Background: The amount of data generated from genome-wide association studies (GWAS) has grown rapidly, but considerations for GWAS phenotype data reuse and interchange have not kept pace. This impacts on the work of GWAS Central -a free and open access resource for the advanced querying and comparison of summary-level genetic association data. The benefits of employing ontologies for standardising and structuring data are widely accepted. The complex spectrum of observed human phenotypes (and traits), and the requirement for cross-species phenotype comparisons, calls for reflection on the most appropriate solution for the organisation of human phenotype data. The Semantic Web provides standards for the possibility of further integration of GWAS data and the ability to contribute to the web of Linked Data.
Beck et al. REF provided a nanopublication dataset on comprehensive genome-wide association studies (GWAS) to organize and annotate the complex spectrum of observed human GWAS phenotypes for reuse and interchange, to assist with cross -species genotype and phenotype comparisons, and to integrate GW AS data into the Linked Data Web.
Semantically enabling a genome-wide association study database
Journal of Biomedical Semantics
Journal of Biomedical Semantics
Computer Science Medicine