Assessing Variant Impact and Variant Discovery Potential in PGx – PGx Phenotypes
1) Show that we can appreciate variant effect in the EMR and
2) Proof of concept for using EMR data to identify novel functional variants
For Goal 1):
· Identify known pathological variants (by lit review, in silico analysis, interrogating extant databases)
· Request specific quantitative trait phenotype data from sites (simple – no algorithm)
Carotid artert atherosclerosis disease (CAAD) is measured in cases and controls by both structured data, including ICD diagnosis codes, and quantitative measurements of carotid stenosis based on doppler and other imaging technologies.
The phenotype algorithm includes typical eMERGE pseudo code for implementing the structured data components of the algorithm, as well as a portable natural language processing (NLP) system used to extract percent stenosis measurements from imaging reports.
This is a central location to discuss implementing RxNorm mappings for Medication data. This will be used for enabling medication data standardization as needed for phenotype algorithms.
MedEx-UIMA (strongly encouraged to use this version instead of the older Python version) - Hosted by Google code: https://code.google.com/p/medex-uima/
Algorithm to electronically identify age-related macular degeneration and/or drusen cases and controls.
Phenotype Description: Patients on statins for primary prevention who develop an AMI or 1st AMI.
Below are algorithms used to identify AMI and 1st AMI cohort at BioVU. If you have questions regarding any of the information presented on this page, you may contact either:
Wei-Qi Wei at firstname.lastname@example.org
Joshua Denny at email@example.com