Large-scale pathway specific polygenic risk and transcriptomic community network analysis identifies novel functional pathways in Parkinson disease.

Polygenic inheritance plays a central role in Parkinson disease (PD). A priority in elucidating PD etiology lies in defining the biological basis of genetic risk. Unraveling how risk leads to disruption will yield disease-modifying therapeutic targets that may be effective. Here, we utilized a high-throughput and hypothesis-free approach to determine biological processes underlying PD using the largest currently available cohorts of genetic and gene expression data from International Parkinson's Disease Genetics Consortium (IPDGC) and the Accelerating Medicines Partnership-Parkinson's disease initiative (AMP-PD), among other sources. We applied large-scale gene-set specific polygenic risk score (PRS) analyses to assess the role of common variation on PD risk focusing on publicly annotated gene sets representative of curated pathways. We nominated specific molecular sub-processes underlying protein misfolding and aggregation, post-translational protein modification, immune response, membrane and intracellular trafficking, lipid and vitamin metabolism, synaptic transmission, endosomal-lysosomal dysfunction, chromatin remodeling and apoptosis mediated by caspases among the main contributors to PD etiology. We assessed the impact of rare variation on PD risk in an independent cohort of whole-genome sequencing data and found evidence for a burden of rare damaging alleles in a range of processes, including neuronal transmission-related pathways and immune response. We explored enrichment linked to expression cell specificity patterns using single-cell gene expression data and demonstrated a significant risk pattern for dopaminergic neurons, serotonergic neurons, hypothalamic GABAergic neurons, and neural progenitors. Subsequently, we created a novel way of building de novo pathways by constructing a network expression community map using transcriptomic data derived from the blood of PD patients, which revealed functional enrichment in inflammatory signaling pathways, cell death machinery related processes, and dysregulation of mitochondrial homeostasis. Our analyses highlight several specific promising pathways and genes for functional prioritization and provide a cellular context in which such work should be done.

Genetic risk factors for cognitive decline in Parkinson's disease: a review of the literature.

Parkinson's disease is a highly heterogeneous disorder, where genetic factors are likely to contribute to clinical variability, including susceptibility to cognitive impairment and dementia. Monogenic forms of parkinsonism show distinct cognitive profiles, yet less is known about the impact of common genetic variants on cognition in sporadic Parkinson's disease. In a systematic review of the literature, the current results from genetic association studies of cognitive outcomes are summarized and prospects and challenges for future studies are discussed. Literature searches of the PubMed database were performed and studies using statistical methods to assess associations between genetic variation and any cognitive outcome in Parkinson's disease patients were included. For each of the candidate loci investigated in several studies, the current evidence is summarized and discussed. Sixty-one articles meeting our inclusion criteria were identified, which were highly heterogeneous with respect to study design, size and cognitive outcome measures. GBA mutations have a negative impact on cognition, whereas LRRK2-associated disease may have a milder cognitive phenotype than idiopathic Parkinson's disease. For common variants, reported results are partly conflicting, even across the larger studies, with some evidence to suggest a potential effect of APOE, MAPT, COMT and SNCA on cognitive outcomes. Future investigations should aim to collect high-quality cognitive data in a standardized way that allows for direct comparison across studies and large-scale meta-analysis. Linking genetic profiles to cognitive outcomes may have an important clinical impact, facilitating the stratification of patients for clinical trials and, ultimately, individualized treatment in Parkinson's disease.