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Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data

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Title Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data
 
Creator Costa-Neto, Germano
Crespo-Herrera, Leonardo A.
Fradgley, Nick
Gardner, Keith
Bentley, Alison R.
Dreisigacker, Susanne
Fritsche-Neto, Roberto
Montesinos-López, Osval A.
Crossa, Jose
 
Subject marker-assisted selection
climate change
wheat
breeding
adaptability
environment
 
Description Linking high-throughput environmental data (enviromics) to genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (G × E). This study developed a data-driven approach based on Environment-Phenotype Associations (EPA) aimed at recycling important G × E 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. These results were then used as a dimensionality reduction strategy, integrating historical data to feed multi-environment GP models, which led to development of four new G × E kernels considering genomics, enviromics and EPA outcomes. The wheat trial data used included 36 locations, eight years and three target populations of environments (TPE) in India. Four prediction scenarios and six 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. Nevertheless, when EPA was introduced as an intermediary learning step to reduce the dimensionality of the G × E kernels while connecting phenotypic and environmental-wide variation, a significant enhancement of G × E prediction accuracy was evident. EPA revealed that the effect of seasonality makes strategies such as “covariable selection” unfeasible because G × E 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.
 
Date 2022-12-01
2023-01-03T12:05:51Z
2023-01-03T12:05:51Z
 
Type Journal Article
 
Identifier Costa-Neto, G., Crespo-Herrera, L., Fradgley, N., Gardner, K., Bentley, A.R., Dreisigacker, S., Fritsche-Neto, R., Montesinos-López, O.A. and Crossa, J. 2022. Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data. G3 Genes|Genomes|Genetics jkac313. https://hdl.handle.net/10883/22364
2160-1836
https://hdl.handle.net/10568/126482
https://hdl.handle.net/10883/22364
https://doi.org/10.1093/g3journal/jkac313
 
Language en
 
Rights OGL-UK-3.0
Open Access
 
Format application/pdf
 
Source G3-Genes|Genomes|Genetics