Using Electronic Medical Records to advance genomic medicine

author: Joshua C Denny, School of Medicine, Vanderbilt University
published: July 21, 2014,   recorded: May 2014,   views: 1662


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


Personalized medicine offers the promise of improved diagnosis and for more effective, patient-specific therapies. Typically, such studies have been pursued using research cohorts. At Vanderbilt, we have created a DNA biobank, BioVU, which now has nearly 180,000 samples linked to de-identified electronic medical records (EMRs). BioVU allows study of genomic and pharmacogenomic associations using real-world clinical data. Finding phenotypes in the EMR can be challenging, but the combination of billing data, laboratory data, medication exposures, and natural language processing has enabled efficient study of genomic and pharmacogenomic phenotypes. The EMR also enables the inverse experiment - starting with a genotype and discovering all the phenotypes with which it is associated - a phenome-wide association study (PheWAS). PheWAS requires a densely phenotyped population such as found in the EMR. We have used PheWAS to replicate >200 genotype-phenotype associations, characterize pleiotropy, and discover new associations. Finally, these discovery efforts have been the foundation for implementing a prospective personalized medicine that genotypes people at risk for common medications with pharmacogenetic variants. Through this program, we have genotyped >15,000 individuals at 184 pharmacovariants. Computerized decision support has been implemented to tailor medical therapy based on genotypes relevant to clopidogrel, warfarin, thiopurines, tacrolimus, and statins.

See Also:

Download slides icon Download slides: mdo2014_denny_genomic_medicine_01.pdf (6.6┬áMB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: