Get the latest public health information from CDC: https://www.coronavirus.gov
Get the latest research information from NIH: https://www.nih.gov/coronavirus
Information about ongoing health services research and public health projects
| : Electronic health record (EHR)-based genomic risk assessment and management for diverse populations State: | |
|---|---|
| Investigator (PI): | Weng, Chunhua; Hripcsak, George M; Chung, Wendy K; Kirluk, Krzysztof Past Investigator: Gharavi, Ali G |
| Performing Organization (PO): |
(Current): Columbia University Irving Medical Center, Department of Medicine / (212) 305-5334 (Past): Columbia University Irving Medical Center, Department of Biomedical Informatics / (212) 305-5334 |
| Supporting Agency (SA): | National Institutes of Health (NIH), National Human Genome Research Institute (NHGRI) |
| Initial Year: | 2015 |
| Final Year: | 2025 |
| Record Source/Award ID: | RePorter/U01HG008680 |
| Funding: | 2015 Award Amount: $859,484 2016 Award Amount: $923,820 2017 Award Amount: $901,893 2018 Award Amount: $858,631 2019 Award Amount: $707,363 2020 Award Amount: $1,457,507 |
| Award Type: | Grant |
| Award Information: | Reports resulting from this project |
| Abstract: | Recently, large-scale genome-wide association studies (GWAS) provide evidence for a substantial polygenic contribution to the risk of many common complex diseases. However, most of these studies were performed in Europeans, and new data and methods are necessary to tailor polygenic risk prediction to non-Europeans, to ensure that genomic stratification does not further exacerbate health disparities. The overarching goal of the eMERGE-IV network is to leverage genetic and electronic health record (EHR) data for diverse populations to design, validate and test the clinical utility of ancestry-tailored 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 diverse 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. Our scientific expertise combined with our strong tradition of patient-centered research and community engagement in a socioeconomically, racially, and ethnically diverse community of Northern Manhattan, positions us to successfully contribute as the Enhanced Diversity Clinical Site of the eEMERGE-IV network. 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 ancestry-tailored 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 ancestry-tailored genome-wide polygenic predictors, integrate them with clinical risk predictions, and test their performance in diverse populations. In Aim 3, we will investigate ELSI issues related to the return of health risk predictions to diverse 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 recruit 2,500 diverse patients and use a randomized controlled trial design to 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 for diverse populations, and the solutions and knowledge gained will be broadly applicable to precision medicine for common complex traits across many clinical specialties |
| Abstract Archived: |
A current participant in the eMERGE-II consortium, Columbia serves a racially and ethnically diverse patient population in New York City, and has a strong tradition of community engagement. We have made significant contributions to the goals of eMERGE-II, including developing and evaluating electronic health records-based phenotyping algorithms; understanding data biases, data missingness, and other data quality issues in EHR data and their impact on phenotyping; defining a research agenda for next-generation EHR phenotyping; exploring the use of patient self-reported health status data to complement EHR data for phenotyping; developing novel methods for hereditability estimation; designing informatics interventions to integrate patient care and clinical research workflows and to link EHR and sequence data with genomic knowledge for decision support; communicating genetic risk to patients; addressing patients' preferences for returning incidental findings; and investigating the impact of returning results on patients and clinicians. Columbia has also established Precision Medicine as a major university-wide initiative. To date, our biobank has accumulated a multiethnic cohort of 26,310 individuals with their samples linked to our EHR data, among which we currently have exome sequence data on 3,059 patients and consent for broad genetic discoveries and wide data sharing without re-consent from 7,648 patients. This includes nearly 4,000 patients with rich self-reported health status information, who are representative of the Northern Manhattan community, and were not pre-selected based on any specific disease or diagnosis. Our proposal for eMERGE-III builds on our prior work and expertise in genomic medicine. Our four specific aims will be accomplished by wide dissemination of data and phenotyping algorithms, close collaboration with eMERGE and other research consortia (e.g., CSER, LEGACY, DHEAMS, OHDSI, CTSA, PCORI, and so on), and by using standards-based formal methods. Aim 1 is to advance next-generation phenotyping by designing, validating, and sharing high-throughput, data quality-aware, standards-based phenotyping methods. Aim 2 is to perform genetic association studies of rare variants with diverse clinical phenotypes through broad collaboration with the eMERGE network and other phenotyping research communities. Aim 3 is to develop practical, scalable learning mechanisms for returning results by leveraging a genomic patient portal and genetic providers to dynamically elicit and incorporate patient preferences for return of genomic results, returning results, and studying patient understanding of returned results. Aim 4 is to provide genomic decision support by enhancing and validating our clinical and informatics infrastructure for genomic decision support with learning mechanisms for tailored shared decision making. |
| MeSH Terms: |
|
| Keywords: |
|
| Country: | United States || United States |
| State: | New York || New York |
| Zip Code: | 10032 / 10032 |
| UI: | 20181421 |
| Project Status: | Ongoing |
| Record History: | ('2020: Project title changed (Prior title: Columbia GENIE (GENomic Integration with EHR)); Co-PI Gharavi left project; Co-PIs Chung & Kirluk joined project; PO changed; Abstract archived and replaced; Project extended to 2025. 2019: Project extended to 2020.',) |