2018–2019 field seasons of the Maize Genomes to Fields (G2F) G x E project

Lima, Dayane Cristina; Aviles, Alejandro Castro; Alpers, Ryan Timothy; McFarland, Bridget A.; Kaeppler, Shawn M.; Ertl, David; Romay, M. Cinta; Gage, Joseph L.; Holland, James B.; Beissinger, Timothy; Bohn, Martin; Buckler, Edward S.; Edwards, Jode W.; Flint-García, Sherry; Hirsch, Candice N.; Hood, Elizabeth E.; Hooker, David C.; Knoll, J.; Kolkman, Judith M.; Liu, Sanzhen; McKay, John; Minyo, Richard; Moreta, Danilo; Murray, Seth C.; Nelson, Rebecca; Schnable, James C.; Sekhon, Rajandeep S.; Singh, Maninder P.; Thomison, Peter R.; Thompson, Addie; Tuinstra, Mitchell R.; Wallace, Jason G.; Washburn, Jacob D.; Weldekidan, Teclemariam; Wisser, Randall J.; Xu, Wenwei; León, Natalia de

doi:10.1186/s12863-023-01129-2

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…Pop1 consisted of 415 subset hybrids planted on 2018 March 6 and 2019 March 20, in College Station, Texas, USA. Further details about 2018 and 2019 G2F experiments can be found in Lima, Aviles, Alpers, McFarland, et al . (2023) .…”

Section: Methodsmentioning

confidence: 99%

Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize

Adak,

DeSalvio,

Arik

et al. 2024

G3: Genes, Genomes, Genetics

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Field-based phenomic prediction employs novel features, like vegetation indices (VIs) from drone images, to predict key agronomic traits in maize, despite challenges in matching biomarker measurement time points across years or environments. This study utilized functional principal component analysis (FPCA) to summarize the variation of temporal VIs, uniquely allowing the integration of this data into phenomic prediction models tested across multiple years (2018–2021) and environments. The models, which included 1 genomic, 2 phenomic, 2 multikernel, and 1 multitrait type, were evaluated in 4 prediction scenarios (CV2, CV1, CV0, and CV00), relevant for plant breeding programs, assessing both tested and untested genotypes in observed and unobserved environments. Two hybrid populations (415 and 220 hybrids) demonstrated the visible atmospherically resistant index’s strong temporal correlation with grain yield (up to 0.59) and plant height. The first 2 FPCAs explained 59.3 ± 13.9% and 74.2 ± 9.0% of the temporal variation of temporal data of VIs, respectively, facilitating predictions where flight times varied. Phenomic data, particularly when combined with genomic data, often were comparable to or numerically exceeded the base genomic model in prediction accuracy, particularly for grain yield in untested hybrids, although no significant differences in these models’ performance were consistently observed. Overall, this approach underscores the effectiveness of FPCA and combined models in enhancing the prediction of grain yield and plant height across environments and diverse agricultural settings.

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Section: Methodsmentioning

confidence: 99%

Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize

Adak,

DeSalvio,

Arik

et al. 2024

G3: Genes, Genomes, Genetics

Self Cite

View full text Add to dashboard Cite

show abstract

“…The 2018-2019 experiments utilized genetic materials with a relatively narrow maturity window; the hybrids resulted from the cross between doubled-haploids (DH) from a collection of three biparental populations (PHW65 x PHN11, PHW65 x MO44, and PHW65 x MOG) to the inbred testers LH195 and PHT69. In these two years all the hybrids were evaluated in a modified randomized complete block design 23 , 43 .…”

Section: Methodsmentioning

confidence: 99%

Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America

Lopez-Cruz,

Aguate,

Washburn

et al. 2023

Nat Commun

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Genotype-by-environment (G×E) interactions can significantly affect crop performance and stability. Investigating G×E requires extensive data sets with diverse cultivars tested over multiple locations and years. The Genomes-to-Fields (G2F) Initiative has tested maize hybrids in more than 130 year-locations in North America since 2014. Here, we curate and expand this data set by generating environmental covariates (using a crop model) for each of the trials. The resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids. We show how this valuable data set can serve as a benchmark in agricultural modeling and prediction, paving the way for countless G×E investigations in maize. We use multivariate analyses to characterize the data set’s genetic and environmental structure, study the association of key environmental factors with traits, and provide benchmarks using genomic prediction models.

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Ensemble of best linear unbiased predictor, machine learning and deep learning models predict maize yield better than each model alone

Kick,

Washburn

2023

In Silico Plants

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Predicting phenotypes accurately from genomic, environment, and management factors is key to accelerating the development of novel cultivars with desirable traits. Inclusion of management and environmental factors enables in silico studies to predict the effect of specific management interventions or future climates. Despite the value such models would confer, much work remains to improve the accuracy of phenotypic predictions. Rather than advocate for a single specific modeling strategy, here we demonstrate within large multi-environment and multi-genotype maize trials that combining predictions from disparate models using simple ensemble approaches most often results in better accuracy than using any one of the models on their own. We investigated various ensemble combinations of different model types, model numbers, and model weighting schemes to determine the accuracy of each. We find that ensembling generally improves performance even when combining only two models. The number and type of models included alter accuracy with improvements diminishing as the number of models included increases. Using a genetic algorithm to optimize ensemble composition reveals that, when weighted by the inverse of each model’s expected error, a combination of best linear unbiased predictor, linear fixed effects, deep learning, random forest, and support vector regression models performed best on this dataset.

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2018–2019 field seasons of the Maize Genomes to Fields (G2F) G x E project

Cited by 6 publications

References 4 publications

Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize

Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize

Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America

Ensemble of best linear unbiased predictor, machine learning and deep learning models predict maize yield better than each model alone

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