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