling procedure, measurement procedure, inference procedure, and logistics organization. RESULTS: 44 069 full housing interviews and 82 490 full interviews of individuals were obtained. The home response rate was 87%. The response rate for individuals was 98%. CONCLUSIONS: The probabilistic design of the survey allows to create valid statistical inferences on parameters of public health interest at the national level and for all 32 states. In addition, some of the results are comparable with Ensanut 2012 in order to identify potential changes in the health and nutrition status of the Mexican population, so that health policies can be adjusted if necessary.

OBJETIVO: Describir el diseño metodológico de la Encuesta Nacional de Salud y Nutrición 2018-19 (Ensanut 2018-19). MATERIAL Y MÉTODOS: La Ensanut 2018-19 es una encuesta probabilística de hogares. Se describen los siguientes elementos del diseño: alcance de la encuesta, procedimiento de muestreo, procedimiento de medición, procedimiento de inferencia y organización logística. RESULTADOS: Se obtuvieron 44 069 entrevistas de viviendas completas y 82 490 entrevistas completas de individuos. La tasa de respuesta de hogar fue 87%. La tasa de respuesta de individuos fue de 98%. CONCLUSIONES: El diseño probabilístico de la encuesta permite hacer inferencias estadísticas válidas sobre parámetros de interés para la salud pública a nivel nacional y para las 32 entidades federativas. Además, algunos de sus resultados son comparables con los de la Ensanut 2012 para poder identificar potenciales cambios en los estados de salud y nutrición de la población mexicana, para que en caso de ser necesario s adecuen las políticas de salud.

Encuesta Nacional de Salud y Nutrición 2018-19: metodología y perspectivas / National Health and Nutrition Survey 2018-19: methodology and perspectives

Resumen: Objetivo Describir el diseño metodológico de la Encuesta Nacional de Salud y Nutrición 2018-19 (Ensanut 2018-19). Material y métodos La Ensanut 2018-19 es una encuesta probabilística de hogares. Se describen los siguientes elementos del diseño: alcance de la encuesta, procedimiento de muestreo, procedimiento de medición, procedimiento de inferencia y organización logística. Resultados Se obtuvieron 44 069 entrevistas de viviendas completas y 82 490 entrevistas completas de individuos. La tasa de respuesta de hogar fue 87%. La tasa de respuesta de individuos fue de 98%. Conclusiones El diseño probabilístico de la encuesta permite hacer inferencias estadísticas válidas sobre parámetros de interés para la salud pública a nivel nacional y para las 32 entidades federativas. Además, algunos de sus resultados son comparables con los de la Ensanut 2012 para poder identificar potenciales cambios en los estados de salud y nutrición de la población mexicana, para que en caso de ser necesario se adecuen las políticas de salud.

Abstract: Objective Describe the methodological design of the National Health and Nutrition Survey 2018-19 (Ensanut 2018-19). Materials and methods Ensanut 2018-19 is a probabilistic household survey. The following design elements are described: survey scope, sampling procedure, measurement procedure, inference procedure, and logistics organization. Results 44 069 full housing interviews and 82 490 full interviews of individuals were obtained. The home response rate was 87%. The response rate for individuals was 98%. Conclusions The probabilistic design of the survey allows to create valid statistical inferences on parameters of public health interest at the national level and for all 32 states. In addition, some of the results are comparable with Ensanut 2012 in order to identify potential changes in the health and nutrition status of the Mexican population, so that health policies can be adjusted if necessary.

A Bayesian Genomic Multi-output Regressor Stacking Model for Predicting Multi-trait Multi-environment Plant Breeding Data.

In this paper we propose a Bayesian multi-output regressor stacking (BMORS) model that is a generalization of the multi-trait regressor stacking method. The proposed BMORS model consists of two stages: in the first stage, a univariate genomic best linear unbiased prediction (GBLUP including genotype × environment interaction GE) model is implemented for each of the L traits under study; then the predictions of all traits are included as covariates in the second stage, by implementing a Ridge regression model. The main objectives of this research were to study alternative models to the existing multi-trait multi-environment (BMTME) model with respect to (1) genomic-enabled prediction accuracy, and (2) potential advantages in terms of computing resources and implementation. We compared the predictions of the BMORS model to those of the univariate GBLUP model using 7 maize and wheat datasets. We found that the proposed BMORS produced similar predictions to the univariate GBLUP model and to the BMTME model in terms of prediction accuracy; however, the best predictions were obtained under the BMTME model. In terms of computing resources, we found that the BMORS is at least 9 times faster than the BMTME method. Based on our empirical findings, the proposed BMORS model is an alternative for predicting multi-trait and multi-environment data, which are very common in genomic-enabled prediction in plant and animal breeding programs.

Deep Kernel for Genomic and Near Infrared Predictions in Multi-environment Breeding Trials.

Kernel methods are flexible and easy to interpret and have been successfully used in genomic-enabled prediction of various plant species. Kernel methods used in genomic prediction comprise the linear genomic best linear unbiased predictor (GBLUP or GB) kernel, and the Gaussian kernel (GK). In general, these kernels have been used with two statistical models: single-environment and genomic × environment (GE) models. Recently near infrared spectroscopy (NIR) has been used as an inexpensive and non-destructive high-throughput phenotyping method for predicting unobserved line performance in plant breeding trials. In this study, we used a non-linear arc-cosine kernel (AK) that emulates deep learning artificial neural networks. We compared AK prediction accuracy with the prediction accuracy of GB and GK kernel methods in four genomic data sets, one of which also includes pedigree and NIR information. Results show that for all four data sets, AK and GK kernels achieved higher prediction accuracy than the linear GB kernel for the single-environment and GE multi-environment models. In addition, AK achieved similar or slightly higher prediction accuracy than the GK kernel. For all data sets, the GE model achieved higher prediction accuracy than the single-environment model. For the data set that includes pedigree, markers and NIR, results show that the NIR wavelength alone achieved lower prediction accuracy than the genomic information alone; however, the pedigree plus NIR information achieved only slightly lower prediction accuracy than the marker plus the NIR high-throughput data.

Deep Kernel and Deep Learning for Genome-Based Prediction of Single Traits in Multienvironment Breeding Trials.

Deep learning (DL) is a promising method for genomic-enabled prediction. However, the implementation of DL is difficult because many hyperparameters (number of hidden layers, number of neurons, learning rate, number of epochs, batch size, etc.) need to be tuned. For this reason, deep kernel methods, which only require defining the number of layers, may be an attractive alternative. Deep kernel methods emulate DL models with a large number of neurons, but are defined by relatively easily computed covariance matrices. In this research, we compared the genome-based prediction of DL to a deep kernel (arc-cosine kernel, AK), to the commonly used non-additive Gaussian kernel (GK), as well as to the conventional additive genomic best linear unbiased predictor (GBLUP/GB). We used two real wheat data sets for benchmarking these methods. On average, AK and GK outperformed DL and GB. The gain in terms of prediction performance of AK and GK over DL and GB was not large, but AK and GK have the advantage that only one parameter, the number of layers (AK) or the bandwidth parameter (GK), has to be tuned in each method. Furthermore, although AK and GK had similar performance, deep kernel AK is easier to implement than GK, since the parameter "number of layers" is more easily determined than the bandwidth parameter of GK. Comparing AK and DL for the data set of year 2015-2016, the difference in performance of the two methods was bigger, with AK predicting much better than DL. On this data, the optimization of the hyperparameters for DL was difficult and the finally used parameters may have been suboptimal. Our results suggest that AK is a good alternative to DL with the advantage that practically no tuning process is required.

Stevensite-based geofilter for the retention of tetracycline from water.

The antibiotic tetracycline, is considered a contaminant of emerging concern due to its presence in wastewater effluents, surface waters and groundwaters. Adsorption of tetracycline on soils and clays has been extensively studied to remove the contaminant from the water. A decreasing adsorption as the pH increases is normally reported in the pH range 3-9. However, adsorption isotherms performed on a commercial stevensite presented increasing adsorption with the increasing pH, in the pH range 2-8. This is very interesting since the pH in natural and wasterwaters are normally in the range 6-8. A laboratory design of a geofilter using a mixture of sand and stevensite was tested against an inflow solution of tetracycline 1 g/L, NaNO3 0.1 M and pH = 7 in an advective transport cell experiment. The number of tetracycline molecules exceed by >3 times the number exchangeable positions in the stevensite geofilter. Under these conditions, the TC adsorption on the geofilter reaches 590 mg/g, surpassing the retention capacity of most adsorbents found in literature. Besides, the tetracycline is completely desorbed by the inflow of a saline solution (Mg(NO3)2 0.5 M, at pH = 2) with capacity to replace the exchangeable positions, thus, recovering the geofilter and the tetracycline.

BGGE: A New Package for Genomic-Enabled Prediction Incorporating Genotype × Environment Interaction Models.

One of the major issues in plant breeding is the occurrence of genotype × environment (GE) interaction. Several models have been created to understand this phenomenon and explore it. In the genomic era, several models were employed to improve selection by using markers and account for GE interaction simultaneously. Some of these models use special genetic covariance matrices. In addition, the scale of multi-environment trials is getting larger, and this increases the computational challenges. In this context, we propose an R package that, in general, allows building GE genomic covariance matrices and fitting linear mixed models, in particular, to a few genomic GE models. Here we propose two functions: one to prepare the genomic kernels accounting for the genomic GE and another to perform genomic prediction using a Bayesian linear mixed model. A specific treatment is given for sparse covariance matrices, in particular, to block diagonal matrices that are present in some GE models in order to decrease the computational demand. In empirical comparisons with Bayesian Genomic Linear Regression (BGLR), accuracies and the mean squared error were similar; however, the computational time was up to five times lower than when using the classic approach. Bayesian Genomic Genotype × Environment Interaction (BGGE) is a fast, efficient option for creating genomic GE kernels and making genomic predictions.

Investigating the Potential Barrier Function of Nanostructured Materials Formed in Engineered Barrier Systems (EBS) Designed for Nuclear Waste Isolation.

Clay and cement are known nano-colloids originating from natural processes or traditional materials technology. Currently, they are used together as part of the engineered barrier system (EBS) to isolate high-level nuclear waste (HLW) metallic containers in deep geological repositories (DGR). The EBS should prevent radionuclide (RN) migration into the biosphere until the canisters fail, which is not expected for approximately 103  years. The interactions of cementitious materials with bentonite swelling clay have been the scope of our research team at the Autonomous University of Madrid (UAM) with participation in several European Union (EU) projects from 1998 up to now. Here, we describe the mineral and chemical nature and microstructure of the alteration rim generated by the contact between concrete and bentonite. Its ability to buffer the surrounding chemical environment may have potential for further protection against RN migration.

Genomic-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials.

In this study, we compared the prediction accuracy of the main genotypic effect model (MM) without G×E interactions, the multi-environment single variance G×E deviation model (MDs), and the multi-environment environment-specific variance G×E deviation model (MDe) where the random genetic effects of the lines are modeled with the markers (or pedigree). With the objective of further modeling the genetic residual of the lines, we incorporated the random intercepts of the lines ([Formula: see text]) and generated another three models. Each of these 6 models were fitted with a linear kernel method (Genomic Best Linear Unbiased Predictor, GB) and a Gaussian Kernel (GK) method. We compared these 12 model-method combinations with another two multi-environment G×E interactions models with unstructured variance-covariances (MUC) using GB and GK kernels (4 model-method). Thus, we compared the genomic-enabled prediction accuracy of a total of 16 model-method combinations on two maize data sets with positive phenotypic correlations among environments, and on two wheat data sets with complex G×E that includes some negative and close to zero phenotypic correlations among environments. The two models (MDs and MDE with the random intercept of the lines and the GK method) were computationally efficient and gave high prediction accuracy in the two maize data sets. Regarding the more complex G×E wheat data sets, the prediction accuracy of the model-method combination with G×E, MDs and MDe, including the random intercepts of the lines with GK method had important savings in computing time as compared with the G×E interaction multi-environment models with unstructured variance-covariances but with lower genomic prediction accuracy.