Maize genomes to fields (G2F): 2014–2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets (original) (raw)
Related papers
BMC research notes, 2018
Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F's genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are re...
MaizeGDB 2018: the maize multi-genome genetics and genomics database
Nucleic Acids Research, 2018
Since its 2015 update, MaizeGDB, the Maize Genetics and Genomics database, has expanded to support the sequenced genomes of many maize inbred lines in addition to the B73 reference genome assembly. Curation and development efforts have targeted high quality datasets and tools to support maize trait analysis, germplasm analysis, genetic studies, and breeding. MaizeGDB hosts a wide range of data including recent support of new data types including genome metadata, RNA-seq, proteomics, synteny, and large-scale diversity. To improve access and visualization of data types several new tools have been implemented to: access large-scale maize diversity data (SNPversity), download and compare gene expression data (qTeller), visualize pedigree data (Pedigree Viewer), link genes with phenotype images (MaizeDIG), and enable flexible user-specified queries to the MaizeGDB database (MaizeMine). MaizeGDB also continues to be the community hub for maize research, coordinating activities and providing technical support to the maize research community. Here we report the changes MaizeGDB has made within the last three years to keep pace with recent software and research advances, as well as the pan-genomic landscape that cheaper and better sequencing technologies have made possible. MaizeGDB is accessible online at https://www.maizegdb.org.
Plant, Cell & Environment, 2019
A challenge to improve an integrative phenotype, like yield, is the interaction between the broad range of possible molecular and physiological traits that contribute to yield and the multitude of potential environmental conditions in which they are expressed. This study collected data on 31 phenotypic traits, 83 annotated metabolites, and nearly 22,000 transcripts from a set of 57 diverse, commercially relevant maize hybrids across three years in central U.S. Corn Belt environments. Although variability in characteristics created a complex picture of how traits interact produce yield, phenotypic traits and gene expression were more consistent across environments, while metabolite levels showed low repeatability. Phenology traits, such as green leaf number and grain moisture and whole plant nitrogen content showed the most consistent correlation with yield. A machine learning predictive analysis of phenotypic traits revealed that ear traits, phenology, and root traits were most important to predicting yield. Analysis suggested little correlation between biomass traits and yield, suggesting there is more of a sink limitation to yield under the conditions studied here. This work suggests that continued improvement of maize yields requires a strong understanding of baseline variation of plant characteristics across commercially-relevant germplasm to drive strategies for consistently improving yield.
The performance of numerical, statistical, and data-driven diagnostic and predictive crop production modeling heavily relies on data quality for input and calibration/validation processes. This study presents a comprehensive database and the analytics used to consolidate it as a homogeneous, consistent, and multi-dimensional genotype, phenotypic, and environmental database for maize phenotype modeling, diagnostics, and prediction. The data used is obtained from the Genomes to Fields (G2F) initiative, which provides multi-year genomic (G), environmental (E), and phenotypic (P) datasets that can be used to train and test crop growth models to understand the genotype by environment (GxE) interaction phenomenon. A particular advantage of the G2F database is its diverse set of maize genotype DNA sequences (G2F-G), phenotypic measurements (G2F-P), station-based environmental time series (mainly, climatic data) observations collected during the maize growing season (G2F-E), and metadata for each field trials (G2F-M) across the U.S. and the province of Ontario in Canada. The construction of this comprehensive climate and genomic database incorporates the analytics for data quality control (QC) and consistency control (CC) to consolidate the digital representation of geospatially distributed environmental and genomic data required for phenotype predictive analytics and modeling the GxE interaction. The two-phase QC-CC pre-processing algorithm also includes a module to estimate environmental uncertainties. Generally, this data pipeline collects raw files, checks their formats, corrects data structures, and identifies and cures/imputes missing data. This pipeline uses machine learning techniques to fulfill the environmental time series gaps and quantifies the uncertainty introduced by using other data sources for gaps imputation in G2F-E, discards the missing values in G2F-P, and removes rare variants in G2F-G. Finally, an integrated and enhanced multi-dimensional database is generated. The analytics for improving the G2F database and the improved database called "CLIM4OMICS" follows the FAIR principles, and all the digital resources are
Panzea: a database and resource for molecular and functional diversity in the maize genome
2006
Serving as a community resource, Panzea (http:// www.panzea.org) is the bioinformatics arm of the Molecular and Functional Diversity in the Maize Genome project. Maize, a classical model for genetic studies, is an important crop species and also the most diverse crop species known. On average, two randomly chosen maize lines have one singlenucleotide polymorphism every 100 bp; this divergence is roughly equivalent to the differences between humans and chimpanzees. This exceptional genotypic diversity underlies the phenotypic diversity maize needs to be cultivated in a wide range of environments. The Molecular and Functional Diversity in the Maize Genome project aims to understand how selection has shaped molecular diversity in maize and then relate molecular diversity to functional phenotypic variation. The project will screen 4000 loci for the signature of selection and create a wide range of maize and maize-teosinte mapping populations. These populations will be genotyped and phenotyped, permitting high-power and high-resolution dissection of the traits and relating the molecular diversity to functional variation. Panzea provides access to the genotype, phenotype and polymorphism data produced by the project through user-friendly web-based database searches and data retrieval/visualization tools, as well as a wide variety of information and services related to maize diversity.
2002
Division of Biological Sciences (K.C.C.) and Department of Agronomy (M.D.M., I.V.B., G.L.D., Y.-S.Y., J.M.G., M.L.P., H.S.-V., Z.F., S.G.S., S.A.H., E.H.C.), University of Missouri, Columbia, Missouri 65211; Agricultural Research Service, United States Department of Agriculture, Columbia, Missouri 65211 (M.D.M., M.L.P., E.H.C.); Center for Applied Genetic Technologies, Departments of Crop and Soil Science, Botany, and Genetics, University of Georgia, Athens, Georgia 30602 (J.E.B., A.H.P.); and Arizona Genomics Computational Laboratory (C.A.S., F.W.E.) and Arizona Genomics Institute (R.A.W.), University of Arizona, Tucson, Arizona 85721
Maize Breeding: From Domestication to Genomic Tools
Agronomy
Maize will continue to expand and diversify as an industrial resource and a feed and fuel crop in the near future. The United Nations estimate that in 2050 the global population will reach 9.7 billion people. In this context, food security is increasingly being discussed. Additionally, another threat to food security is global warming. It is predicted that both the quantity and the quality of crops will be seriously affected by climate change in the near future. Scientists and breeders need to speed up the process of creating new maize cultivars that are resistant to climate stress without diminishing yield or quality. The present paper provides a brief overview of some of the most important genomics tools that can be used to develop high-performance and well-adapted hybrids of maize and also emphasizes the contribution of bioinformatics to an advanced maize breeding. Genomics tools are essential for a precise, fast, and efficient breeding of crops especially in the context of clima...
Comparative population genomics of maize domestication and improvement
Nature genetics, 2012
Domestication and plant breeding are ongoing 10,000-year-old evolutionary experiments that have radically altered wild species to meet human needs. Maize has undergone a particularly striking transformation. Researchers have sought for decades to identify the genes underlying maize evolution 1, 2, but these efforts have been limited in scope. Here, we report a comprehensive assessment of the evolution of modern maize based on the genome-wide resequencing of 75 wild, landrace and improved maize lines 3. We find ...