Dataset Information

Genomes to Fields 2022 Maize genotype by Environment Prediction Competition.

ABSTRACT:

Objectives

The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data.

Data description

This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [2]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years.

SUBMITTER: Lima DC

PROVIDER: S-EPMC10353085 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genomes to Fields 2022 Maize genotype by Environment Prediction Competition.

Lima Dayane Cristina DC Washburn Jacob D JD Varela José Ignacio JI Chen Qiuyue Q Gage Joseph L JL Romay Maria Cinta MC Holland James J Ertl David D Lopez-Cruz Marco M Aguate Fernando M FM de Los Campos Gustavo G Kaeppler Shawn S Beissinger Timothy T Bohn Martin M Buckler Edward E Edwards Jode J Flint-Garcia Sherry S Gore Michael A MA Hirsch Candice N CN Knoll Joseph E JE McKay John J Minyo Richard R Murray Seth C SC Ortez Osler A OA Schnable James C JC Sekhon Rajandeep S RS Singh Maninder P MP Sparks Erin E EE Thompson Addie A Thompson Addie A Tuinstra Mitchell M Wallace Jason J Wallace Jason J Weldekidan Teclemariam T Xu Wenwei W de Leon Natalia N

BMC research notes 20230717 1

<h4>Objectives</h4>The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data.<h4>Data description</h4>This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations ...[more]

PMID: 37461058

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

Genomes to Fields 2022 Maize genotype by Environment Prediction Competition.

Objectives

Data description

Publications

Genomes to Fields 2022 Maize genotype by Environment Prediction Competition.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets