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Costa-Neto, G., Crespo-Herrera, L., Fradgley, N., Gardner, K. A., Bentley, A. R., Dreisigacker, S., Fritsche-Neto, R., Montesinos-López, O. A., & Crossa, J. (2022). Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data. G3: Genes, Genomes, Genetics, 13(2), jkac313. https://doi.org/10.1093/g3journal/jkac313
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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.
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Journal
G3: Genes, Genomes, Genetics
Journal volume
13
Journal issue
2
Article number
jkac313
Place of Publication
Bethesda, MD (USA)
Publisher
Genetics Society of America
Donor or Funder
Bill & Melinda Gates Foundation (BMGF)
Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AGG)
United States Agency for International Development (USAID)
CGIAR Research Program on Maize
CGIAR Research Program on Wheat
Foundation for Research Levy on Agricultural Products (FFL)
Agricultural Agreement Research Fund
Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AGG)
United States Agency for International Development (USAID)
CGIAR Research Program on Maize
CGIAR Research Program on Wheat
Foundation for Research Levy on Agricultural Products (FFL)
Agricultural Agreement Research Fund
Related Datasets
CGIAR
Initiative
Accelerated Breeding
Impact Area
Poverty reduction, livelihoods & jobs
Climate adaptation & mitigation
Climate adaptation & mitigation
Action Area
Genetic Innovation