Genetic Data Integration: A Model for Clinical Implementation and Intervention Research Public
Davison, Cynthia (Summer 2018)
Published
Abstract
Opioid addiction is a multifactorial condition for which there is growing evidence of a genetic contribution and thus vulnerability to abuse. Concurrent with the growth of this epidemic, is significant advancement in genomic sequencing, cloud-based services, predictive analytics, big data storage and retrieval, and emerging computing technologies - a concomitant growth that is enabling an ever-widening scope of genetic inquiry and thus application. Given this technological landscape, we can construct a model that integrates genetic data into a cloud-based platform, facilitates use of next generation and emerging applications, and contributes to the growth of evidence-based treatment and genomic knowledge. As a foundation for development, the MeTree study platform, developed by Duke University and sponsored by the National Institutes of Health, serves as a starting point.
As currently constructed, the IGNITE (Implementing GeNomics In pracTicE) MeTree project platform offers elements from which to architect a clinico-genomic decision support platform that incorporates modern technologies. The MeTree platform collects family health history (FHH) data, links to a patient electronic health record (EHR) database, and provides clinical decision support (CDS) to providers and patients using guidelines-based recommendations for individuals at risk of developing common chronic diseases. It supports the SMART-on-FHIR standard, an HL7 data access and management platform, and, thus, it can employ the SMART-on-FHIR genomic profile for incorporating genetic data into an EHR system. Adoption of that standard creates the opportunity for an application-driven, microservices architected model for disease study and clinical use, the original concept behind SMART-on-FHIR development. Since prior studies suggest that dopamine receptor genes are prime candidates for the study of genetic variants and their effects on opioid dependence vulnerability, the investigation and inclusion of specific abuse-related variant information along with EHR, and FHH data has the potential to expand the current understanding of genetic addiction vulnerabilities, and genotype-phenotype associations. And, from a public health perspective, the inclusion of genetic data and application of predictive analytic techniques as presented in this model, can point the way for intervention strategy development for many multivariate disease types, and for the future implementation and practice of
precision medicine.
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