Dystonia is a neurologic disorder characterized by abnormal body posture and tone. Although much progress has been made in recent years in identifying the genes for dystonia, the contribution of genetic risk factors to disease is more than what can currently be explained by the known genes. Similarly, while dystonia has recently been defined as a neural network disorder, the complex interactions between different brain alterations as well as the primary vs. compensatory contribution of these abnormalities remain unknown. Our group is interested in discovering the genes and pathophysiologically significant imaging and protein biomarkers associated with the risk of dystonia and other related disorders. Using whole genome sequencing data, we will identify genetic variations that are enriched in the dystonia cohort in the UK Biobank in comparison to the general population. We will layer this information with proteomic data to identify disease-relevant biomarkers. We will study brain images from participants to identify the relationship between specific brain structural and functional changes to genetic variation and protein changes. We will also employ advanced machine-learning methods to discover the genetic, proteomic, and neuroimaging biomarkers that differentiate between dystonia and related disorders and healthy individuals as well as between the many different forms and manifestations of dystonia. Our results will establish enhanced criteria and improve the clinical management of dystonia, including its early detection and accurate diagnosis.
