Abi Pfaff

Introduction Parkinson’s disease (PD) is the second most common neurodegenerative disease, affecting 1% of the population over 60 years of age. PD pathology is characterised by the loss of dopaminergic neurons from the substantia nigra and the formation of neuronal inclusions called Lewy bodies. The development of PD involves a complex interaction of genetic and environmental factors. Many of the genetic factors affecting disease progression have been identified but there is much still to be determined. This is particularly true for those factors not causative of the disease, but progression and severity of the disease once diagnosed. We aim to address both genetic and transcriptomic changes which we can bioinformatically identify in the Parkinson’s Progression Markers Initiative (PPMI) cohort which was established to identify PD progression markers to help understand disease aetiology and ultimately aid in the development of novel therapeutics. Methods Genetic data, longitudinal clinical data and transcriptomic data has been obtained from the Parkinson’s Progression Markers Initiative (PPMI) cohort (www.ppmi-info.org). The DESeq2 package in R will be used to detect statistically significant differences in the gene expression profiles between the different genotypes which we are currently delineating to correlate with specific clinical progression markers. This will allow us to align genetic variation with blood transcriptomic changes with disease progression. Approach for statistical analysis Once we have identified targets using DESeq2, their association with PD progression will be analysed in the longitudinal analysis using a linear mixed-effects model combined with FDR correction, measuring the changes in phenotypic scores during follow-up visits that will later be analysed.