Genomic medicine offers hope for improved diagnostic methods and for more effective, patient-specific therapies. Genome-wide associated studies (GWAS) elucidate genetic markers that improve clinical understanding of risks and mechanisms for many diseases and conditions and that may ultimately guide diagnosis and therapy on a patient-specific basis. This project will expand on existing work to identify gene-phenotype associations across the genome and phenome, deploying new phenome-wide associations study (PheWAS) methods to deeply investigate electronic medical record (EMR)-derived phenotypes across common and rare variants across the genome. The project is enabled by large DNA biobanks coupled to de-identified copies of EMR. This project has three specific aims. First, we will expand the PheWAS phenotype library to include both binary traits and continuous variables incorporating about 7000 phenotypes derived from natural language processing, laboratory data, and report data.
The second aim i s to perform a PheWAS for common and rare variants using extant genome-wide and exome variant data and the broader set of phenotypes derived in Aim 1. We will analyze associations using single variant and multi-variant aggregation methods. We will validate the efficacy of our methods in Aim 2 by comparing to known associations.
The third aim i s to develop a standards-based infrastructure to share PheWAS results and develop tools to enable others to perform PheWAS. The tools generated from this project will not only expand the capabilities of the current PheWAS methodology, but will also broadly enable clinical research and subsequent genetic studies.
Genomic medicine offers hope for improved diagnosis and for more effective, patient- specific therapies. This PheWAS proposal will develop new methods to identify detailed phenotypes and diseases from electronic medical records and then find novel genetic associations from existing genomic data.
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