EHR-based Genome-Informed Risk Assessment and Communication

Recently, large-scale genome-wide association studies (GWAS) provide evidence for a substantial polygenic contribution to the risk of common complex diseases. This research aims to leverage large-scale genetic and electronic health record (EHR) data to design and validate the clinical utility of polygenic risk scores for common diseases. As a current member of the eMERGE network, Columbia University has significantly advanced its goals, having recruited over 2,500 patients for sequencing and return of actionable findings, leading the effort to transition the network to the OMOP Common Data Model to improve the efficiency, accuracy, reproducibility and portability of electronic phenotypes, and contributing a widely-adopted XML parser for structuring genetic test reports. Since our last application, the Columbia Precision Medicine Initiative has also grown and now includes participation in several national initiatives, such as the All-of-Us program, in which we have demonstrated our ability to rapidly recruit patients under-represented in biomedical research. We have deep expertise and strong tradition of patient-centered research and community engagement in a community of Northern Manhattan. We will leverage our prior experience with eMERGE, scientific expertise, and knowledge gained from participation in other national precision medicine initiatives to develop, optimize, validate and disseminate personalized genomic risk assessment and clinical management tools. In Aim 1, we will continue to advance electronic phenotyping by contributing sharable natural language processing tools for converting clinical text into OMOP-based discrete data and facilitating phenotype interoperability. In Aim 2, we will develop and optimize accurate genome-wide polygenic predictors, integrate them with clinical risk predictions, and test their performance. In Aim 3, we will investigate ELSI issues related to the return of health risk predictions to patients by ascertaining patients’, clinicians’, and IRB members’ views through focus groups. In Aim 4, we will develop portable EHR plug-ins to facilitate prospective risk communication and management using integrated genomic data, family history, and clinical data. In Aim 5, we will assess the impact of return of genomic prediction on the accuracy of risk perception, health surveillance, and risk reducing measures. This proposal will address major knowledge gaps in genetic risk assessment, and the solutions and knowledge gained will be broadly applicable to precision medicine for common complex traits across many clinical specialties.