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CHIME College of Healthcare Information Management Executives
2014-10-28 - 2014-10-31    
All Day
The Premier Event for Healthcare CIOs Hotel Accomodations JW Marriott San Antonio Hill Country 23808 Resort Parkway San Antonio, Texas 78761 Telephone: 210-276-2500 Guest Fax: [...]
The Myth of the Paperless EMR
2014-10-29    
2:00 pm - 3:00 pm
Is Paper Eluding Your Current Technologies; The Myth of the Paperless EMR Please join Intellect Resources as we present Is Paper Eluding Your Current Technologies; The Myth [...]
The New York eHealth Collaborative Digital Health Conference
2014-11-17    
All Day
 Showcasing Innovation Join a dynamic community of innovators and thought leaders who are shaping the future of healthcare through technology. The New York eHealth Collaborative [...]
Big Data Healthcare Analytics Forum
2014-11-20    
All Day
The Big Data & Healthcare Analytics Forum Cuts Through the Hype When it comes to big data, the healthcare industry is flooded with hype and [...]
Events on 2014-10-28
Events on 2014-10-29
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Events on 2014-11-20
research papers

Researchers repurpose genetic data, EMR to perform large-scale PheWAS study

genetic data

Vanderbilt University Medical Center researchers and co-authors from four other U.S. institutions from the Electronic Medical Records and Genomics (eMERGE) Network are repurposing genetic data and electronic medical records to perform the first large-scale phenome-wide association study (PheWAS), released today in Nature Biotechnology.

Traditional genetic studies start with one phenotype and look at one or many genotypes, PheWAS does the inverse by looking at many diseases for one genetic variant or genotype.

“This study broadly shows that we can take decades of off-the-shelf electronic medical record data, link them to DNA, and quickly validate known associations across hundreds of previous studies,” said lead author Josh Denny, M.D., M.S., Vanderbilt Associate Professor of Biomedical Informatics and Medicine. “And, at the same time, we can discover many new associations.

“A third important finding is that our method does not select any particular disease – it is searches simultaneously for more than a thousand diseases that bring one to the doctor. By doing this, we were able to show some genes that are associated several diseases or traits, while others are not,” he added.

Researchers used genotype data from 13,835 individuals of European descent, exhibiting 1,358 diseases collectively. The team then ran PheWAS on 3,144 single-nucleotide polymorphisms (SNP’s), checking each SNP’s association with each of the 1,358 disease phenotypes.

As a result, study authors reported 63 previously unknown SNP-disease associations, the strongest of which related to skin diseases.

“The key result is that the method works,” Denny said. “This is a robust test of PheWAS across all domains of disease, showing that you can see all types of phenotypes in the electronic medical record – cancers, diabetes, heart diseases, brain diseases, etc. – and replicate what’s known about their associations with various SNPs.”

An online PheWAS catalog spawned by the study may help investigators understand the influence of many common genetic variants on human conditions.

source