Parkinson’s disease (PD) is a progressive, neurodegenerative disorder of aging that affects both motor and cognitive function. Despite considerable progress in identifying candidate genetic and environmental influences on the development of PD, to date no cures exist, the diagnosis remains mainly clinical, and no validated biomarkers are in clinical use. Thus, there’s a need to identifying additional genes and contributing genetic biomarkers to track progression of the disease. In this application, we will apply innovative statistical approaches to the integration of clinical, genomic and transcriptomic data from the Accelerating Medicine Partnership in Parkinson’s disease (AMP PD) to advance the identification of novel biomarkers and disease pathways related to the progression of PD. We will conduct state-of-the-art analyses that will integrate genomics, clinical, and longitudinal transcriptomic data sets to generate patient derived, data-driven, multi-scale models of disease. This will enable the generation of hypotheses around gene interactions specific to disease states and progression. In aim 1, we will apply probabilistic models to AMP-PD transcriptomic data to infer time course trajectories of gene expression that are associated with rate of PD progression. In aim 2, we will map common genetic variants that modulate inter-individual transcriptomic variation in multiple time points through progression using a novel linear mixed model that accounts for repeat measurements, multiple conditions and both latent and measured technical confounding. At the completion of these aims we will have access to a well-powered set of expression quantitative trait loci at different stages of PD progression, for different computationally inferred cell-types, and for progression interaction effects. This project will have a large overall impact by: 1) providing mechanistic interpretation of specific PD risk and progression GWAS loci; 2) possibly leading to the discovery of novel biomarkers and therapeutic targets that modulate the immune system.