Enviromic prediction is useful to define the limits of climate adaptation: A case study of common bean in Brazil

Heinemann, A. B.; Costa‐Neto, Germano; Fritsche‐Neto, Roberto; Matta, David Henriques da; Fernandes, Igor Kuivjogi

doi:10.1016/j.fcr.2022.108628

Cited by 24 publications

(15 citation statements)

References 45 publications

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“…Because of this, for further analysis, we advocate the using all envirotyping covariables for creating the environmental relationship matrices – and consequently, the kernel-based reaction norms in genomic prediction. For modeling reaction-norms, here the PLS approach seems to simplify the diversity of reaction-norms too much; consequently, we suggest that in future studies, nonlinear approaches such as GAM (Heinemann et al, 2022) be used as an alternative approach. Despite this, the use of PLS-based genotype-specific coefficients of reaction-norm were successful in recovering the main trends of plasticity and leverage of the genomic prediction for future years in the historical data sets.…”

Section: Discussionmentioning

confidence: 99%

“…From the biological point of view, it seems reasonable to affirm that environmental covariables are non-additive between each other (Costa-Neto et al, 2021c). Also, environmental covariables have higher collinearity and a lack of orthogonality (Heinemann et al, 2022). Because of this, some studies have added the step of “variable selection” (e.g., Millet et al, 2019; Westhues et al, 2021; Mu et al, 2020), which could help in to overcome this issue but could cost a loss of information when trying to predict a yet-to-be-seen GxE, as will be discussed in further sections of this paper.…”

Section: Discussionmentioning

confidence: 99%

“…The theoretical basis of the EPA modeling approach assumes that the genotypic response over the environmental variation, and consequently the emergent G×E interaction observed in the experimental network, is strictly related to the particular envirotype-to-phenotype dynamics observed for each genotype and its evaluated growing conditions. Thus, the core of the reaction-norm not only determines the main drivers of the G×E (Millet et al, 2019; Costa-Neto et al, 2020a; Porker et al, 2020; Heinemann et al, 2022), but also explains how the environmental conditions have shaped the phenotypic correlations among experimental sites. Because of this property, here we used EPA as an intermediary step to adapt the envirotyping data (covariables) into environmental weights of similarity, while also exploring particular genotype responses for those factors (that is, reaction-norms) as a secondary source of genetic variation to approach genomics and the observed G×E.…”

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confidence: 99%

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Envirome-Wide Associations Enhance Multi-Year Genome-Based Prediction of Historical Wheat Breeding Data

Costa‐Neto

Crespo‐Herrera

Fradgley

et al. 2022

Preprint

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Linking high-throughput environmental data (enviromics) into genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (GxE). This study developed a data-driven approach based on Environment-Phenotype Associations (EPA) aimed at recycling important GxE information from historical breeding data. EPA was developed in two applications: (1) scanning a secondary source of genetic variation, weighted from the shared reaction norms of past-evaluated genotypes; (2) pinpointing weights of the similarity among trial-sites (locations), given the historical impact of each envirotyping data variable for a given site. Then, the EPA outcomes were integrated into multi-environment GP models through a new single-step GBLUP. The wheat trial data used included 36 locations, 8 years and 3 target populations of environments (TPE) in India. Four prediction scenarios and 6 kernel-models within/across TPEs were tested. Our results suggest that the conventional GBLUP, without enviromic data or when omitting EPA, is inefficient in predicting the performance of wheat lines in future years. However, when EPA was introduced as an intermediary learning step to reduce the dimensionality of the GxE kernels while connecting phenotypic and environmental-wide variation, a significant enhancement of GxE prediction accuracy was evident. EPA revealed that the effect of seasonality makes strategies such as 'covariable selection' unfeasible because GxE is year-germplasm specific. We propose that the EPA effectively serves as a 'reinforcement learner' algorithm capable of uncovering the effect of seasonality over the reaction norms, with the benefits of better forecasting the similarities between past and future trialing sites. EPA combines the benefits of dimensionality reduction while reducing the uncertainty of genotype-by-year predictions and increasing the resolution of GP for the genotype-specific level.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Envirome-Wide Associations Enhance Multi-Year Genome-Based Prediction of Historical Wheat Breeding Data

Costa‐Neto

Crespo‐Herrera

Fradgley

et al. 2022

Preprint

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“…For trials with no weather stations available in the municipality, we used daily climate data from NASA POWER (Sparks, 2018) and the R package EnvRtype available at https://github.com/allogamous/EnvRtype). Heinemann et al (2022) also applied this process.…”

Section: Envirotyping the Ir Growing Conditionsmentioning

confidence: 99%

Environmental clusters defining breeding zones for tropical irrigated rice in Brazil

Costa‐Neto,

Matta,

Fernandes

et al. 2023

Agronomy Journal

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Geographic and seasonal effects are important in driving selection decisions in rice breeding research. Adopting new strategies for characterizing environmental‐phenotype associations is critical to understanding these effects, and the outcomes of their study could reflect the benefits of developing locally adapted cultivars. This study aimed to characterize Brazil's tropical irrigated rice environment, Latin America's largest rice production system. We integrated unsupervised (K‐means clustering) and supervised (decision tree classifier, DTC) algorithms to identify environmental clusters (EC) based on historical yield data. The dataset included 31 locations and 471 genotypes from 1982 to 2017. We used environmental features (EF), such as weather and geography, as input variables for our analysis, assuming the model as EC ∼ f (EF). Results indicate that the tropical irrigated rice production region can be divided into four primary breeding zones, with temperature emerging as a significant factor in the study area. After employing a linear mixed model (LMM) analysis, we observed that the current relationship between genetics (G), environmental variation (E), and their interaction (GE) in Brazil's tropical irrigated rice has a 1:6:2 ratio. However, when introducing our data‐driven model based on environmental clusters, we reduced this ratio to 1:5:1. Therefore, the selection for local adaptability across a large region became more reliable. Our approach successfully identified environmental clusters in Brazil's tropical production region of irrigated rice, providing valuable insights for defining breeding zones and identifying more productive and stable seed production fields.This article is protected by copyright. All rights reserved

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“…Researchers can better understand the environmental factors influencing plant traits by including these variables in breeding studies. This knowledge can then be used to develop better adapted crops to specific soil conditions, leading to improved crop productivity and resilience (Bryan et al., 2022). In this way, the incorporation of soil‐derived covariates has the potential to revolutionize plant breeding and contribute to global food security.…”

Section: Introductionmentioning

confidence: 99%

SoilType: An R package to interplay soil characterization in plant science

Fritsche‐Neto

2023

Agronomy Journal

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Yield is a complex quantitative trait whose expression is sensitive to environmental stimuli. Therefore, soil‐related information can increase the predictive ability of genotype's performances across different locations. However, soil information is not always readily available worldwide or before the site or plot level growing season. Thus, in the current version, this tool has two functions. The first function retrieves soil samples and soil (from WoSIS Soil Profile Database) near your target location. Then it predicts 13 soil characteristics (physical and chemical). If the number of samples per location is greater than five, the function uses random forest to predict soil characteristics otherwise it averages the information. The output is a table with the target location and its latitude and longitude coordinates—the number of samples used, the root mean square error (RMSE), and the R‐square for each prediction. From a couple of instances in a trial (location), the second function predicts soil characteristics at the plot level via random forest. The output is a table with the plot IDs, coordinates, number of samples used to make predictions, the RMSE and R‐square for each prediction, the trait predicted, and the predicted value. As a proof‐of‐concept, we used the first function in the LSU Rice Breeding multi‐environmental trials (24 locations), identified the most important soil covariates to determine rice yield, and then clustered the locations. This tool can support breeders in better allocating trials in advance, borrow information from other regions, identify the best variety for each location, reduce costs, and increase accuracy.

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Enviromic prediction is useful to define the limits of climate adaptation: A case study of common bean in Brazil

Cited by 24 publications

References 45 publications

Envirome-Wide Associations Enhance Multi-Year Genome-Based Prediction of Historical Wheat Breeding Data

Envirome-Wide Associations Enhance Multi-Year Genome-Based Prediction of Historical Wheat Breeding Data

Environmental clusters defining breeding zones for tropical irrigated rice in Brazil

SoilType: An R package to interplay soil characterization in plant science

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