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The work proposed in this study is an attempt to use Semantic Web technologies for integrating patient clinical data derived from Electronic Health Records (EHRs) with large-scale genomics data to study genotype-phenotype associations. This aim is achieved via:
RDF-based representation of clinical data from Mayo Clinic EHR systems exposed via multiple SPARQL endpoints
Patient demographics, diagnoses, procedures and medications
Coded with Meaningful Use terminologiesRDF-based representation of genetic data from Mayo Clinic biobank repository exposed via a SPARQL endpoint
Patient single nucleotide polymorphism (SNP) genotype data
Coded with gene and sequence ontologiesFederated SPARQL 1.1 queries integrating genotype data with patient clinical data
Perform a Phenome-Wide Association Study (PheWAS) that allows a systematic study of associations between a number of common genetic variations and variety of large number of clinical phenotypes
From Relational Data Model to RDF Mapping to Querying via SPARQLSELECT ?ClinicNumber ?DiagnosisWHERE { SERVICE <http://edison.mayo.edu:8890/sparql> { ?s1 snomedct:3982250 ?clinicId . ?s1 gc:mayogcid ?mayogcId . ?s2 snomedct:3982250 ?patientId . ?s2 so:SO_0000694 ?rsId . ?s2 so:SO_0001027 ?genotype . FILTER (?patientId =?clinicId ) } SERVICE <http://hsrdev02:8890/sparql> { ?s3 mclss: internalKey ?table1Key . ?s3 tmo:TMO_0031 ?Diagnosis . ?s4 mclss: internalKey ?table2Key . ?s4 snomedct:3982250 ?ClinicNumber. FILTER (?table1Key = ?table2Key ) . } FILTER(?ClinicNumber = ?mayogcid) . FILTER(regex(str(?rsId), "rs5219", "i")) . FILTER(regex(str(?genotype), “T:T", "i")) .}
@prefix rr: <http://www.w3.org/ns/r2rml#>.@prefix mayogc: <http://mayogc/>.@prefix snomedct: <http://purl.bioontology.org/ontology/SNOMEDCT#>.@prefix so: <http://purl.org/obo/owl/SO#>.mayogc:PatientsMap a rr:TriplesMapClass; rr:tableName "patients_hypothyroidism"; rr:subjectMap [ rr:template "http://patients/{clinicId}" ]; rr:predicateObjectMap [ rr:predicateMap [ rr:predicate snomedct:3982250]; rr:objectMap [ rr:column "clinicId" ] ]; rr:predicateObjectMap [ rr:predicateMap [ rr:predicate mayogc:mayogid ]; rr:objectMap [ rr:column "mayogid" ] ].
mayogc:GenesMap a rr:TriplesMapClass; rr:tableName "patient_genotypes"; rr:subjectMap [ rr:template "http://genes/{patientId}" ]; rr:predicateObjectMap [ rr:predicateMap [ rr:predicate snomedct:3982250]; rr:objectMap [ rr:column "patientId" ] ]; rr:predicateObjectMap [ rr:predicateMap [ rr:predicate so:SO_0000694 ]; rr:objectMap [ rr:column "rsId" ] ]; rr:predicateObjectMap [ rr:predicateMap [ rr:predicate so:SO_0001027 ]; rr:objectMap [ rr:column "genotype" ] ].
SNP-disease associations for T2DM
SNP rs5219 within the gene KCNJ11
Mining Genotype-Phenotype Associations from Electronic Health Records and Biorepositories using Semantic Web Technologies
Jyotishman Pathak, PhD Richard C. Kiefer, Robert R. Freimuth, PhD Suzette J. Bielinski, PhD Christopher G. Chute, MD, DrPHDivision of Biomedical Statistics and Informatics, Department of Health Sciences Research
Mayo Clinic, Rochester, MN
Background and Aims
The Linked Clinical Data (LCD) project at aims to develop a semantics-driven framework for high-throughput phenotype representation, extraction, integration, and querying from electronic medical records using emerging Semantic Web technologies, such as the W3C’s Linking Open Data project .
The main goals of the LCD project are to:Investigate ontology-based techniques for representing and encoding phenotype data derived from EHRs; Develop a framework for publishing and integrating ontology-encoded structured phenotype data for federated querying using Linked Data principles and technologies, and Propose and validate semantic reasoning techniques to support rapid cohort identification in chronic diseases.
Linked Data refers to a set of best practices for publishing and linking pieces of data, information and knowledge in the Web.
Core technologies supporting Linked Data: URIs for identifying entities or concepts, RDF data model and RDFS/OWL ontologies for representing, structuring and linking descriptions of entities as resources, An endpoint providing access to the resources through SPARQL queries andHTTP for retrieving resources or descriptions of the resources.
W3C Linked Open Data project2007 - 2 billion RDF triples, 2 million links2011 - 31 billion RDF triples, 504 million links
Linked Data
Methods
For more information – http://informatics.mayo.edu/LCD
rsIDrs5219
genotypeT:T
patientId18299403
clinicId18299403
MayogcId17297
ClinicNumber17297
table1KeyRK4748
table2KeyRK4748
diagnosisType2 diabetes
patient_demographics
wh_demographics
patient_genotypes
wh_diagnosis
Use an ontology to describe the columns of the relational database
Map the model to express the relationship between nodes/edges
Write a SPARQL query based on the mapping
Workflow diagram of how the data is traversed
Sample query results
Results: Type 2 Diabetes Mellitus
A query determines all the individuals having a SNP associated with Type 2 Diabetes Mellitus and retrieves the clinical diagnoses (represented as ICD-9-CM codes) for each eligible subject
Using AHRQ’s Clinical Classification Software, clustering is done for creating a manageable number of clinically meaningful categories
Client applications send query requests Using the Linked Data API, the request is translated into a
federated SPARQL 1.1 query Patient data stored in RDBMS are surfaced as an endpoint SPARQL queries are automatically translated into SQL
statements using applications, such as Spyder Results are returned in XML, RDF or JSON formats
Architecture
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