title: Validation and discovery of genotype-phenotype associations in chronic diseases using linked data.
abstract: This study investigates federated SPARQL queries over Linked Open Data (LOD) in the Semantic Web to validate existing, and potentially discover new genotype-phenotype associations from public datasets. In particular, we report our preliminary findings for identifying such associations for commonly occurring chronic diseases using the Online Mendelian Inheritance in Man (OMIM) and Database for SNPs (dbSNP) within the LOD knowledgebase and compare them with Gene Wiki for coverage and completeness. Our results indicate that Semantic Web technologies can play an important role for in-silico identification of novel disease-gene-SNP associations, although additional verification is required before such information can be applied and used effectively.
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA. pathak.jyotishman@mayo.edu
authors.forename: Jyotishman
authors.initials: J
authors.lastname: Pathak
authors.forename: Richard
authors.initials: R
authors.lastname: Kiefer
authors.forename: Robert
authors.initials: R
authors.lastname: Freimuth
authors.forename: Christopher
authors.initials: C
authors.lastname: Chute
mesh_terms: D002908:Chronic Disease / Q000145:classification / Q000453:epidemiology*; D057225:Data Mining / Q000379:methods*; D003628:Database Management Systems; D030541:Databases, Genetic; D062665:Epidemiological Monitoring; D056726:Genetic Association Studies / Q000379:methods; D020022:Genetic Predisposition to Disease / Q000453:epidemiology* / Q000235:genetics*; D005838:Genotype; D008498:Medical Record Linkage / Q000379:methods*