Semantically enabling a genome-wide association study database

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 compari...

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Vydáno v:Journal of biomedical semantics Ročník 3; číslo 1; s. 9
Hlavní autoři: Beck, Tim, Free, Robert C, Thorisson, Gudmundur A, Brookes, Anthony J
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 17.12.2012
Springer Nature B.V
BMC
Témata:
Abnormalities
Algorithms
Annotations
Automation
Biocompatibility
Bioinformatics
Blood pressure
Combinatorial Libraries
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Data Mining and Knowledge Discovery
Disease
Genome-wide association studies
Genomes
Genotype & phenotype
Genotypes
GWAS
Integration
Linked Data
Mathematics
Mathematics and Statistics
Medical Subject Headings-MeSH
Ontology
Phenotype
Phenotypes
Principles
RDF
Resource Description Framework-RDF
Semantic technologies in healthcare and life sciences 2012
Semantic web
Semantics
Signs and symptoms
Species
Studies
Terminology
Phenotype
GWAS
Ontology
RDF
ISSN:2041-1480, 2041-1480
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Popis
Shrnutí: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. Results A pragmatic consideration when applying phenotype ontologies to GWAS data is the ability to retrieve all data, at the most granular level possible, from querying a single ontology graph. We found the Medical Subject Headings (MeSH) terminology suitable for describing all traits (diseases and medical signs and symptoms) at various levels of granularity and the Human Phenotype Ontology (HPO) most suitable for describing phenotypic abnormalities (medical signs and symptoms) at the most granular level. Diseases within MeSH are mapped to HPO to infer the phenotypic abnormalities associated with diseases. Building on the rich semantic phenotype annotation layer, we are able to make cross-species phenotype comparisons and publish a core subset of GWAS data as RDF nanopublications. Conclusions We present a methodology for applying phenotype annotations to a comprehensive genome-wide association dataset and for ensuring compatibility with the Semantic Web. The annotations are used to assist with cross-species genotype and phenotype comparisons. However, further processing and deconstructions of terms may be required to facilitate automatic phenotype comparisons. The provision of GWAS nanopublications enables a new dimension for exploring GWAS data, by way of intrinsic links to related data resources within the Linked Data web. The value of such annotation and integration will grow as more biomedical resources adopt the standards of the Semantic Web.
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ISSN:2041-1480
2041-1480
DOI:10.1186/2041-1480-3-9