Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones

Pierre, Carolina Saint; Burgueño, Juan; Crossa, José; Dávila, Guillermo; López, Pedro Figueroa; Moya, Ernesto Solís; Moreno, Javier Ireta; Muela, V. M. Hernández; Villa, Víctor Manuel Zamora; Vikram, Prashant; Mathews, Ky L.; Sansaloni, Carolina; Sehgal, Deepmala; Jarquín, Diego; Wenzl, Peter; Singh, Sukhwinder

doi:10.1038/srep27312

Cited by 60 publications

(51 citation statements)

References 31 publications

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“…This CV2 validation strategy performed better than CV strategy CV1 (where lines have not been evaluated in any field trials) proposed by Burgueño et al (2012), when applied in multi-environment models (Jarquín et al 2014; López-Cruz et al 2015; Crossa et al 2016b; Saint Pierre et al 2016). …”

Section: Discussionmentioning

confidence: 84%

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction

Sousa

Cuevas

Couto

et al. 2017

G3 Genes|Genomes|Genetics

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Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied.

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Section: Discussionmentioning

confidence: 84%

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction

Sousa

Cuevas

Couto

et al. 2017

G3 Genes|Genomes|Genetics

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“…They assessed two scenarios: the first one was to predict the value of lines that were tested in some but not all environments, and the second one was to predict the value of new lines that were not tested in any environments (Jarquín et al, 2014). Their model worked better under the first scenario, which allowed borrowing information from one set of environments for the same line to predict its performance in a different set of environments, whereas the latter uses data from one set of lines to predict performance of a different line in different environments (Crossa et al, 2014; Jarquín et al, 2014, 2016; Saint‐Pierre et al, 2016). Cuevas et al (2017) reported that using models incorporating the ge term always produced higher GS accuracy than models without ge for one maize ( Zea mays L.) and four wheat CIMMYT data sets.…”

Section: Discussionmentioning

confidence: 99%

The Accuracy of Genomic Prediction between Environments and Populations for Soft Wheat Traits

et al. 2018

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Genomic selection (GS) uses training population (TP) data to estimate the value of lines in a selection population. In breeding, the TP and selection population are often grown in different environments, which can cause low prediction accuracy when the correlation of genetic effects between the environments is low. Subsets of TP data may be more predictive than using all TP data. Our objectives were (i) to evaluate the effect of using subsets of TP data on GS accuracy between environments, and (ii) to assess the accuracy of models incorporating marker × environment interaction (MEI). Two wheat (Triticum aestivum L.) populations were phenotyped for 11 traits in independent environments and genotyped with single‐nucleotide polymorphism markers. Within each population–trait combination, environments were clustered. Data from one cluster were used as the TP to predict the value of the same lines in the other cluster(s) of environments. Models were built using all TP data or subsets of markers selected for their effect and stability. The GS accuracy using all TP data was >0.25 for 9 of 11 traits. The between‐environment accuracy was generally greatest using a subset of stable and significant markers; accuracy increased up to 48% relative to using all TP data. We also assessed accuracy using each population as the TP and the other as the selection population. Using subsets of TP data or the MEI models did not improve accuracy between populations. Using optimized subsets of markers within a population can improve GS accuracy by reducing noise in the prediction data set.

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“…We measure prediction accuracy using cross-validation, where the training, validation and test sets are selected to enforce the realistic constraints (Figure 2c). As the performance measure for prediction accuracy, we follow the conventional approach, i.e., the Pearson correlation between the predicted and observed yields in the test set 8, 10–12 . This correlation is computed for each cross-validation fold in turn, and averaged over the test cases.…”

Section: Methodsmentioning

confidence: 99%

“…In genomic selection (GS) 9 , field trials are replaced with genomic predictions to speed-up plant breeding. We formulate an in silico experimental setup for GS in targeted breeding that, unlike existing works 7, 8, 10–12 , strictly satisfies all realistic constraints: test locations, years, and genotypes are all genuinely new (not part of the training set) and yields are predicted for the off-spring of the training set. In this setup, we demonstrate the feasibility of targeted breeding by investigating the accuracy of G×E prediction using environmental data including historical weather information but without in-season data .…”

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confidence: 99%

ModellingG×Ewith historical weather information improves genomic prediction in new environments

Gillberg

Marttinen

Mamitsuka

et al. 2017

Preprint

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Interaction between the genotype and the environment (G×E) has a strong impact on the 7 yield of major crop plants. Although influential, taking G×E explictily into account in plant 8 breeding has remained difficult. Recently G×E has been predicted from environmental and 9 genomic covariates, but existing works have not shown that generalization to new environ-10 ments and years without access to in-season data is possible and practical applicability re-11 mains unclear. Using data from a Barley breeding program in Finland, we construct an 12 in-silico experiment to study the viability of G×E prediction under practical constraints. We 13show that the response to the environment of a new generation of untested Barley cultivars 14 can be predicted in new locations and years using genomic data, machine learning and his-15 torical weather observations for the new locations. Our results highlight the need for models 16 of G×E: non-linear effects clearly dominate linear ones and the interaction between the soil 17 type and daily rain is identified as the main driver for G×E for Barley in Finland. Our study 18 implies that genomic selection can be used to capture the yield potential in G×E effects for 19 future growth seasons, providing a possible means to achieve yield improvements, needed for 20 1 . CC-BY-NC-ND 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/213231 doi: bioRxiv preprint first posted online Nov. 2, 2017; feeding the growing population. 21Global yield improvements are needed to feed the growing population the genotype and environment could be realised. While the importance of G×E for agronomic 25 performance is widely accepted, utilisation calls for methods that predict yields in new environ-26 ments, because actual experimental data, consisting of yields of plant variety candidates from yield 27 trials, will in practice be available from only a very limited number of environments. Importantly, 28prediction of a plant's response to a new environment cannot be based on weather data from the 29 growth season, as those will never be available at the time of prediction. 2. CC-BY-NC-ND 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/213231 doi: bioRxiv preprint first posted online Nov. 2, 2017; Figure 1: Outline of our approach. a) Precision breeding aims at producing varieties that are optimal for a specific environment. As compared to traditional breeding (b), targeted breeding aims at higher environmental adaptation, i.e., smaller target environments. Weather (microclimate) is a crucial driver for agronomic performance, but as it is unknown for future growth seasons, we use historical weather records (c) ...

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Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones

Cited by 60 publications

References 31 publications

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction

The Accuracy of Genomic Prediction between Environments and Populations for Soft Wheat Traits

ModellingG×Ewith historical weather information improves genomic prediction in new environments

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