2023
DOI: 10.1038/s41467-023-42687-4
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Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America

Marco Lopez-Cruz,
Fernando M. Aguate,
Jacob D. Washburn
et al.

Abstract: 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 env… Show more

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Cited by 10 publications
(7 citation statements)
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“…The data used in these benchmarks was generated by the Genomes to Fields (G2F) Initiative ( Lima et al 2023 ), which was curated and expanded by adding environmental covariates by Lopez-Cruz et al (2023) . This data set was used to derive a genetic (GRM) and an environmental relationship matrix (ERM, from the environmental covariates, see Supplementary Note 4 ) for 4 344 maize hybrids and 97 environments (year–locations), respectively, corresponding to the northern testing locations.…”
Section: Resultsmentioning
confidence: 99%
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“…The data used in these benchmarks was generated by the Genomes to Fields (G2F) Initiative ( Lima et al 2023 ), which was curated and expanded by adding environmental covariates by Lopez-Cruz et al (2023) . This data set was used to derive a genetic (GRM) and an environmental relationship matrix (ERM, from the environmental covariates, see Supplementary Note 4 ) for 4 344 maize hybrids and 97 environments (year–locations), respectively, corresponding to the northern testing locations.…”
Section: Resultsmentioning
confidence: 99%
“…Finally, we evaluated the performance of the approximation of provided by the tensorEVD method in Gaussian linear models in terms of variance component estimates and cross-validation prediction accuracies. For this evaluation, we used all the G2F data from the northern testing locations included in the data set presented by Lopez-Cruz et al (2023) . For the northern testing locations, this data set includes 59 069 records for 4 traits (grain yield, anthesis, silking, and anthesis–silking interval) from 4 344 hybrids and 97 environments.…”
Section: Resultsmentioning
confidence: 99%
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“…The response variable used in this study consisted of grain yield in Mg ha −1 at 15.5% grain moisture. More details on 2019, 2020, and 2021 genetic material are available at Lopez-Cruz et al (2023).…”
Section: Methodsmentioning
confidence: 99%
“…The genetic effects can also depend on other variants, either from the same genome [8,9] or the genome of another species (such as pathogen and host [10], mother and offspring [11]). Detection of such interaction effects can enhance the ability to identify genetic effects that would otherwise be reduced or masked [12]; they are considered as one of the reasons why results of marginal association studies are sometimes hard to replicate [13]; they are believed to account for a large part of missing heritability [14][15][16] ; and they help people better understand genetic architecture of complex traits and diseases [12,17,18] and benefit many areas such as public health [19] and agriculture [20,21]. Much previous work has been done to develop appropriate methods for detecting interactions in GWAS, aiming to improve computational efficiency, reduce false positives and increase power [4,[22][23][24][25][26][27][28][29].…”
Section: Introductionmentioning
confidence: 99%