Parkinson’s disease (PD) is the most common neurodegenerative movement disorder, affecting 1% of the population over 65 years characterized clinically by both motor and non-motor symptoms accompanied by the preferential loss of dopamine neurons in the substantia nigra pars compacta. Here, we sequenced the exomes of 244 Parkinson’s patients selected from the Oxford Parkinson’s Disease Centre Discovery Cohort and, after quality control, 228 exomes were available for analyses. The PD patient exomes were compared to 884 control exomes selected from the UK10K datasets. No single non-synonymous (NS) single nucleotide variant (SNV) nor any gene carrying a higher burden of NS SNVs was significantly associated with PD status after multiple-testing correction. However, significant enrichments of genes whose proteins have roles in the extracellular matrix were amongst the top 300 genes with the most significantly associated NS SNVs, while regions associated with PD by a recent Genome Wide Association (GWA) study were enriched in genes containing PD-associated NS SNVs. By examining genes within GWA regions possessing rare PD-associated SNVs, we identified RAD51B. The protein-product of RAD51B interacts with that of its paralogue RAD51, which is associated with congenital mirror movements phenotypes, a phenotype also comorbid with PD.

Background There is large individual variation in both clinical presentation and progression between Parkinson’s disease patients. Generation of deeply and longitudinally phenotyped patient cohorts has enormous potential to identify disease subtypes for prognosis and therapeutic targeting. Methods Replicating across three large Parkinson’s cohorts (Oxford Discovery cohort (n = 842)/Tracking UK Parkinson’s study (n = 1807) and Parkinson’s Progression Markers Initiative (n = 472)) with clinical observational measures collected longitudinally over 5–10 years, we developed a Bayesian multiple phenotypes mixed model incorporating genetic relationships between individuals able to explain many diverse clinical measurements as a smaller number of continuous underlying factors (“phenotypic axes”). Results When applied to disease severity at diagnosis, the most influential of three phenotypic axes “Axis 1” was characterised by severe non-tremor motor phenotype, anxiety and depression at diagnosis, accompanied by faster progression in cognitive function measures. Axis 1 was associated with increased genetic risk of Alzheimer’s disease and reduced CSF Aβ1-42 levels. As observed previously for Alzheimer’s disease genetic risk, and in contrast to Parkinson’s disease genetic risk, the loci influencing Axis 1 were associated with microglia-expressed genes implicating neuroinflammation. When applied to measures of disease progression for each individual, integration of Alzheimer’s disease genetic loci haplotypes improved the accuracy of progression modelling, while integrating Parkinson’s disease genetics did not. Conclusions We identify universal axes of Parkinson’s disease phenotypic variation which reveal that Parkinson’s patients with high concomitant genetic risk for Alzheimer’s disease are more likely to present with severe motor and non-motor features at baseline and progress more rapidly to early dementia.

The generation of deeply phenotyped patient cohorts offers an enormous potential to identify disease subtypes with prognostic and therapeutic utility. Here, we quantify diverse Parkinson's disease patient phenotypes on continuous scales by identifying the underlying axes of phenotypic variation using a Bayesian multiple phenotype mixed model that incorporates genotypic relationships. This approach overcomes many of the limitations associated with clustering methods and better reflects the more continuous phenotypic variation observed amongst patients. We identify three principal axes of Parkinson's disease patient phenotypic variation which are reproducibly found across three independent, deeply and diversely phenotyped UK and US Parkinson's disease cohorts. These three axes explain over 75% of the observed clinical variation and remain robustly captured with a fraction of the clinically-recorded features. Using these axes as quantitative traits, we identify significant overlaps in the genetic risk associated with each axis and other human complex diseases, namely coronary artery disease and schizophrenia, providing new avenues for diseasemodifying therapies. Our study demonstrates how deeply phenotyped cohorts can be used to identify latent heritable disease-modifying traits.

ObjectivesTo explore the genetics of four Parkinson’s disease (PD) subtypes that have been previously described in two large cohorts of patients with recently diagnosed PD. These subtypes came from a data-driven cluster analysis of phenotypic variables.MethodsWe looked at the frequency of genetic mutations in glucocerebrosidase (GBA) and leucine-rich repeat kinase 2 against our subtypes. Then we calculated Genetic Risk Scores (GRS) for PD, multiple system atrophy, progressive supranuclear palsy, Lewy body dementia, and Alzheimer’s disease. These GRSs were regressed against the probability of belonging to a subtype in the two independent cohorts and we calculated q-values as an adjustment for multiple testing across four subtypes. We also carried out a Genome-Wide Association Study (GWAS) of belonging to a subtype.ResultsA severe disease subtype had the highest rates of patients carrying GBA mutations while the mild disease subtype had the lowest rates (p=0.009). Using the GRS, we found a severe disease subtype had a reduced genetic risk of PD (p=0.004 and q=0.015). In our GWAS no individual variants met genome wide significance (<5×10e-8) although four variants require further follow-up, meeting a threshold of <1×10e-6.ConclusionsWe have found that four previously defined PD subtypes have different genetic determinants which will help to inform future studies looking at underlying disease mechanisms and pathogenesis in these different subtypes of disease.