Identification of genomic predictors of atrioventricular conduction: Using electronic medical records as a tool for genome science

Joshua C. Denny, Marylyn D. Ritchie, Dana C. Crawford, Jonathan S. Schildcrout, Andrea H. Ramirez, Jill M. Pulley, Melissa A. Basford, Daniel R. Masys, Jonathan L. Haines, Dan M. Roden

Research output: Contribution to journalArticle

97 Citations (Scopus)

Abstract

Background: Recent genome-wide association studies in which selected community populations are used have identified genomic signals in SCN10A influencing PR duration. The extent to which this can be demonstrated in cohorts derived from electronic medical records is unknown. Methods and Results: We performed a genome-wide association study on 2334 European American patients with normal ECGs without evidence of prior heart disease from the Vanderbilt DNA databank, BioVU, which accrues subjects from routine patient care. Subjects were identified by combinations of natural language processing, laboratory queries, and billing code queries of deidentified medical record data. Subjects were 58% female, of mean (±SD) age 54±15 years, and had mean PR intervals of 158±18 ms. Genotyping was performed with the use of the Illumina Human660W-Quad platform. Our results identify 4 single nucleotide polymorphisms (rs6800541, rs6795970, rs6798015, rs7430477) linked to SCN10A associated with PR interval (P=5.73×10 -7 to 1.78×10 -6). Conclusions: This genome-wide association study confirms a gene heretofore not implicated in cardiac pathophysiology as a modulator of PR interval in humans. This study is one of the first replication genome-wide association studies performed with the use of an electronic medical records-derived cohort, supporting their further use for genotype-phenotype analyses.

Original languageEnglish (US)
Pages (from-to)2016-2021
Number of pages6
JournalCirculation
Volume122
Issue number20
DOIs
StatePublished - Nov 16 2010

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Electronic Health Records
Genome-Wide Association Study
Genome
Natural Language Processing
Nucleic Acid Databases
Medical Records
Single Nucleotide Polymorphism
Heart Diseases
Patient Care
Electrocardiography
Genotype
Phenotype
Population
Genes

All Science Journal Classification (ASJC) codes

  • Cardiology and Cardiovascular Medicine
  • Physiology (medical)

Cite this

Denny, J. C., Ritchie, M. D., Crawford, D. C., Schildcrout, J. S., Ramirez, A. H., Pulley, J. M., ... Roden, D. M. (2010). Identification of genomic predictors of atrioventricular conduction: Using electronic medical records as a tool for genome science. Circulation, 122(20), 2016-2021. https://doi.org/10.1161/CIRCULATIONAHA.110.948828
Denny, Joshua C. ; Ritchie, Marylyn D. ; Crawford, Dana C. ; Schildcrout, Jonathan S. ; Ramirez, Andrea H. ; Pulley, Jill M. ; Basford, Melissa A. ; Masys, Daniel R. ; Haines, Jonathan L. ; Roden, Dan M. / Identification of genomic predictors of atrioventricular conduction : Using electronic medical records as a tool for genome science. In: Circulation. 2010 ; Vol. 122, No. 20. pp. 2016-2021.
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Denny, JC, Ritchie, MD, Crawford, DC, Schildcrout, JS, Ramirez, AH, Pulley, JM, Basford, MA, Masys, DR, Haines, JL & Roden, DM 2010, 'Identification of genomic predictors of atrioventricular conduction: Using electronic medical records as a tool for genome science', Circulation, vol. 122, no. 20, pp. 2016-2021. https://doi.org/10.1161/CIRCULATIONAHA.110.948828

Identification of genomic predictors of atrioventricular conduction : Using electronic medical records as a tool for genome science. / Denny, Joshua C.; Ritchie, Marylyn D.; Crawford, Dana C.; Schildcrout, Jonathan S.; Ramirez, Andrea H.; Pulley, Jill M.; Basford, Melissa A.; Masys, Daniel R.; Haines, Jonathan L.; Roden, Dan M.

In: Circulation, Vol. 122, No. 20, 16.11.2010, p. 2016-2021.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Identification of genomic predictors of atrioventricular conduction

T2 - Using electronic medical records as a tool for genome science

AU - Denny, Joshua C.

AU - Ritchie, Marylyn D.

AU - Crawford, Dana C.

AU - Schildcrout, Jonathan S.

AU - Ramirez, Andrea H.

AU - Pulley, Jill M.

AU - Basford, Melissa A.

AU - Masys, Daniel R.

AU - Haines, Jonathan L.

AU - Roden, Dan M.

PY - 2010/11/16

Y1 - 2010/11/16

N2 - Background: Recent genome-wide association studies in which selected community populations are used have identified genomic signals in SCN10A influencing PR duration. The extent to which this can be demonstrated in cohorts derived from electronic medical records is unknown. Methods and Results: We performed a genome-wide association study on 2334 European American patients with normal ECGs without evidence of prior heart disease from the Vanderbilt DNA databank, BioVU, which accrues subjects from routine patient care. Subjects were identified by combinations of natural language processing, laboratory queries, and billing code queries of deidentified medical record data. Subjects were 58% female, of mean (±SD) age 54±15 years, and had mean PR intervals of 158±18 ms. Genotyping was performed with the use of the Illumina Human660W-Quad platform. Our results identify 4 single nucleotide polymorphisms (rs6800541, rs6795970, rs6798015, rs7430477) linked to SCN10A associated with PR interval (P=5.73×10 -7 to 1.78×10 -6). Conclusions: This genome-wide association study confirms a gene heretofore not implicated in cardiac pathophysiology as a modulator of PR interval in humans. This study is one of the first replication genome-wide association studies performed with the use of an electronic medical records-derived cohort, supporting their further use for genotype-phenotype analyses.

AB - Background: Recent genome-wide association studies in which selected community populations are used have identified genomic signals in SCN10A influencing PR duration. The extent to which this can be demonstrated in cohorts derived from electronic medical records is unknown. Methods and Results: We performed a genome-wide association study on 2334 European American patients with normal ECGs without evidence of prior heart disease from the Vanderbilt DNA databank, BioVU, which accrues subjects from routine patient care. Subjects were identified by combinations of natural language processing, laboratory queries, and billing code queries of deidentified medical record data. Subjects were 58% female, of mean (±SD) age 54±15 years, and had mean PR intervals of 158±18 ms. Genotyping was performed with the use of the Illumina Human660W-Quad platform. Our results identify 4 single nucleotide polymorphisms (rs6800541, rs6795970, rs6798015, rs7430477) linked to SCN10A associated with PR interval (P=5.73×10 -7 to 1.78×10 -6). Conclusions: This genome-wide association study confirms a gene heretofore not implicated in cardiac pathophysiology as a modulator of PR interval in humans. This study is one of the first replication genome-wide association studies performed with the use of an electronic medical records-derived cohort, supporting their further use for genotype-phenotype analyses.

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