Dataset Information

Maize genomes to fields (G2F): 2014-2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets.

ABSTRACT: OBJECTIVES:Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F's genotype-by-environment (G?×?E) project. This report covers the public release of datasets for 2014-2017. DATA DESCRIPTION:Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.

SUBMITTER: McFarland BA

PROVIDER: S-EPMC7017475 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Maize genomes to fields (G2F): 2014-2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets.

McFarland Bridget A BA AlKhalifah Naser N Bohn Martin M Bubert Jessica J Buckler Edward S ES Ciampitti Ignacio I Edwards Jode J Ertl David D Gage Joseph L JL Falcon Celeste M CM Flint-Garcia Sherry S Gore Michael A MA Graham Christopher C Hirsch Candice N CN Holland James B JB Hood Elizabeth E Hooker David D Jarquin Diego D Kaeppler Shawn M SM Knoll Joseph J Kruger Greg G Lauter Nick N Lee Elizabeth C EC Lima Dayane C DC Lorenz Aaron A Lynch Jonathan P JP McKay John J Miller Nathan D ND Moose Stephen P SP Murray Seth C SC Nelson Rebecca R Poudyal Christina C Rocheford Torbert T Rodriguez Oscar O Romay Maria Cinta MC Schnable James C JC Schnable Patrick S PS Scully Brian B Sekhon Rajandeep R Silverstein Kevin K Singh Maninder M Smith Margaret M Spalding Edgar P EP Springer Nathan N Thelen Kurt K Thomison Peter P Tuinstra Mitchell M Wallace Jason J Walls Ramona R Wills David D Wisser Randall J RJ Xu Wenwei W Yeh Cheng-Ting CT de Leon Natalia N

BMC research notes 20200212 1

<h4>Objectives</h4>Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly av ...[more]

PMID: 32051026

Similar Datasets

Project description:BACKGROUND:Influenza epidemiological and virologic data from Georgia are limited. We aimed to present Influenza Like Illness (ILI) and Severe Acute Respiratory Infection (SARI) surveillance data and characterize influenza viruses circulating in the country over three influenza seasons. METHODS:We analyzed sentinel site ILI and SARI data for the 2014-2017 seasons in Georgia. Patients' samples were screened by real-time RT-PCR and influenza viruses isolated were characterized antigenically by haemagglutination inhibition assay and genetically by sequencing of HA and NA genes. RESULTS:32% (397/1248) of ILI and 29% (581/1997) of SARI patients tested were positive for influenza viruses. In 2014-2015 the median week of influenza detection was week 7/2015 with B/Yamagata lineage viruses dominating (79%); in 2015-2016-week 5/2016 was the median with A/H1N1pdm09 viruses prevailing (83%); and in 2016-2017 a bimodal distribution of influenza activity was observed-the first wave was caused by A/H3N2 (55%) with median week 51/2016 and the second by B/Victoria lineage viruses (45%) with median week 9/2017. For ILI, influenza virus detection was highest in children aged 5-14 years while for SARI patients most were aged >15 years and 27 (4.6%) of 581 SARI cases died during the three seasons. Persons aged 30-64 years had the highest risk of fatal outcome, notably those infected with A/H1N1pdm09 (OR 11.41, CI 3.94-33.04, p<0.001). A/H1N1pdm09 viruses analyzed by gene sequencing fell into genetic groups 6B and 6B.1; A/H3N2 viruses belonged to genetic subclades 3C.3b, 3C.3a, 3C.2a and 3C.2a1; B/Yamagata lineage viruses were of clade 3 and B/Victoria lineage viruses fell in clade1A. CONCLUSION:In Georgia influenza virus activity occurred mainly from December through March in all seasons, with varying peak weeks and predominating viruses. Around one third of ILI/ SARI cases were associated with influenza caused by antigenically and genetically distinct influenza viruses over the course of the three seasons.

Project description:Heterosis, or hybrid vigor, contributes to superior agronomic performance of hybrids compared to their inbred parents. Despite its importance, little is known about the genetic and molecular basis of heterosis. Early maize ear inflorescences formation affects grain yield, and are thus an excellent model for molecular mechanisms involved in heterosis. To determine the parental contributions and their regulation during maize ear-development-genesis, we analyzed genome-wide digital gene expression profiles in two maize elite inbred lines (B73 and Mo17) and their F1 hybrid using deep sequencing technology. Our analysis revealed 17,128 genes expressed in these three genotypes and 22,789 genes expressed collectively in the present study. Approximately 38% of the genes were differentially expressed in early maize ear inflorescences from heterotic cross, including many transcription factor genes and some presence/absence variations (PAVs) genes, and exhibited multiple modes of gene action. These different genes showing differential expression patterns were mainly enriched in five cellular component categories (organelle, cell, cell part, organelle part and macromolecular complex), five molecular function categories (structural molecule activity, binding, transporter activity, nucleic acid binding transcription factor activity and catalytic activity), and eight biological process categories (cellular process, metabolic process, biological regulation, regulation of biological process, establishment of localization, cellular component organization or biogenesis, response to stimulus and localization). Additionally, a significant number of genes were expressed in only one inbred line or absent in both inbred lines. Comparison of the differences of modes of gene action between previous studies and the present study revealed only a small number of different genes had the same modes of gene action in both maize seedlings and ear inflorescences. This might be an indication that in different tissues or developmental stages, different global expression patterns prevail, which might nevertheless be related to heterosis. Our results support the hypotheses that multiple molecular mechanisms (dominance and overdominance modes) contribute to heterosis.

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

Maize genomes to fields (G2F): 2014-2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets.

Publications

Maize genomes to fields (G2F): 2014-2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets