Ontology-driven International Maize Information System (IMIS) for Phenotypic and Genotypic Data Exchange

The Consultative Group on International Agricultural Research (CGIAR; http://www.cgiar.org/) centres have developed the International Crop Information System (ICIS; http://www.icis.cgiar.org) for the management and integration of global information on genetic resources, and germplasm improvement for any crop. The Maize breeding programs at CIMMYT (http://beta.cimmyt.org/) have different software tools to manage phenotypic, genotypic, and environmental information for their experiments generated worldwide. These tools have the capacity of collecting information in the field, wet lab, and store it into different relational databases. The IMIS (http://imis.cimmyt.org/confluence/display/IMIS/Crop+Finder) is an implementation of the ICIS, which is a computerized database system for general, integrated management and utilization of genealogy, nomenclature, genetic, phenotypic and characterization data for maize. Data exchange within and between databases as well as retrieving information are often hampered by the variability of terms used to describe comparable objects. To overcome this problem, the Crop Ontology (CO) database (http://cropontology.org/) is developed. It provides controlled vocabulary sets for several economically important plant species and facilitates biocurators working in genebanks of plant genetic resources (PGR) and crop breeding data curation and annotation. The maize trait ontology is developed as one of subclasses of CO trait ontology providing standardized trait descriptions, scales and scale values implemented into the IMIS. This ontology-driven IMIS will allow researchers who wish to exploit comparative phenotypic and genotypic information of maize to elucidate functional aspects of each trait

Guidelines for creating crop-specific ontologies to annotate phenotypic data: version 2.1

The Crop Ontology Guidelines version 2.1 provide detailed information and numerous examples for the use of the Trait Dictionary Template v.5.2 to develop a high quality Trait Dictionary with trait and variables used for the annotation of crop phenotypic data. The guidelines were developed in collaboration with the Integrated Breeding Platform and CIMMYT in the context of the CGIAR Big Data Platform

Development of GCP Ontology for Sharing Crop Information

The Generation Challenge Programme (GCP – "http://www.generationcp.org":http://www.generationcp.org) is a globally distributed crop research consortium directed toward crop improvement through the application of comparative biology and genetic resources characterization to plant breeding. GCP adopted the development paradigm of a ‘model-driven architecture’ to achieve the interoperability and integration of diverse GCP data types that are available through distributed data sources and consumed by end-user data analysis tools. Its objective is to ensure semantic compatibility across the Consortium that will lead to the creation of robust global public goods from GCP research results. 

The GCP scientific domain model is an object model that encapsulates key crop science concepts and is documented using Unified Modeling Language (see GCP Models on "http://pantheon.generationcp.org/index.php":http://pantheon.generationcp.org/index.php). 

At the core of the GCP architecture is a scientific domain model, which is heavily parameterized with GCP-indexed ontology terms. The GCP-indexed ontology reuses established international standards where available, converts other publicly available controlled vocabularies into formally managed ontology, and develops novel ontology if no public vocabularies yet exist. General and crop-specific GCP ontologies are being developed by crop teams involving GCP and external scientific experts – in particular, for crop-specific ontology relating to plant anatomy, developmental stage, trait and phenotype for selected GCP crops. Crop ontologies are being developed for chickpea, maize, Musa, potato, rice, sorghum and wheat. The Bioversity crop descriptor lists already loaded into OBO format files provide the primary structure to develop the crop ontologies. Then, terms to be mapped to the ontologies are extracted from the crop databases where trait values have been stored by crop scientists. These sources allow the ontology teams to identify the most commonly used concept names and their interrelations. Experts validate the selection of keywords that will build the controlled vocabulary. 

These GCP ontologies will allow researchers and end users to query keywords related to traits, plant structure, growth stage, and molecular function, and link them to associated phenotyping and genotyping data sets including data on germplasm, crop physiology, geographic information, genes, QTL, etc. To reach that stage, the crop ontologies will be integrated into the data-entry user interface or data templates as picklists facilitating data annotation and submission of new terms. In addition, the GCP ontologies will be integrated with Plant Ontology (PO) and Gramene (Trait Ontology, TO; Environment Ontology, EO) to develop a common, internationally shared crop trait and anatomy ontology. The team will initiate collaboration with SONet (Scientific Observations Network) and OBOE (Extensible Observation Ontology), which proposed to integrate the GCP ontology as a study case.
The Open Biomedical Ontologies (OBO) edit tool has been used to develop the ontologies for rice, wheat and maize traits, which are currently available at "http://cropforge.org/projects/gcpontology/":http://cropforge.org/projects/gcpontology/ . The crop-specific work plans and ontologies related to other materials are published at "http://pantheon.generationcp.org":http://pantheon.generationcp.org. 
The development and curation of general-purpose ontologies will be continued and made available on the Pantheon and CropForge websites

Multifunctional crop trait ontology for breeders' data: field book, annotation, data discovery and semantic enrichment of the literature

The ‘Crop Ontology’ database we describe provides a controlled vocabulary for several economically important crops. It facilitates data integration and discovery from global databases and digital literature. This allows researchers to exploit comparative phenotypic and genotypic information of crops to elucidate functional aspects of traits

Mining alleles for tar spot complex resistance from CIMMYT's maize Germplasm Bank

The tar spot complex (TSC) is a devastating disease of maize (Zea mays L.), occurring in 17 countries throughout Central, South, and North America and the Caribbean, and can cause grain yield losses of up to 80%. As yield losses from the disease continue to intensify in Central America, Phyllachora maydis, one of the causal pathogens of TSC, was first detected in the United States in 2015, and in 2020 in Ontario, Canada. Both the distribution and yield losses due to TSC are increasing, and there is a critical need to identify the genetic resources for TSC resistance. The Seeds of Discovery Initiative at CIMMYT has sought to combine next-generation sequencing technologies and phenotypic characterization to identify valuable alleles held in the CIMMYT Germplasm Bank for use in germplasm improvement programs. Individual landrace accessions of the “Breeders' Core Collection” were crossed to CIMMYT hybrids to form 918 unique accessions topcrosses (F1 families) which were evaluated during 2011 and 2012 for TSC disease reaction. A total of 16 associated SNP variants were identified for TSC foliar leaf damage resistance and increased grain yield. These variants were confirmed by evaluating the TSC reaction of previously untested selections of the larger F1 testcross population (4,471 accessions) based on the presence of identified favorable SNPs. We demonstrated the usefulness of mining for donor alleles in Germplasm Bank accessions for newly emerging diseases using genomic variation in landraces

Early life child micronutrient status, maternal reasoning, and a nurturing household environment have persistent influences on child cognitive development at age 5 years: Results from MAL-ED

Background: Child cognitive development is influenced by early-life insults and protective factors. To what extent these factors have a long-term legacy on child development and hence fulfillment of cognitive potential is unknown. Objective: The aim of this study was to examine the relation between early-life factors (birth to 2 y) and cognitive development at 5 y. Methods: Observational follow-up visits were made of children at 5 y, previously enrolled in the community-based MAL-ED longitudinal cohort. The burden of enteropathogens, prevalence of illness, complementary diet intake, micronutrient status, and household and maternal factors from birth to 2 y were extensively measured and their relation with the Wechsler Preschool Primary Scales of Intelligence at 5 y was examined through use of linear regression. Results: Cognitive T-scores from 813 of 1198 (68%) children were examined and 5 variables had significant associations in multivariable models: mean child plasma transferrin receptor concentration (β: −1.81, 95% CI: −2.75, −0.86), number of years of maternal education (β: 0.27, 95% CI: 0.08, 0.45), maternal cognitive reasoning score (β: 0.09, 95% CI: 0.03, 0.15), household assets score (β: 0.64, 95% CI: 0.24, 1.04), and HOME child cleanliness factor (β: 0.60, 95% CI: 0.05, 1.15). In multivariable models, the mean rate of enteropathogen detections, burden of illness, and complementary food intakes between birth and 2 y were not significantly related to 5-y cognition. Conclusions: A nurturing home context in terms of a healthy/clean environment and household wealth, provision of adequate micronutrients, maternal education, and cognitive reasoning have a strong and persistent influence on child cognitive development. Efforts addressing aspects of poverty around micronutrient status, nurturing caregiving, and enabling home environments are likely to have lasting positive impacts on child cognitive development.publishedVersio

The Plant Ontology as a Tool for Comparative Plant Anatomy and Genomic Analyses

The Plant Ontology (PO;http://www.plantontology.org/" is a publicly available, collaborative effort to develop and maintain a controlled, structured vocabulary ('ontology') of terms to describe plant anatomy, morphology and the stages of plant development. The goals of the PO are to link (annotate) gene expression and phenotype data to plant structures and stages of plant development, using the data model adopted by the Gene Ontology. From its original design covering only rice, maize and Arabidopsis, the scope of the PO has been expanded to include all green plants. The PO was the first multispecies anatomy ontology developed for the annotation of genes and phenotypes. Also, to our knowledge, it was one of the first biological ontologies that provides translations (via synonyms) in non-English languages such as Japanese and Spanish. As of Release #18 (July 2012), there are about 2.2 million annotations linking PO terms to > 110,000 unique data objects representing genes or gene models, proteins, RNAs, germplasm and quantitative trait loci (QTLs) from 22 plant species. In this paper, we focus on the plant anatomical entity branch of the PO, describing the organizing principles, resources available to users and examples of how the PO is integrated into other plant genomics databases and web portals. We also provide two examples of comparative analyses, demonstrating how the ontology structure and PO-annotated data can be used to discover the patterns of expression of the LEAFY (LFY) and terpene synthase (TPS) gene homologs.Keywords: Plant anatomy, Terpene synthase, Bioinformatics, Comparative genomics, Genome annotation, Ontolog

Crop Ontology Governance and Stewardship Framework

A governance & stewardship framework for the Crop Ontology Project is required as this is a collaborative tool developed by a Community of Practice. Over the last 12 years of its existence, it has increased significantly in scope and use. Collecting and storing plant trait data and annotating the data with ontology terms is widely accepted by the crop science community to be critical to enable data interoperability and interexchange through tools such as the Breeding API (BrAPI). The Crop Ontology Community of Practice is organised around roles, curation principles and validation processes that require a formal description. A governance framework is defined by the various actors involved in the asset’s design, development and maintenance. It is complemented by a quality assurance process to ensure that trust levels, value creation, and sustainability objectives meet appropriate quality levels. The general principles underlying data governance are integrity, transparency, accountability and ownership, stewardship, standardization, change management and a robust data audit

Determinants of the urinary and serum metabolome in children from six European populations

Background Environment and diet in early life can affect development and health throughout the life course. Metabolic phenotyping of urine and serum represents a complementary systems-wide approach to elucidate environment–health interactions. However, large-scale metabolome studies in children combining analyses of these biological fluids are lacking. Here, we sought to characterise the major determinants of the child metabolome and to define metabolite associations with age, sex, BMI and dietary habits in European children, by exploiting a unique biobank established as part of the Human Early-Life Exposome project (http://www.projecthelix.eu). Methods Metabolic phenotypes of matched urine and serum samples from 1192 children (aged 6–11) recruited from birth cohorts in six European countries were measured using high-throughput 1H nuclear magnetic resonance (NMR) spectroscopy and a targeted LC-MS/MS metabolomic assay (Biocrates AbsoluteIDQ p180 kit). Results We identified both urinary and serum creatinine to be positively associated with age. Metabolic associations to BMI z-score included a novel association with urinary 4-deoxyerythronic acid in addition to valine, serum carnitine, short-chain acylcarnitines (C3, C5), glutamate, BCAAs, lysophosphatidylcholines (lysoPC a C14:0, lysoPC a C16:1, lysoPC a C18:1, lysoPC a C18:2) and sphingolipids (SM C16:0, SM C16:1, SM C18:1). Dietary-metabolite associations included urinary creatine and serum phosphatidylcholines (4) with meat intake, serum phosphatidylcholines (12) with fish, urinary hippurate with vegetables, and urinary proline betaine and hippurate with fruit intake. Population-specific variance (age, sex, BMI, ethnicity, dietary and country of origin) was better captured in the serum than in the urine profile; these factors explained a median of 9.0% variance amongst serum metabolites versus a median of 5.1% amongst urinary metabolites. Metabolic pathway correlations were identified, and concentrations of corresponding metabolites were significantly correlated (r > 0.18) between urine and serum. Conclusions We have established a pan-European reference metabolome for urine and serum of healthy children and gathered critical resources not previously available for future investigations into the influence of the metabolome on child health. The six European cohort populations studied share common metabolic associations with age, sex, BMI z-score and main dietary habits. Furthermore, we have identified a novel metabolic association between threonine catabolism and BMI of children