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

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

The ontologies community of practice: a CGIAR initiative for Big Data in agrifood systems

Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams

Genetic dissection of grain size and grain number trade-offs in CIMMYT wheat germplasm.

Grain weight (GW) and number per unit area of land (GN) are the primary components of grain yield in wheat. In segregating populations both yield components often show a negative correlation among themselves. Here we use a recombinant doubled haploid population of 105 individuals developed from the CIMMYT varieties Weebill and Bacanora to understand the relative contribution of these components to grain yield and their interaction with each other. Weebill was chosen for its high GW and Bacanora for high GN. The population was phenotyped in Mexico, Argentina, Chile and the UK. Two loci influencing grain yield were indicated on 1B and 7B after QTL analysis. Weebill contributed the increasing alleles. The 1B effect, which is probably caused by to the 1BL.1RS rye introgression in Bacanora, was a result of increased GN, whereas, the 7B QTL controls GW. We concluded that increased in GW from Weebill 7B allele is not accompanied by a significant reduction in grain number. The extent of the GW and GN trade-off is reduced. This makes this locus an attractive target for marker assisted selection to develop high yielding bold grain varieties like Weebill. AMMI analysis was used to show that the 7B Weebill allele appears to contribute to yield stability

Correlation of GRYLD, GRpsqm and TGRWT with heading date.

<p>Asterisks denote significance levels of correlations</p><p>0.05 (*)</p><p>0.01 (**)</p><p>0.001 (***)</p><p>Correlation of GRYLD, GRpsqm and TGRWT with heading date.</p

Chromosomal locations of QTL identified in Weebill x Bacanora.

<p>Chromosomes carrying QTL for the same trait identified in more than two environments are shown. Coloured bars represent QTL confidence interval. Shortened trait names are given in methods. Trait names are appended with names of environments in which they were detected (see Table C in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118847#pone.0118847.s001" target="_blank">S1 File</a>).</p

AMMI 2 plot.

<p>Principal components 1 and 2 from AMMI analysis of grain yield are plotted against each other. Genotypes closest to zero for PC1 and PC2 show the highest levels of yield stability. Genotypes carrying the Weebill (increasing) allele of the 7B grain size QTL are shown in dark blue.</p

Correlation coefficients of thousand grain weight (TGRWT) and grains per unit area of land (GRpsqm) with grain yield (GRYLD).

<p>Asterisks denote significance levels of correlations</p><p>0.05 (*)</p><p>0.01 (**)</p><p>0.001 (***)</p><p>Correlation coefficients of thousand grain weight (TGRWT) and grains per unit area of land (GRpsqm) with grain yield (GRYLD).</p

AMMI 1 plot of grain yield for Weebill, Bacanora, and WxB segregating population.

<p>See Tables A and B in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118847#pone.0118847.s001" target="_blank">S1 File</a> for full descriptions of environments. Yield is the mean of all environments shown. Genotype close to 0 for PC1 (Principal component 1) show low levels of environmental interaction for this trait. Proximity to an environment data point indicates that the genotype in question is relatively well adapted to that environment.</p