A population scale analysis of rare SNCA variation in the UK Biobank

Parkinson's disease (PD) is a complex neurodegenerative disease with a variety of genetic and environmental factors contributing to disease. The SNCA gene encodes for the alpha-synuclein protein which plays a central role in PD, where aggregates of this protein are one of the pathological hallmarks of disease. Rare point mutations and copy number gains of the SNCA gene have been shown to cause autosomal dominant PD, and common DNA variants identified using Genome-Wide Association Studies (GWAS) are a moderate risk factor for PD. The UK Biobank is a large-scale population prospective study including ~500,000 individuals that has revolutionized human genetics. Here we assessed the frequency of SNCA variation in this cohort and identified 30 subjects carrying variants of interest including duplications (n = 6), deletions (n = 6) and large complex likely mosaic events (n = 18). No known pathogenic missense variants were identified. None of these subjects were reported to be a PD case, although it is possible that these individuals may develop PD at a later age, and whilst three had known prodromal features, these did not meet defined clinical criteria for being considered ‘prodromal’ cases. Seven of the 18 large complex carriers showed a history of blood based cancer. Overall, we identified copy number variants in the SNCA region in a large population based cohort without reported PD phenotype and symptoms. Putative mosaicism of the SNCA gene was identified, however, it is unclear whether it is associated with PD. These individuals are potential candidates for further investigation by performing SNCA RNA and protein expression studies, as well as promising clinical trial candidates to understand how duplication carriers potentially escape PD

Lower lymphocyte count is associated with increased risk of Parkinson's disease

Objectives: Patients with established Parkinson’s disease (PD) display differences in peripheral blood biomarkers of immune function, including leukocyte differential counts, compared to controls. These differences may be useful biomarkers to predict PD and shed light on pathogenesis. We sought to identify whether peripheral immune dysregulation was associated with increased risk of subsequent PD diagnosis. Methods: We examined the relationship between incident PD and baseline differential leukocyte count and other blood markers of acute inflammation in UK Biobank, a longitudinal cohort with >500 000 participants. We used a range of sensitivity analyses and Mendelian randomization (MR) to further explore the nature of associations. Results: After excluding individuals with comorbidities which could influence biomarkers of inflammation, 465 incident PD cases and 312,125 controls remained. Lower lymphocyte count was associated with increased risk of subsequent PD diagnosis (per 1‐SD decrease in lymphocyte count OR 1.18, 95% CI 1.07‐1.32, padjusted=0.01). There was some evidence that reductions in eosinophil and monocyte counts and CRP were associated with increased PD risk, as was higher neutrophil count. Only the association between lower lymphocyte count and increased PD risk remained robust to sensitivity analyses. MR suggested that the effect of lower lymphocyte count on PD risk may be causal (per 1‐SD decrease in lymphocyte count; ORMR 1.09, 95% CI 1.01‐1.18, p=0.02). Interpretation: We provide converging evidence from observational analyses in UKB and MR that lower lymphocyte count is associated with an increased risk of subsequent PD

Heritability enrichment implicates microglia in Parkinson's disease pathogenesis

OBJECTIVE: Understanding how different parts of the immune system contribute to pathogenesis in Parkinson's disease is a burning challenge with important therapeutic implications. We studied enrichment of common variant heritability for Parkinson's disease stratified by immune and brain cell types. METHODS: We used summary statistics from the most recent meta-analysis of genome-wide association studies in Parkinson's disease and partitioned heritability using linkage disequilibrium score regression, stratified for specific cell types as defined by open chromatin regions. We also validated enrichment results using a polygenic risk score approach and intersected disease-associated variants with epigenetic data and expression quantitative loci to nominate and explore a putative microglial locus. RESULTS: We found significant enrichment of Parkinson's disease risk heritability in open chromatin regions of microglia and monocytes. Genomic annotations overlapped substantially between these two cell types, and only the enrichment signal for microglia remained significant in a joint model. We present evidence suggesting P2RY12, a key microglial gene and target for the anti-thrombotic agent clopidogrel, as the likely driver of a significant Parkinson's disease association signal on chromosome 3. INTERPRETATION: Our results provide further support for the importance of immune mechanisms in PD pathogenesis, highlight microglial dysregulation as a contributing etiological factor and nominate a targetable microglial gene candidate as a pathogenic player. Immune processes can be modulated by therapy, with potentially important clinical implications for future treatment in Parkinson's disease

Finding genetically-supported drug targets for Parkinson's disease using Mendelian randomization of the druggable genome

There is currently no disease-modifying treatment for Parkinson's disease, a common neurodegenerative disorder. Here, the authors use genetic variation associated with gene and protein expression to find putative drug targets for Parkinson's disease using Mendelian randomization of the druggable genome. Parkinson's disease is a neurodegenerative movement disorder that currently has no disease-modifying treatment, partly owing to inefficiencies in drug target identification and validation. We use Mendelian randomization to investigate over 3,000 genes that encode druggable proteins and predict their efficacy as drug targets for Parkinson's disease. We use expression and protein quantitative trait loci to mimic exposure to medications, and we examine the causal effect on Parkinson's disease risk (in two large cohorts), age at onset and progression. We propose 23 drug-targeting mechanisms for Parkinson's disease, including four possible drug repurposing opportunities and two drugs which may increase Parkinson's disease risk. Of these, we put forward six drug targets with the strongest Mendelian randomization evidence. There is remarkably little overlap between our drug targets to reduce Parkinson's disease risk versus progression, suggesting different molecular mechanisms. Drugs with genetic support are considerably more likely to succeed in clinical trials, and we provide compelling genetic evidence and an analysis pipeline to prioritise Parkinson's disease drug development.Peer reviewe

Finding genetically-supported drug targets for Parkinson's disease using Mendelian randomization of the druggable genome

There is currently no disease-modifying treatment for Parkinson's disease, a common neurodegenerative disorder. Here, the authors use genetic variation associated with gene and protein expression to find putative drug targets for Parkinson's disease using Mendelian randomization of the druggable genome. Parkinson's disease is a neurodegenerative movement disorder that currently has no disease-modifying treatment, partly owing to inefficiencies in drug target identification and validation. We use Mendelian randomization to investigate over 3,000 genes that encode druggable proteins and predict their efficacy as drug targets for Parkinson's disease. We use expression and protein quantitative trait loci to mimic exposure to medications, and we examine the causal effect on Parkinson's disease risk (in two large cohorts), age at onset and progression. We propose 23 drug-targeting mechanisms for Parkinson's disease, including four possible drug repurposing opportunities and two drugs which may increase Parkinson's disease risk. Of these, we put forward six drug targets with the strongest Mendelian randomization evidence. There is remarkably little overlap between our drug targets to reduce Parkinson's disease risk versus progression, suggesting different molecular mechanisms. Drugs with genetic support are considerably more likely to succeed in clinical trials, and we provide compelling genetic evidence and an analysis pipeline to prioritise Parkinson's disease drug development.Peer reviewe

Genetic analysis of amyotrophic lateral sclerosis identifies contributing pathways and cell types

Despite the considerable progress in unraveling the genetic causes of amyotrophic lateral sclerosis (ALS), we do not fully understand the molecular mechanisms underlying the disease. We analyzed genome-wide data involving 78,500 individuals using a polygenic risk score approach to identify the biological pathways and cell types involved in ALS. This data-driven approach identified multiple aspects of the biology underlying the disease that resolved into broader themes, namely, neuron projection morphogenesis, membrane trafficking, and signal transduction mediated by ribonucleotides. We also found that genomic risk in ALS maps consistently to GABAergic interneurons and oligodendrocytes, as confirmed in human single-nucleus RNA-seq data. Using two-sample Mendelian randomization, we nominated six differentially expressed genes (ATG16L2, ACSL5, MAP1LC3A, MAPKAPK3, PLXNB2, and SCFD1) within the significant pathways as relevant to ALS. We conclude that the disparate genetic etiologies of this fatal neurological disease converge on a smaller number of final common pathways and cell types

Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.

The clinical manifestations of Parkinson's disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. We used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson's Disease Progression Marker Initiative (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson's Disease Biomarker Program (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression 5 years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 ± 0.01) for the slower progressing group (PDvec1), 0.87 ± 0.03 for moderate progressors, and 0.95 ± 0.02 for the fast-progressing group (PDvec3). We identified serum neurofilament light as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent cohort, released the analytical code, and developed models in an open science manner. Our data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes. We anticipate that machine learning models will improve patient counseling, clinical trial design, and ultimately individualized patient care

Multi-ancestry genome-wide association meta-analysis of Parkinson's disease.

Although over 90 independent risk variants have been identified for Parkinson's disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson's disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations

The Parkinson's Disease Mendelian Randomization Research Portal

Background Mendelian randomization is a method for exploring observational associations to find evidence of causality. Objective To apply Mendelian randomization between risk factors/phenotypic traits (exposures) and PD in a large, unbiased manner, and to create a public resource for research. Methods We used two‐sample Mendelian randomization in which the summary statistics relating to single‐nucleotide polymorphisms from 5,839 genome‐wide association studies of exposures were used to assess causal relationships with PD. We selected the highest‐quality exposure genome‐wide association studies for this report (n = 401). For the disease outcome, summary statistics from the largest published PD genome‐wide association studies were used. For each exposure, the causal effect on PD was assessed using the inverse variance weighted method, followed by a range of sensitivity analyses. We used a false discovery rate of 5% from the inverse variance weighted analysis to prioritize exposures of interest. Results We observed evidence for causal associations between 12 exposures and risk of PD. Of these, nine were effects related to increasing adiposity and decreasing risk of PD. The remaining top three exposures that affected PD risk were tea drinking, time spent watching television, and forced vital capacity, but these may have been biased and were less convincing. Other exposures at nominal statistical significance included inverse effects of smoking and alcohol. Conclusions We present a new platform which offers Mendelian randomization analyses for a total of 5,839 genome‐wide association studies versus the largest PD genome‐wide association studies available (https://pdgenetics.shinyapps.io/MRportal/). Alongside, we report further evidence to support a causal role for adiposity on lowering the risk of P

An integrated genomic approach to dissect the genetic landscape regulating the cell-to-cell transfer of α-synuclein

Neuropathological and experimental evidence suggests that the cell-to-cell transfer of α-synuclein has an important role in the pathogenesis of Parkinson's disease (PD). However, the mechanism underlying this phenomenon is not fully understood. We undertook a small interfering RNA (siRNA), genome-wide screen to identify genes regulating the cell-to-cell transfer of α-synuclein. A genetically encoded reporter, GFP-2A-αSynuclein-RFP, suitable for separating donor and recipient cells, was transiently transfected into HEK cells stably overexpressing α-synuclein. We find that 38 genes regulate the transfer of α-synuclein-RFP, one of which is ITGA8, a candidate gene identified through a recent PD genome-wide association study (GWAS). Weighted gene co-expression network analysis (WGCNA) and weighted protein-protein network interaction analysis (WPPNIA) show that those hits cluster in networks that include known PD genes more frequently than expected by random chance. The findings expand our understanding of the mechanism of α-synuclein spread