Identification and modelling of causal genetic and environmental factors for HD severity and progression

The student will model the interplay between genetic and environmental risk factors and clinical outcomes in a longitudinal cohort of 300 manifest and pre-manifest HD gene carriers.
Huntington’s disease (HD) is a progressive neurodegenerative disorder caused by expanded CAG repeats in the HTT gene. Despite having a single genetic cause, HD exhibits considerable variation in age at onset, progression and symptoms. Recently, genome-wide association studies (GWAS) have implicated genetic variants as modifiers of age at onset and progression , but the mechanisms through which they act are still largely unknown. Likewise, there have been studies reporting associations between environmental factors such as physical activity and diet to HD onset and progression. However, in order to develop effective therapies for HD, it is important to be able to model the interplay between genetic and environmental factors and their causal effects on the progression and severity of clinical symptoms.
This project will use data from the recently funded DOMINO-HD (Multi-Domain Lifestyle Targets for Improving ProgNOsis in Huntington’s Disease) study. This consists of 300 manifest and pre-manifest HD patients with genetic data and detailed clinical (motor, cognitive, behavioural) and environmental (physical activity, sleep and dietary intake) endpoints measured longitudinally over a period of 12 months. The clinical data will be used to develop informative measures of disease severity and progression using a variety of statistical techniques such as principal component analysis and hidden Markov models. The relationships between the genetic and environmental factors and the model of disease progression will be modelled using linear (e.g. canonical correlation) and non-linear machine-learning techniques, and the accuracy of the resulting predictors to predict conversion from pre-manifest to manifest HD will be investigated. Causal relationships between the genetic and environmental factors and the clinical measures of severity and progression will be tested using methods such as Mendelian randomisation. In addition to including known genetic risk factors for HD onset and progression (either individually or combined in a polygenic risk score), the genetic data will be used to discover novel genetic variants and biological pathways influencing disease progression by performing a GWAS with the measures derived in this project.