Dataset Information

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

ABSTRACT:

Objectives

This report provides information about the public release of the 2018-2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines across multiple environments and makes available phenotypic, genotypic, environmental, and metadata information. The initiative understands the necessity to characterize and deploy public sources of genetic diversity to face the challenges for more sustainable agriculture in the context of variable environmental conditions.

Data description

Datasets include phenotypic, climatic, and soil measurements, metadata information, and inbred genotypic information for each combination of location and year. Collaborators in the G2F initiative collected data for each location and year; members of the group responsible for coordination and data processing combined all the collected information and removed obvious erroneous data. The collaborators received the data before the DOI release to verify and declare that the data generated in their own locations was accurate. ReadMe and description files are available for each dataset. Previous years of evaluation are already publicly available, with common hybrids present to connect across all locations and years evaluated since this project's inception.

SUBMITTER: Lima DC

PROVIDER: S-EPMC10214680 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Lima Dayane Cristina DC Aviles Alejandro Castro AC Alpers Ryan Timothy RT McFarland Bridget A BA Kaeppler Shawn S Ertl David D Romay Maria Cinta MC Gage Joseph L JL Holland James J Beissinger Timothy T Bohn Martin M Buckler Edward E Edwards Jode J Flint-Garcia Sherry S Hirsch Candice N CN Hood Elizabeth E Hooker David C DC Knoll Joseph E JE Kolkman Judith M JM Liu Sanzhen S McKay John J Minyo Richard R Moreta Danilo E DE Murray Seth C SC Nelson Rebecca R Schnable James C JC Sekhon Rajandeep S RS Singh Maninder P MP Thomison Peter P Thompson Addie A Tuinstra Mitchell M Wallace Jason J Washburn Jacob D JD Weldekidan Teclemariam T Wisser Randall J RJ Xu Wenwei W de Leon Natalia N

BMC genomic data 20230525 1

<h4>Objectives</h4>This report provides information about the public release of the 2018-2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines across multiple environments and makes available phenotypic, genotypic, environmental, and metadata information. The initiative understands the necessity to characterize and deploy public sources of genetic diversity to face the challenges for more sustainab ...[more]

PMID: 37231352

Similar Datasets

Project description:Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.

Dataset Information

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

Objectives

Data description

Publications

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

Similar Datasets

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