Genetic Data Integration: A Model for Clinical Implementation and Intervention Research Open Access

Davison, Cynthia (Summer 2018)

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Genetic Data Integration:

A Model for Clinical Implementation

And Intervention Research

By Cynthia Davison


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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