Although a significant genetic contribution to the risk of developing sporadic Parkinson's disease has been well described, the relationship between local genetic factors, pathogenesis, and subsequent spread of pathology throughout the brain has been largely unexplained in humans. To address this question, we use network diffusion modelling to infer probable pathology seed regions and patterns of disease spread from MRI atrophy maps derived from 232 de novo subjects in the Parkinson's Progression Markers Initiative study. Allen Brain Atlas regional transcriptional profiles of 67 Parkinson's disease risk factor genes were mapped to the inferred seed regions to determine the local influence of genetic risk factors. We used hierarchical clustering and L1 regularized regression analysis to show that transcriptional profiles of immune-related and lysosomal risk factor genes predict seed region location and the pattern of disease propagation from the most likely seed region, substantia nigra. By leveraging recent advances in transcriptomics, we show that regional microglial abundance quantified by high fidelity gene expression also predicts seed region location. These findings suggest that early disease sites are genetically susceptible to dysfunctional lysosomal α-synuclein processing and microglia-mediated neuroinflammation, which may initiate the disease process and contribute to spread of pathology along neural connectivity pathways.

There is enormous clinical value in inferring the brain regions initially atrophied in Parkinson disease for individual patients and understanding its relationship with clinical and genetic risk factors. The aim of this study is to leverage a new seed-inference algorithm demonstrated for Alzheimer's disease to the Parkinsonian context and to cluster patients in meaningful subgroups based on these incipient atrophy patterns. Instead of testing brain regions separately as the likely initiation site for each patient, we solve an L1-penalized optimization problem that can return a more predictive heterogeneous, multi-locus seed patterns. A cluster analysis of the individual seed patterns reveals two distinct subgroups (S1 versus S2). The S1 subgroup is characterized by the involvement of the brainstem and ventral nuclei, and S2 by cortex and striatum. Post hoc analysis in features not included in the clustering shows significant differences between subgroups regarding age of onset and local transcriptional patterns of Parkinson-related genes. Top genes associated with regional microglial abundance are strongly associated with subgroup S1 but not with S2. Our results suggest two distinct aetiological mechanisms operative in Parkinson disease. The interplay between immune-related genes, lysosomal genes, microglial abundance and atrophy initiation sites may explain why the age of onset for patients in S1 is on average 4.5 years later than for those in S2. We highlight and compare the most prominently affected brain regions for both subgroups. Altogether, our findings may improve current screening strategies for early Parkinson onsetters.