dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA Extraction and Microarray Data RNAs from 3-week-old seedlings, protoplasts, and PdCs after 24, 48, 96, and 168 h of culture were extracted using the RNAeasy mini kit (Qiagen). RNA integrity was tested with an Agilent Bioanalyzer. For each biological replicate, 4. 5 3 10 7 protoplasts were isolated: 10 7 of freshly prepared protoplasts were used for time point 0 h, and 3 3 10 7 protoplasts were dispatched and cultured in 40 Petri dishes (8 3 10 5 per dish). Cells from nine Petri dishes were pooled for each culture time point. Hybridization microarray analysis and statistical analyses based on two independent biological replicates, and two dye-swaps were performed as previously described using the 25 K CATMA_v2. 1 microarray bearing 24 576 genespecific tags  (Lurin et al. , 2004; Gagnot et al.
, 2008)  (see Supplemental Methods 1 online). After statistical validation, variations in transcript abundance were expressed as the log 2 of the ratio of hybridization intensities between biological materials and/or successive steps. The microarray data sets were deposited into the Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/; accession number GSE7984) and CATdb (http://urgv. evry. inra. fr/cgi-bin/projects/CATdb/consult_project. pl?project_id=28. ), according to Minimum Information About a Microarray Experiment standards.
Transcriptome Analysis Supplemental Data Set 1 online lists the 8206 genes differentially expressed in at least one of the nine conditions tested in our experimental design. The various publicly available data sets used in this study, such as the chromatin-related genes (ChromDB; http://chromdb. org/), the lists of TFs  (Mitsuda and Ohme-Takagi, 2009; Lu et al. , 2012) , and the list of genes from Visual LRTC  (Parizot et al. , 2010) , were merged with our transcript profiles, our lists of genes associated with selected GO information related to the cell wall and cell cycle (GO website), and the identity of the clusters. A specific spreadsheet in Supplemental Data Set 1 online, called gene selector, was designed in Excel using advanced functions, to extract expression profiles and information related to small user lists of sorted AGIs.
ACKNOWLEDGMENTS This work was supported by the  Institut National de la Recherche Agronomique, France. We thank  John Celenza  for afl4-1 seeds,  Hervé Vaucheret  for ago4-4 seeds and fruitful discussions, and all technicians in charge of the  Institut Jean-Pierre Bourgin greenhouses , with special mention to  Bruno Letarnec. We thank  Carolyn Napoli  and the Chromatin Database for providing the Arabidopsis chromatin gene list,  Boris Parizot  for providing the VisuaLRTC Excel module, and  Chi Zhang  for the list of TFs and their families. We also thank  Margaret Platt-Hommel ,  Herman Höfte , and  Loïc Lepiniec  for critical reading of the article.
Genomic selection (GS) offers breeders the possibility of using historic data and unbalanced breeding trials to form training populations for predicting the performance of new lines. However, when using datasets that are unbalanced over time and space, there is increasing exposure to different genotype environment combinations and interactions that may make predictions less accurate. Global crossvalidated genomic prediction accuracies may be high when using large historic datasets but accuracies for individual years using a forward-prediction approach, or accuracies for individual locations, are often much lower. The objective of this study was to evaluate the overall accuracy of genomic predictions for untested genotypes using an unbalanced dataset to train a genomic selection model, and to explore ways of combining genomic selection and genotype-by-environment (G×E) interaction models to better target untested lines to different locations.
Using the International Center for Maize and Wheat Improvement's (CIMMYT) Semi-Arid Wheat Yield Trials (SAWYT) we assessed the accuracy of genomic predictions and the potential to subset these nurseries using the concept of mega-environments (ME) adapted to a genomic selection context. We found that there was no difference in accuracy between models accounting for G×E interactions and global models. Data-driven methods of clustering locations based on similarities in genomic predictions also failed to improve accuracies within clusters. Using a simulation based on the empirical SAWYT data, we found that if there were different true genotypic values between clusters, there was an advantage to modeling G×E in prediction models. In the SAWYT dataset it appears that there is not a consistent pattern of genotype-by-environment interaction among the ME, and this dataset is not balanced enough to partition into new clusters that have predictive power.
Potential of genomic data to improve the utility of unbalanced historical datasets The ubiquity of unbalanced historic datasets in plant breeding programs is a longstanding challenge. Breeding lines are selected and promising lines are advanced to a point where enough seed is available for multi-locational trials of candidates for release. Other than possibly one or two long term checks, the entries in these trials change yearly. The use of phenotypic data from relatives and ancestors has been limited by the challenge of maintaining adequate pedigree records and the expense of obtaining genotypic data on hundreds of lines at early stages in a breeding program. This situation is rapidly changing, however, as new genotyping platforms have made it possible to obtain high-density markers at very low cost  (Elshire et al.
, 2011; Poland and Rife, 2012). Software and database tools have become available that make it possible to keep track of phenotypic, genotypic and pedigree records for thousand of individuals over many years. Because of improvements in genotyping and statistical methods that can handle this type of data, genomic selection has become possible for many breeding programs. Genomic selection makes predictions of performance for new lines or improves estimates of performance for these lines by using phenotypic and genomic data from related genotypes  (Heffner et al. , 2009; Lorenz et al. , 2011). While there is much interest in using genomic selection and historical data to improve current selection programs, this has not yet been put into practice because of logistical difficulties in assembling data and questions about the best data and sets of genotypes ("training populations") to use in model training for individual breeding programs or target environments.
National breeding programs could greatly benefit from using data from other programs with similar environmental conditions, combined with their own historical data and the international center data, in order to increase their power to detect superior lines for their target environments. The objective of this study was to assess the accuracy of genomic predictions in a large unbalanced dataset. We used CIMMYT's international semi-arid wheat yield trial (SAWYT), with data reported on grain yield for genotypes sent out by CIMMYT over a period of 17 years. See Appendix A for more information and references on the CIMMYT international yield trials. We first looked at the global accuracy and variation in accuracy over time. Then we tested different methods of accounting for G×E interaction when making genomic predictions.
Genotypic data The wheat genotypes included in SAWYT 1-17, indexed by their genotypic identification number (GID), were characterized using genotyping-by-sequencing following the same procedure as described in  Poland et al. (2012). A total of 45,818 SNP markers were obtained, and 34,843 were retained with a maximum of 70% missing data for each individual marker. The marker-based, additive relationship matrix (A m ) for the 622 genotypes was calculated with the function A. mat in R package rrBLUP, version 4. 1 (R Development Core  Team, 2012; Endelman, 2011) , which centers (but does not standardize) each marker by the population mean  (VanRaden, 2008). Missing data were imputed with the "EM" option in A. mat, which implements a multivariate normal expectationmaximization (EM) algorithm (details in  Poland and Rife, 2012).
Data curation Phenotypic data for the SAWYT was obtained from the CIMMYT bioinformatics unit after initial data cleaning to remove outliers. Yield was the most complete trait; out of a total of 723 trials in the dataset, yield was measured in 611 individual trials and 237 unique locations over 17 years  (planted in years 1992-2009, excluding 1993  because no SAWYT was sent out that year). Each year a separate set of lines was sent to international collaborators who requested seed for the trial. Most trials in the SAWYT had three replications in the first year  (1992) , two replications in years  2-5 (1994-1997)  and two replications with incomplete blocks within reps starting with the sixth year. Data were curated to keep only genotypes with GBS marker data available (622 total). Repeated checks (Dharwar Dry and Cham 6) were eliminated in all years subsequent to their first occurrence because a single check was not considered adequate to characterize environmental conditions or G×E over the 17 years of data available.
Two criteria were used to identify and eliminate trials with errors in matching genotypes and phenotypes. The ratio V a /(V a + V e ), the proportion of variance due to additive genetic effects, was used to eliminate trials where genotypes did not match phenotypic data. Trials with this ratio less than 0. 01 were considered to have errors. Variance components V a & V e were calculated using the relationship matrix A m with the kin. blup function in the package rrBLUP  (Endelman, 2011). Replicated trials were also curated based on having V g /(V g + V e ) greater than 0. 01. Variance components V g & V e were calculated using the lmer function in the R package lme4  (Bates et al. , 2012). Low values were likely due to mismatched plots of the same genotype from the different reps of the trial.
Clustering methods for grouping similar locations Because global genomic predictions may not be the most relevant to individual breeding programs in particular environments, several methods were used to attempt to group locations into similar environmental clusters to improve the accuracy of genomic predictions within each cluster. CIMMYT has defined global megaenvironments (ME) using climatic patterns, farming systems, water regimes, and the incidence of biotic and abiotic stress in the major wheat growing regions of the world. Trials were assigned to ME by breeders and the bioinformatics group at CIMMYT. A complete description of these classifications is available in  Rajaram et al. (1993). The ME present in the SAWYT database, as well as their average yields and yield variability, are shown in Table  1.
ME 3, with acidic soil, was only present in two years at one location in the SAWYT dataset and so was eliminated. The remaining ME were represented in at least 16 of the 17 years. ME 2/4, alternating high and low rainfall, was considered to be part of ME 2 to balance the ME representation across years, and the subsets 4A, 4B and 4C of While CIMMYT ME provide pre-defined clusters, the classifications are not necessarily based on the relative performance of genotypes within each ME. Data-driven clustering methods may provide an alternative grouping of locations based on similar genotypic performance. These clusters can then be used either to identify relevant locations to include in an analysis for a particular group of locations, or to use as target environments in a more complex model accounting for G×E interactions.
Because of the unbalanced nature of the SAWYT dataset, typical clustering analyses could not be used with individual locations, as neither locations nor genotypes were balanced over years. Cluster analysis requires a complete distance matrix calculated from genotypic performance among the locations to be clustered, which usually requires the same genotypes to be evaluated in all environments. The use of genomic prediction can overcome this limitation, as genotypes that were not evaluated in a particular location still have a predicted genotypic value for that location based on the relationship matrix. We used the strategy of  Heslot et al. (2013)  and calculated genomic predictions for every genotype in each location. The distance matrix among locations based on the genomic predictions for all genotypes was used with four different clustering methods: Ward's, complete linkage, K means and model-based clustering.
Details of these clustering methods are presented in Appendix. Ward's hierarchical clustering method and the complete linkage method were implemented with the function hclust and the K means method was implemented with the function kmeans in the R package stats (R Development Core Team, 2012). Model based clustering was implemented in the R package mclust  (Fraley et al. , 2012). The clusters identified by these four methods and the CIMMYT ME were used in a mixed model analysis for each year individually to calculate variance components in the model yield = block(rep) + rep(trial) + trial(cluster) + cluster + GID + cluster * GID + trial(cluster) * GID + error, with all effects random and no relationship matrix, meaning that genotypes (GIDs) were assumed independent. This was done to determine the relative magnitude of different sources of variation within the SAWYT dataset and the within-year relevance of clusters developed using a multi-year dataset.
BLUPs based on yield phenotypes in the validation dataset were used to calculate the accuracy of genomic predictions. Details on calculation of validation yield estimates are in Appendix A. GS accuracies are reported as the correlation between the genomic prediction and the validation values, corrected by the average square root of the repeatability for the validation set  (Dekkers, 2007). We also performed cross-validation with 16 random folds, which ensures that the size of the validation sets is comparable to that of the forward prediction. Cross-validation using random folds are often used as a metric for evaluating the predicted accuracy of genomic selection models. We first adjusted the plot yields for the average year effect based on the assumption of independent genotypes using the R package lme4  (Bates et al.
, 2012). The dataset was divided at random into 16 equal sets, or folds, and the model was developed using 15 of these golds and validated on the 16th. This was repeated 16 times so that each fold was used for validation once. We also evaluated the accuracy of global and environmentspecific predictions for the CIMMYT ME clusters and clusters defined by the four clustering methods described above. Four statistical models were tested, the first ignores G×E, the second and third are different models that account for G×E using data from all clusters, and the fourth performs a separate analysis for each cluster. Parentheses indicate nesting. The term trial is defined by a locationyear-cluster combination. This distinction is needed because some locations planted two nurseries in the same year, one irrigated (ME1) and one rain-fed (ME4).
Data-driven clustering using all four methods was done separately for each training dataset before models were fit. 1) global (GL): makes one genomic prediction over all clusters for each genotype: yield = block(rep) + rep(trial) + trial + cluster + loc(cluster) + GID + error 2) interaction (IN): fits a main effect of the genotype and a cluster by genotype interaction effect to make a genomic prediction for each genotype in each cluster, with the estimated covariance for the cluster as the identity matrix: yield = block(rep) + rep(trial) + trial + cluster + loc(cluster) + GID(cluster) + error 3) factor analytic (FA): uses all data and the estimated covariances among cluster to fit the GID(cluster) term and make a genomic prediction for each genotype in each cluster with the estimated covariance for the cluster estimated from the data: yield = block(rep) + rep(trial) + trial + cluster + loc(cluster) + GID(fa[cluster]) + error 4) specific (SP): uses data from each cluster separately to make a genomic prediction for each genotype in each cluster: yield = block(rep) + rep(trial) + trial + loc + GID + error (model fit separately within each cluster) The last six years of the  SAWYT (2004 SAWYT ( -2009) )  were used as six separate validation datasets.
For each validation year, all years up to but not including the validation year were used as the training dataset. Locations in the validation dataset were matched to locations in the training dataset for assignment to clusters. ASReml  (Butler, 2009)  was used to make genomic predictions for each genotype using all years prior to the validation year as the training dataset. All effects were considered random, and the covariance among genotypes (GID) was always proportional to A m. Differences among model accuracies for SAWYT ME and data-driven clusters were analyzed using SAS PROC MIXED (SAS Institute Inc, 2012). For the CIMMYT ME the comparison of accuracies was done with the model accuracy = Model + ME + Model * ME + Year + Year * ME + error, with Model {GL,  IN, SP, FA} and Year = validation year {2004, 2005 , 2006 , 2007 , 2008, 2009}.
Simulation of multi-environment trial data Because of the high level of unbalance in the SAWYT dataset and the low ratio of genetic to environmental variance, the effectiveness of dividing the data into target clusters in the SAWYT may not be representative of other breeding programs across multiple target environments. To look at the relative performance of global vs. cluster-specific predictions, simulated data were used based on the empirical data from the SAWYT. The genotypes used for the simulation come from the last five years of the  SAWYT (2005 SAWYT ( -2009, N = 208), N = 208). Parameters varied were 1) the number of years each genotype was present, 2) the genetic correlation between target clusters, and 3) the ratio of genetic to environmental variances, using variance components calculated from the SAWYT data as a baseline.
Every prediction was based on 80% of the lines, corresponding to four years of simulated data, and validated using simulated true breeding values from the remaining year. All five years were used for validation, so for every combination of parameters in the simulation there were five validation experiments and 50 replicates. Further details on the simulation are provided in Appendix. The models used to predict breeding values were the same as described above for the actual data (GL, IN, SP, FA), with the exception that accuracy was calculated as the correlation between the true breeding value from the simulation and the genomic prediction. SAS PROC MIXED and Tukey's multiple comparison procedure were used to test for differences among the prediction models and for the effects of the three parameters that were varied.
Clustering of similar locations Clustering based on the correlation of genomic predictions across locations grouped different locations together and did not correlate with the CIMMYT ME. A year by year analysis of the SAYWT data showed that the interaction between genotype and ME or cluster component of variance was always smaller than the variance due to the interaction of genotypes with locations within ME or cluster (Table  2 ). In many of the individual cycles, one or more of the clustering methods produced clusters that explained none of the variance, with all of the location variance being within rather than between clusters. As the clusters were developed with the full dataset, it is possible that within a year they explain little of the variation in the set of genotypes present that particular year.
3 ). Correlations between the ME-specific yield estimates and the global yield estimates were fairly high for the 6 validation years (Table  3 ), other than ME 6 in 2006, which was unusually low. ME4 has the highest number of locations and trials in the dataset, which contributes to its high correlation to the global yield estimates. The low correlation of ME6 to the global yield estimates is probably due to the fact that ME6 represents high latitude environments, and normally SAWYT lines do not adapt well because of photoperiod differences.
The IN model has an advantage for use with large datasets because of its significantly faster computing time than the FA model. The IN model also avoids problems of model convergence, which may occur when using the FA model on unbalanced datasets. The relative performance of the different models did not depend on the balance of genotypes over years, and accuracies were similar for the cases of fully balanced data (all genotypes present all four years in the training dataset) and completely unbalanced data (all genotypes only present one out of 4 years in the training dataset). This suggests that a relationship matrix calculated with genomic data can be used to tie together genotypically unbalanced datasets. However, genomic data may not be able to overcome the limitations of datasets that are highly unbalanced over environments.
Mega-environments in the SAWYT dataset It appears that the ME in the SAWYT are not distinct enough to justify sub-setting the international nursery to target genomic predictions of yield to specific environmental conditions or for use in training genomic prediction models. The global model validated on global estimated yields had the highest accuracy, followed by the global model validated on ME-specific estimated yields, and all models attempting to make genomic predictions for each ME. Given that in the simulations we saw a benefit to targeting genomic predictions to specific environments, if the SAWYT had true ME, with similar performance of genotypes within ME and fairly low levels of genetic correlation among ME, we would expect to see the GL model ranked last and the IN, FA or SP models performing the best.
We may ask whether 1) there actually are no true ME present in the SAWYT or 2) the nature of the dataset prevents us from effectively identifying and using existing ME for predictions. It seems unlikely that the wide range of global locations present in the SAWYT all represent the same target environment, but SAWYT genotypes were developed for broad international adaptation and it may not be possible to detect adaptation to specific ME with this set of genotypes. If there are true ME for yield in this set of genotypes, there are several possibilities for our failure to detect them. There is more genotype*location interaction within ME than genotype*ME interaction for any given year, and locations in the dataset may be misclassified as the cluster analysis did not group locations in the same CIMMYT ME together.
Data driven clusters did not improve predictions, so it may not be possible to improve on the current locational assignments. In addition, the lack of locational balance across years or the high levels of year-to-year variation within locations may mean it is not possible to meaningfully cluster locations based on genotypic performance. The highest yielding genotype may be different from one year to the next, or the changing set of locations combined with the changing set of genotypes may make it impossible to measure what the best genotype would be over years for a particular ME. If the ME division had significant explanatory power for genotype yields, so that there was less G×E among locations within ME than between ME, we would expect to see a benefit from modeling G×E, with consequent differences in accuracy between models that take G×E into account (IN or FA), or only use data from the individual MEs to train separate models for each ME (SP).