Applying semantic web technologies for phenome-wide scan using an electronic health record linked Biobank
<p>Abstract</p> <p>Background</p> <p>The ability to conduct genome-wide association studies (GWAS) has enabled new exploration of how genetic variations contribute to health and disease etiology. However, historically GWAS have been limited by inadequate sample size due...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2012-12-01
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| Series: | Journal of Biomedical Semantics |
| Online Access: | http://www.jbiomedsem.com/content/3/1/10 |
| _version_ | 1818751570665275392 |
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| author | Pathak Jyotishman Kiefer Richard C Bielinski Suzette J Chute Christopher G |
| author_facet | Pathak Jyotishman Kiefer Richard C Bielinski Suzette J Chute Christopher G |
| author_sort | Pathak Jyotishman |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>The ability to conduct genome-wide association studies (GWAS) has enabled new exploration of how genetic variations contribute to health and disease etiology. However, historically GWAS have been limited by inadequate sample size due to associated costs for genotyping and phenotyping of study subjects. This has prompted several academic medical centers to form “biobanks” where biospecimens linked to personal health information, typically in electronic health records (EHRs), are collected and stored on a large number of subjects. This provides tremendous opportunities to discover novel genotype-phenotype associations and foster hypotheses generation.</p> <p>Results</p> <p>In this work, we study how emerging Semantic Web technologies can be applied in conjunction with clinical and genotype data stored at the Mayo Clinic Biobank to mine the phenotype data for genetic associations. In particular, we demonstrate the role of using Resource Description Framework (RDF) for representing EHR diagnoses and procedure data, and enable federated querying via standardized Web protocols to identify subjects genotyped for Type 2 Diabetes and Hypothyroidism to discover gene-disease associations. Our study highlights the potential of Web-scale data federation techniques to execute complex queries.</p> <p>Conclusions</p> <p>This study demonstrates how Semantic Web technologies can be applied in conjunction with clinical data stored in EHRs to accurately identify subjects with specific diseases and phenotypes, and identify genotype-phenotype associations.</p> |
| first_indexed | 2024-12-18T04:37:40Z |
| format | Article |
| id | doaj.art-60e4b431dba249bbbde630cee264076f |
| institution | Directory Open Access Journal |
| issn | 2041-1480 |
| language | English |
| last_indexed | 2024-12-18T04:37:40Z |
| publishDate | 2012-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Biomedical Semantics |
| spelling | doaj.art-60e4b431dba249bbbde630cee264076f2022-12-21T21:20:48ZengBMCJournal of Biomedical Semantics2041-14802012-12-01311010.1186/2041-1480-3-10Applying semantic web technologies for phenome-wide scan using an electronic health record linked BiobankPathak JyotishmanKiefer Richard CBielinski Suzette JChute Christopher G<p>Abstract</p> <p>Background</p> <p>The ability to conduct genome-wide association studies (GWAS) has enabled new exploration of how genetic variations contribute to health and disease etiology. However, historically GWAS have been limited by inadequate sample size due to associated costs for genotyping and phenotyping of study subjects. This has prompted several academic medical centers to form “biobanks” where biospecimens linked to personal health information, typically in electronic health records (EHRs), are collected and stored on a large number of subjects. This provides tremendous opportunities to discover novel genotype-phenotype associations and foster hypotheses generation.</p> <p>Results</p> <p>In this work, we study how emerging Semantic Web technologies can be applied in conjunction with clinical and genotype data stored at the Mayo Clinic Biobank to mine the phenotype data for genetic associations. In particular, we demonstrate the role of using Resource Description Framework (RDF) for representing EHR diagnoses and procedure data, and enable federated querying via standardized Web protocols to identify subjects genotyped for Type 2 Diabetes and Hypothyroidism to discover gene-disease associations. Our study highlights the potential of Web-scale data federation techniques to execute complex queries.</p> <p>Conclusions</p> <p>This study demonstrates how Semantic Web technologies can be applied in conjunction with clinical data stored in EHRs to accurately identify subjects with specific diseases and phenotypes, and identify genotype-phenotype associations.</p>http://www.jbiomedsem.com/content/3/1/10 |
| spellingShingle | Pathak Jyotishman Kiefer Richard C Bielinski Suzette J Chute Christopher G Applying semantic web technologies for phenome-wide scan using an electronic health record linked Biobank Journal of Biomedical Semantics |
| title | Applying semantic web technologies for phenome-wide scan using an electronic health record linked Biobank |
| title_full | Applying semantic web technologies for phenome-wide scan using an electronic health record linked Biobank |
| title_fullStr | Applying semantic web technologies for phenome-wide scan using an electronic health record linked Biobank |
| title_full_unstemmed | Applying semantic web technologies for phenome-wide scan using an electronic health record linked Biobank |
| title_short | Applying semantic web technologies for phenome-wide scan using an electronic health record linked Biobank |
| title_sort | applying semantic web technologies for phenome wide scan using an electronic health record linked biobank |
| url | http://www.jbiomedsem.com/content/3/1/10 |
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