Translating Genetic Knowledge Into Clinical Care in Non-Autoimmune Diabetes

Overview

The aim of TRANSLATE is to implement genetic information directly into patient care to improve diagnosis and treatment of non-autoimmune diabetes. This project is the first large-scale implementation of systematic genetic testing within a common, non-communicable, chronic disease in Denmark, and will spearhead efforts to advance personalized medicine in Denmark. The project will contribute to establishing technology, workflow, and evidence on how to implement and communicate actionable genetic information to clinicians and patients in a generalized format. These developments are pivotal for personalized medicine to reach broader clinical application.

The TRANSLATE project is an integrative project with multifaceted goals, that can be broken down into two main columns. The foundation for both columns is the WGS analysis in a clinical diagnostic setting in order to guide patient treatment. Patients are not randomized and the inclusion and exclusion criteria are deliberately broad and minimal, respectively. The first column is the clinical development project, which seeks to complete a novel diagnostic process. This column will develop new pipelines and uncover barriers and challenges associated with gene-based precision medicine to facilitate sustainable implementation of gene-based precision medicine beyond the TRANSLATE project. During the project, we wish to focus on potential barriers against a broad application of gene-based precision medicine in a common disease. These may include: * Challenges pertaining to the selection of variants that are deemed clinically actionable, automation of genetic interpretation/translation, and the feasibility of large-scale precision medicine implementation * Ethical concerns of patients, clinicians, and other technicians with regard to the application and utility of genetic information * Validity and limitations of current computational pipelines for variant calling including the calling of structural variants and aggregate genetic tools * Challenges regarding the interoperability of IT systems and databases nationally in Denmark, specifically how central databases can be linked to clinical end-users * How implementation of genetic analyses affects clinical decision-making and/or clinical trajectories, both qualitatively and quantitatively The second column is a register-based research project in which we will utilize data from the patients to advance gene-based precision medicine. In this column we will both address how to establish comprehensive research infrastructure, as well as answer specific research questions. We will address how to combine and harmonize genetic data with other Danish registry sources. We will use the newly established methodologies to focus on the following research areas with respect to patient stratification, clinical trajectories, complication development, and other clinically relevant outcomes: * Polygenic risk scores * Machine learning algorithms * Combined polygenic and monogenic traits * Non-coding variation * Structural variation, specifically exon deletions and duplications, which have previously been shown as a cause of monogenic diabetes