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Several prior studies have proposed the involvement of various brain regions and cell types in Parkinson’s disease (PD) pathology. Here, we performed snRNA-seq on the prefrontal cortex and anterior cingulate regions from post-mortem control and PD brain tissue. We found a significant association of oligodendrocytes and oligodendrocyte precursor cells with PD-linked risk loci and report several dysregulated genes and pathways, including regulation of tau-protein kinase activity, regulation of inclusion body assembly and protein processing involved in protein targeting to mitochondria.

BackgroundPick’s disease (PiD) is a rare and predominantly sporadic form of frontotemporal dementia that is classified as a primary tauopathy. PiD is pathologically defined by argyrophilic inclusion Pick bodies and ballooned neurons in the frontal and temporal brain lobes. PiD is characterised by the presence of Pick bodies which are formed from aggregated, hyperphosphorylated, 3-repeat tau proteins, encoded by theMAPTgene. TheMAPTH2 haplotype has consistently been associated with a decreased disease risk of the 4-repeat tauopathies of progressive supranuclear palsy and corticobasal degeneration, however its role in susceptibility to PiD is unclear. The primary aim of this study was to evaluate the association betweenMAPTH2 and risk of PiD.MethodsWe established the Pick’s disease International Consortium (PIC) and collected 338 (60.7% male) pathologically confirmed PiD brains from 39 sites worldwide. 1,312 neurologically healthy clinical controls were recruited from Mayo Clinic Jacksonville, FL (N=881) or Rochester, MN (N=431). For the primary analysis, subjects were directly genotyped forMAPTH1-H2 haplotype-defining variant rs8070723. In secondary analysis, we genotyped and constructed the six-variantMAPTH1 subhaplotypes (rs1467967, rs242557, rs3785883, rs2471738, rs8070723, and rs7521).FindingsOur primary analysis found that theMAPTH2 haplotype was associated with increased risk of PiD (OR: 1.35, 95% CI: 1.12-1.64 P=0.002). In secondary analysis involving H1 subhaplotypes, a protective association with PiD was observed for the H1f haplotype (0.0% vs. 1.2%, P=0.049), with a similar trend noted for H1b (OR: 0.76, 95% CI: 0.58-1.00, P=0.051). The 4-repeat tauopathy risk haplotypeMAPTH1c was not associated with PiD susceptibility (OR: 0.93, 95% CI: 0.70-1.25, P=0.65).InterpretationThe PIC represents the first opportunity to perform relatively large-scale studies to enhance our understanding of the pathobiology of PiD. This study demonstrates that in contrast to its protective role in 4R tauopathies, theMAPTH2 haplotype is associated with an increased risk of PiD. This finding is critical in directing isoform-related therapeutics for tauopathies.FundingSee funding section

BackgroundThe microtubule-associated protein tau (MAPT) gene is critical because of its putative role in the causal pathway of neurodegenerative diseases including Parkinson’s disease (PD). However, there is a lack of clarity regarding the link between the main H1 haplotype and risk of PD. Inconsistencies in reported association may be driven by genetic variability in the populations studied to date. Data onMAPThaplotype frequencies in the general population and association studies exploring the role ofMAPThaplotypes in conferring PD risk in black Africans are lacking.ObjectivesTo determine the frequencies ofMAPThaplotypes and explore the role of the H1 haplotype as a risk factor for PD risk and age at onset in Nigerian Africans.MethodsThe haplotype and genotype frequencies ofMAPTrs1052553 were analysed using PCR-based KASP™ in 907 individuals with PD and 1,022 age-matched neurologically normal controls from the Nigeria Parkinson’s Disease Research (NPDR) network cohort. Clinical data related to PD included age at study, age at onset, and disease duration.ResultsThe frequency of the mainMAPTH1 haplotype in this cohort was 98.7% in individuals with PD, and 99.1% in healthy controls (p=0.19). The H2 haplotype was present in 41/1929 (2.1%) of the cohort (PD - 1.3%; Controls - 0.9%; p=0.24). The most frequentMAPTgenotype was H1H1 (PD - 97.5%, controls - 98.2%). The H1 haplotype was not associated with PD risk after accounting for gender and age at onset (Odds ratio for H1/H1 vs H1/H2 and H2/H2: 0.68 (95% CI:0.39-1.28); p=0.23).ConclusionsOur findings support previous studies that report a low frequency of theMAPTH2 haplotype in black ancestry Africans, but document its occurrence in the Nigerian population (2.1%). In this cohort of black Africans with PD, theMAPTH1 haplotype was not associated with an increased risk or age at onset of PD.

BackgroundBi-allelic pathogenic variants inGBA1are the cause of Gaucher disease (GD1), a lysosomal storage disorder resulting from deficient glucocerebrosidase. HeterozygousGBA1variants are also a common genetic risk factor for Parkinson’s disease (PD). GD manifests with considerable clinical heterogeneity and is also associated with an increased risk of PD.ObjectiveTo investigate the contribution of PD risk variants to risk of PD in patients with GD1.MethodsWe studied 225 patients with GD1, including 199 without PD and 26 with PD. All cases were genotyped and the genetic data was imputed using common pipelines.ResultsOn average, patients with GD1 with PD have a significantly higher PD genetic risk score than those without PD (P=0.021).ConclusionsOur results indicate that variants included in the PD genetic risk score were more frequent in patients with GD1 who developed PD, suggesting that common risk variants may affect underlying biological pathways.Supplemental datahere

Overlapping symptoms and copathologies are common in closely related neurodegenerative diseases (NDDs). Investigating genetic risk variants across these NDDs can give further insight into disease manifestations. In this study we have leveraged genome-wide single nucleotide polymorphisms (SNPs) and genome-wide association study (GWAS) summary statistics to cluster patients based on their genetic status across identified risk variants for five NDDs (Alzheimer’s disease [AD], Parkinson’s disease [PD], amyotrophic lateral sclerosis [ALS], Lewy body dementia [LBD], and frontotemporal dementia [FTD]). The multi-disease and disease-specific clustering results presented here provide evidence that NDDs have more overlapping genetic etiology than previously expected and how neurodegeneration should be viewed as a spectrum of symptomology. These clustering analyses also show potential subsets of patients with these diseases that are significantly depleted for any known common genetic risk factors suggesting environmental or other factors at work. Establishing that NDDs with overlapping pathologies share genetic risk loci, future research into how these variants might have different effects on downstream protein expression, pathology and NDD manifestation in general is important for refining and treating NDDs.

The human genome contains numerous duplicated regions, such as parent-pseudogene pairs, causing sequencing reads to align equally well to either gene. The extent to which this ambiguity complicates transcriptomic analyses is currently unknown. This is concerning as many parent genes have been linked to disease, includingGBA1,causally linked to both Parkinson’s and Gaucher disease. We find that most of the short sequencing reads that map toGBA1, also map to its pseudogene,GBAP1. Using long-read RNA-sequencing in human brain, where all reads mapped uniquely, we demonstrate significant differences in expression compared to short-read data. We identify novel transcripts from bothGBA1andGBAP1, including protein-coding transcripts that are translatedin vitroand detected in proteomic data, but that lack GCase activity. By combining long-read with single-nuclear RNA-sequencing to analyse brain-relevant cell types we demonstrate that transcript expression varies by brain region with cell-type-selectivity. Taken together, these results suggest a non-lysosomal function for both GBA1 and GBAP1 in brain. Finally, we demonstrate that inaccuracies in annotation are widespread among parent genes, with implications for many human diseases.

BackgroundGBA1 variants are among the most common genetic risk factors for Parkinson’s Disease (PD). GBA1 variants can be classified into three categories based on their role in Gaucher’s Disease (GD) or PD: severe, mild, and risk variant (for PD).ObjectivesThis paper aims to generate and share a comprehensive database for GBA1 variants reported in PD to support future research and clinical trials.MethodsWe performed a literature search for all GBA1 variants that have been reported in PD. The data has been standardized and complimented with variant classification, Odds Ratio (OR) if available and other data.ResultsWe found 371 GBA1 variants reported in PD: 22 mild, 84 severe, 3 risk variants, and 262 of unknown status. We created a browser, containing up-to-date information on these variants (https://pdgenetics.shinyapps.io/GBA1Browser/).ConclusionsThe classification and browser presented in this work should inform and support basic, translational, and clinical research on GBA1-PD.

BackgroundThe 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. The emergence of machine learning to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities to address this critical need.Methods and FindingsWe used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson’s Disease Progression Marker Initiative (PPMI) (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 (PDBP) (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 five 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 (Nfl) as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent validation cohort, released the analytical code, and developed models in an open science manner.ConclusionsOur 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 that might have been masked by cohort heterogeneity. We anticipate that machine learning models will improve patient counseling, clinical trial design, allocation of healthcare resources, and ultimately individualized patient care.

Genome-wide association studies (GWAS) of Alzheimer’s disease are predominantly carried out in European ancestry individuals despite the known variation in genetic architecture and disease prevalence across global populations. We leveraged published GWAS summary statistics from European, East Asian, and African American populations, and an additional GWAS from a Caribbean Hispanic population using previously reported genotype data to perform the largest multi-ancestry GWAS meta-analysis of Alzheimer’s disease and related dementias to date. This method allowed us to identify two independent novel disease-associated loci on chromosome 3. We also leveraged diverse haplotype structures to fine-map nine loci with a posterior probability >0.8 and globally assessed the heterogeneity of known risk factors across populations. Additionally, we compared the generalizability of multi-ancestry- and single-ancestry-derived polygenic risk scores in a three-way admixed Colombian population. Our findings highlight the importance of multi-ancestry representation in uncovering and understanding putative factors that contribute to risk of Alzheimer’s disease and related dementias.

BackgroundThere are 90 genetic risk variants for Parkinson’s disease (PD) but currently only five nominated loci for PD progression. The biology of PD progression is likely to be of central importance in defining mechanisms that can be used to develop new treatments.MethodsWe studied 6,766 PD patients, over 15,340 visits with a mean follow-up of between 4.2 and 15.7 years and carried out a genome wide survival study for time to motor progression, defined by reaching Hoehn and Yahr stage 3 or greater, cognitive impairment as defined by serial cognitive examination, and death (mortality).FindingsThere was a robust effect of the APOE ε4 allele on mortality and cognitive impairment in PD. We identified three novel loci for mortality and motor progression, and nominated genes based on physical proximity or expression quantitative trait loci data. One locus within the TBXAS1 gene encoding thromboxane A synthase 1 was associated with mortality in PD (HR = 2.04 [95% CI 1.63 to 2.56], p-value = 7.71 x 10-10). Another locus near the SYT10 gene encoding synaptotagmin 10 was associated with mortality just above genome-wide significance (HR=1.36 [95% CI 1.21 to 1.51], p-value=5.31×10-8). A genomic variant associated with the expression of ADORA2A, encoding the A2A adenosine receptor, was associated with motor progression (HR=4.83 [95% CI 2.89 to 8.08], p-value=1.94×10-9). Only the non-Gaucher disease causing GBA PD risk variant E326K, of the known PD risk variants, was associated with progression in PD.InterpretationWe report three novel loci associated with PD progression or mortality. Further work is needed to understand the links between these genomic variants and the underlying disease biology. However, thromboxane synthesis, vesicular peptidergic neurotransmitter release and the A2A adenosine receptor may represent new candidates for disease modification in PD.RESEARCH IN CONTEXTEvidence before this studyWe searched PubMed for articles on Parkinson’s disease (PD) with no language restrictions from database inception up to February 9, 2022. We used the search terms “Parkinson disease AND genetics” and “disease progression OR survival OR mortality OR prognosis OR longitudinal studies”. We also conducted this search with the addition of “genome-wide association study” (GWAS) to focus on these genome-wide analyses. There are now three published large-scale GWASs investigating PD progression, and many candidate variant studies. However, no genome-wide studies have reported on survival/mortality in PD.Added value of this studyTo our knowledge, this is the first GWAS of survival in PD. Our study highlights new loci influencing survival in PD, including TBXAS1 and SYT10. We also conducted GWASs of progression to other clinical milestones, Hoehn and Yahr stage 3 or greater, and cognitive impairment. We show that APOE influences both mortality and cognitive progression in PD, and report an additional locus influencing expression of ADORA2A which affects the rate of motor progression.Implications of all the available evidenceWith the exception of APOE, we report new loci which have not been previously associated with PD progression or for mortality and ageing in the general population. These loci could be investigated in functional studies as potential drug targets to stop or slow progression of PD. In addition, new genetic loci can help to improve our understanding of the biology of PD progression and prediction of progression. Further replication of these loci is also needed in independent, longitudinal PD cohorts.

BACKGROUNDWith recent findings connecting Epstein-Barr virus to increased risk of multiple sclerosis and growing concerns regarding the potential neurological impact of the coronavirus pandemic, we surveyed biobank scale real-world data to identify potential links between viral exposures and neurodegenerative disease risks.METHODSTo assess the potential increased risk of neurodegenerative diseases due to viral exposures, we mined time series data from FinnGen as a discovery dataset and cross-sectional data from the UK Biobank as a replication dataset for 73 pairs of common viral exposures and neurodegenerative disease outcomes. We investigated the impact of time span between viral exposure and disease risk using time series data from FinnGen at 1, 5, and 15 year intervals between exposure and disease onset. This analysis helped us to avoid the potential confounding of concurrent diagnosis due to hospitalization with viral infection. Further, to address the possible bias of reverse causality we examined risk for severe viral infections after NDD diagnosis.RESULTSWe identified 45 viral exposures significantly associated with increased risk of post-exposure neurodegenerative disease onset after multiple test correction in the discovery phase using longitudinal data. 22 of these associations were replicated in cross sectional data from the UK Biobank. The largest effect association we saw replicated was between viral encephalitis exposure and Alzheimer’s disease, with discovery hazard estimates of ∼30 and a replication odds ratio of ∼22. We also replicated the association between Epstein-Barr virus exposure and multiple sclerosis 5-15 years before diagnosis of multiple sclerosis. In total, 17 virus/neurodegeneration pairs were significant with 5-15 years between viral exposure and NDD diagnosis. In an investigation of potential confounding and reverse causality, we generally see larger hazard ratios associated with viruses preceding NDD diagnosis than viruses post NDD diagnosis.CONCLUSIONSViral exposures contribute to later in life risk of neurodegenerative disease with increased risk of neurodegeneration still significant at up to 15 years between some events in this report.

BackgroundOpen science and collaboration are necessary to facilitate the advancement of Parkinson’s disease (PD) research. Hackathons are collaborative events that bring together people with different skill sets and backgrounds to generate resources and creative solutions to problems. These events can be used as training and networking opportunities.ObjectiveTo coordinate a virtual hackathon to develop novel PD research tools.Methods49 early career scientists from 12 countries collaborated in a virtual 3-day hackathon event in May 2021, during which they built tools and pipelines with a focus on PD. Resources were created with the goal of helping scientists accelerate their own research by having access to the necessary code and tools.ResultsEach team was allocated one of nine different projects, each with a different goal. These included developing post-genome-wide association studies (GWAS) analysis pipelines, downstream analysis of genetic variation pipelines, and various visualization tools.ConclusionHackathons are a valuable approach to inspire creative thinking, supplement training in data science, and foster collaborative scientific relationships, which are foundational practices for early career researchers. The resources generated can be used to accelerate research on the genetics of PD.

The association between glucocerebrosidase (GCase), encoded byGBA, and Parkinson’s disease highlights the role of the lysosome in Parkinson’s disease pathogenesis. Genome-wide association studies (GWAS) in Parkinson’s disease have revealed multiple associated loci, including theGALClocus on chromosome 14.GALCencodes the lysosomal enzyme galactosylceramidase (GalCase), which plays a pivotal role in the glycosphingolipid metabolism pathway. It is still unclear whetherGALCis the gene driving the association in the chromosome 14 locus, and if so, by which mechanism.We first aimed to examine whether variants in theGALClocus and across the genome are associated with GalCase activity. We performed a GWAS in two independent cohorts from a)Columbia University and b)the Parkinson’s Progression Markers Initiative study, followed by a meta-analysis with a total of 976 Parkinson’s disease patients and 478 controls with available data on GalCase activity. We further analyzed the effects of commonGALCvariants on expression and GalCase activity using genomic colocalization methods. Mendelian randomization was used to study whether GalCase activity may be causal in Parkinson’s disease. To study the role of rareGALCvariants we analyzed sequencing data from 5,028 Parkinson’s disease patients and 5,422 controls. Additionally, we studied the functional impact ofGALCknock-out on alpha-synuclein accumulation and on GCase activity in neuronal cell models and performedin silicostructural analysis of commonGALCvariants associated with altered GalCase activity.The top hit in Parkinson’s disease GWAS in theGALClocus, rs979812, is associated with increased GalCase activity (b=1.2; se=0.06; p=5.10E-95). No other variants outside theGALClocus were associated with GalCase activity. Colocalization analysis demonstrated that rs979812 was also associated with increased GalCase expression. Mendelian randomization suggested that increased GalCase activity may be causally associated with Parkinson’s disease (b=0.025, se=0.007, p=0.0008). We did not find an association between rareGALCvariants and Parkinson’s disease.GALCknockout using CRISPR-Cas9 did not lead to alpha-synuclein accumulation, further supporting that increased rather than reduced GalCase levels may be associated with Parkinson’s disease. The structural analysis demonstrated that the common variant p.I562T may lead to improper maturation of GalCase affecting its activity.Our results nominateGALCas the gene associated with Parkinson’s disease in this locus and suggest that the association of variants in theGALClocus may be driven by their effect of increasing GalCase expression and activity. Whether altering GalCase activity could be considered as a therapeutic target should be further studied.

Modeling human disease in human stem cells requires precise, scarless editing of single nucleotide variants (SNV) on one or both chromosomes. Here we describe improved conditions for Cas9 RNP editing of SNVs that yield high rates of biallelic homology-directed repair. To recover both heterozygous and homozygous SNV clones, catalytically inactive ‘dCas9’was added to moderate high activity Cas9 RNPs. dCas9 can also block re-cutting and damage to SNV alleles engineered with non-overlapping guide RNAs.

Rapid eye movement (REM) sleep behavior disorder (RBD), enactment of dreams during REM sleep, is an early clinical symptom of alpha-synucleinopathies. RBD also defines more severe forms of alpha-synucleinopathies. The genetic background of RBD and its underlying mechanisms are not well understood. Here, we performed the first genome-wide association study of RBD, identifying five RBD risk loci. Expression analyses highlight SNCA-AS1 and SCARB2 differential expression in different brain regions in RBD, with SNCA-AS1 further supported by colocalization analyses. Genetic risk score and other analyses provide further insights into RBD genetics, highlighting RBD as a unique subpopulation that will allow future early intervention.

PRKN mutations are the most common recessive cause of Parkinson’s disease (PD) and are a promising target for gene and cell replacement therapies. Identification of biallelic PRKN patients (PRKN-PD) at the population scale, however, remains a challenge, as roughly half are copy number variants (CNVs) and many single nucleotide polymorphisms (SNPs) are of unclear significance. Additionally, the true prevalence and disease risk associated with heterozygous PRKN mutations is unclear, as a comprehensive assessment of PRKN SNPs and CNVs has not been performed at a population scale. To address these challenges, we evaluated PRKN mutations in 2 cohorts analyzed with both a genotyping array and exome or genome sequencing: the NIH PD cohort, a deeply phenotyped cohort of PD patients, and the UK Biobank, a population scale cohort with nearly half a million participants. Genotyping array identified the majority of PRKN mutations and at least 1 mutation in most biallelic PRKN mutation carriers in both cohorts. Additionally, in the NIH-PD cohort, functional assays of patient fibroblasts resolved variants of unclear significance in biallelic carriers and ruled out cryptic loss of function variants in monoallelic carriers. In the UK Biobank, we identified 2,692 PRKN CNVs from genotyping array data from nearly half a million participants (the largest collection to date). Deletions or duplications involving exons 2 accounted for roughly half of all CNVs and the vast majority (88%) involved exons 2, 3, or 4. Combining estimates from whole exome sequencing (from ∼200,000 participants) and genotyping array data, we found a pathogenic PRKN mutation in 1.8% of participants and 2 mutations in ∼1/7,800 participants. Those with 1 PRKN pathogenic variant were as likely as non-carriers to have PD (OR = 0.91, CI= 0.58 – 1.38, p-value = 0.76) or a parent with PD (OR = 1.12, CI = 0.94 – 1.31, p-value = 0.19). Together our results demonstrate that heterozygous pathogenic PRKN mutations are common in the population but do not increase the risk of PD. Additionally, they suggest a cost-effective framework to screen for biallelic PRKN patients at the population scale for targeted studies.

Parkinson’s disease (PD) is a complex neurodegenerative disorder with a strong genetic component, where most known disease-associated variants are single nucleotide polymorphisms (SNPs) and small insertions and deletions (Indels). DNA repetitive elements account for >50% of the human genome, however little is known of their contribution to PD etiology. While select short tandem repeats (STRs) within candidate genes have been studied in PD, their genome-wide contribution remains unknown. Here we present the first genome-wide association study (GWAS) of STRs in PD. Through a meta-analysis of 16 imputed GWAS cohorts from the International Parkinson’s Disease Genomic Consortium (IPDGC), totalling 39,087 individuals (16,642 PD cases and 22,445 controls of European ancestry) we identified 34 genome-wide significant STR loci (p < 5.34×10-6), with the strongest signal located inKANSL1(chr17:44205351:[T]11, p=3×10-39, OR=1.31 [CI 95%=1.26-1.36]). Conditional-joint analyses suggested that 4 significant STRs mapping nearbyNDUFAF2, TRIML2, MIRNA-129-1andNCOR1were independent from known PD risk SNPs. Including STRs in heritability estimates increased the variance explained by SNPs alone. Gene expression analysis of STRs (eSTR) in RNASeq data from 13 brain regions, identified significant associations of STRs influencing the expression of multiple genes, including PD known genes. Further functional annotation of candidate STRs revealed that significant eSTRs withinNUDFAF2andZSWIM7overlap with regulatory features and are associated with change in the expression levels of nearby genes. Here we show that STRs at known and novel candidate PD loci contribute to PD risk, and have functional effects in disease-relevant tissues and pathways, supporting previously reported disease-associated genes and giving further evidence for their functional prioritization. These data represent a valuable resource for researchers currently dissecting PD risk loci.

Parkinson’s disease (PD) is a complex neurodegenerative disorder in which both rare and common genetic variants contribute to disease risk. Multiple genes have been reported to be linked to monogenic PD, but these only explain a fraction of the observed familial aggregation. Rare variants in RIC3 have been suggested to be associated with PD in the Indian population. However, replication studies yielded inconsistent results. We further investigate the role of RIC3 variants in PD in European cohorts using individual-level genotyping data from 14,671 PD patients and 17,667 controls, as well as whole-genome sequencing data from 1,615 patients and 961 controls. We also investigated RIC3 using summary statistics from a Latin American cohort of 1,481 individuals, and from a cohort of 31,575 individuals of East Asian ancestry. We did not identify any association between RIC3 and PD in any of the cohorts. However, more studies of rare variants in non-European ancestry populations, in particular South Asian populations, are necessary to further evaluate the world-wide role of RIC3 in PD etiology.

BACKGROUNDRisk factors for Parkinson’s disease (PD) can be more or less relevant to a population due to population-specific genetic architecture, local lifestyle habits, and environmental exposures. Therefore, it is essential to study PD at a local, regional, and continental scale in order to increase the knowledge on disease etiology.OBJECTIVEWe aimed to investigate the contribution of genetic and environmental factors to PD in a new Swedish case-control cohort.METHODSPD patients (n=929) and matched population-based controls (n=935) from the southernmost county in Sweden were included in the cohort. Information on environmental exposures was obtained using questionnaires at inclusion. Genetic analyses included a genome-wide association study (GWAS), haplotype assessment, and a risk profile analysis using cumulative genetic risk scores.RESULTSThe cohort is a representative PD case-control cohort (64% men, mean age at diagnosis=67 years, median Hoehn and Yahr score=2.0), in which previously reported associations between PD and environmental factors, such as tobacco, could be confirmed. We describe the first GWAS of PD solely composed of PD patients from Sweden, and confirm associations to well-established risk alleles in SNCA. In addition, we nominate an unconfirmed and potentially population-specific genome-wide significant association in the PLPP4 locus (rs12771445).CONCLUSIONSThis work provides an in-depth description of a new PD case-control cohort from southern Sweden, giving insights into environmental and genetic risk factors of PD in the Swedish population.

In the FOUNdational Data INitiative for Parkinson’s Disease (FOUNDIN-PD) we sought to produce a multi-layered molecular dataset in a large cohort of 95 Induced pluripotent stem cells (iPSC) lines at multiple timepoints during differentiation to dopaminergic (DA) neurons, a major affected cell type in Parkinson’s Disease (PD). The lines are derived from the Parkinson’s Progression Markers Initiative (PPMI) study that includes both people with PD and unaffected individuals across a wide range of polygenic risk scores (PRS) with both risk variants identified by genome-wide association studies (GWAS), and monogenic causal alleles. We generated genetic, epigenetic, regulatory, transcriptomic, proteomic, and longitudinal cellular imaging data from iPSC-derived DA neurons to understand key molecular relationships between disease associated genetic variation and proximate molecular events in a PD relevant cell-type. Analyses of all data modalities collected in FOUNDIN-PD suggest that the differentiation to DA neurons, while not fully mature, was successful and robust. Interrogation of PD genetic risk in this relevant cellular context may elucidate the functional effects of some of these risk variants alone or in combination with other variants. These data reveal that DA neurons derived from human iPSC provide a valuable cellular context and foundational atlas for modeling PD-related genetic risk. In addition to making the data and analyses for this molecular atlas readily available, we have integrated these data into the browsable FOUNDIN-PD data portal (https://www.foundinpd.org) to be used as a resource for understanding the molecular pathogenesis of PD.

BackgroundThe leucine-rich repeat kinase 2 (LRRK2) gene harbors both rare highly damaging missense variants (e.g. p.G2019S) and common non-coding variants (e.g. rs76904798) with lower effect sizes that are associated with Parkinson’s disease risk.ObjectivesThis study aimed to investigate in a large meta-analysis whether the LRRK2 GWAS signal represented by rs76904798 is independently associated with Parkinson’s disease risk from LRRK2 coding variation, and whether complex linkage disequilibrium structures with p.G2019S and the 5’ non-coding haplotype account for the association of LRRK2 coding variants.MethodsWe performed a meta-analysis using imputed genotypes from 17,838 cases, 13,404 proxy-cases and 173,639 healthy controls of European ancestry. We excluded carriers of p.G2019S and/or rs76904798 to clarify the role of LRRK2 coding variation in mediating disease risk, and excluded carriers of relatively rare LRRK2 coding variants to assess the independence of rs76904798. We also investigated the co-inheritance of LRRK2 coding variants with p.G2019S, rs76904798 and p.N2081D.ResultsLRRK2 rs76904798 remained significantly associated with Parkinson’s disease after excluding carriers of relatively rare LRRK2 coding variants. LRRK2 p.R1514Q and p.N2081D were frequently co-inherited with rs76904798 and the allele distribution of p.S1647T significantly changed among cases after removing rs76904798 carriers.ConclusionsThese data suggest that the LRRK2 coding variants previously linked to Parkinson’s disease (p.N551K, p.R1398H, p.M1646T and p.N2081D) do not drive the 5’ non-coding GWAS signal. These data, however, do not preclude the independent association of the haplotype p.N551K-p.R1398H and p.M1646T with altered disease risk.

Parkinson’s disease (PD) is a progressive neurological disorder caused by both genetic and environmental factors. A recent finding has suggested an association between KTN1 genetic variants and changes in its expression in the putamen and substantia nigra brain regions and an increased risk for PD. Here, we examine the link between PD susceptibility and KTN1 using individual-level genotyping data and summary statistics from the most recent genome-wide association studies (GWAS) for PD risk and age at onset from the International Parkinson’s Disease Genomics Consortium (IPDGC), as well as whole-genome sequencing data from the Accelerating Medicines Partnership Parkinson’s disease (AMP-PD) initiative. To investigate the potential effect of changes in KTN1 expression on PD compared to healthy individuals, we further assess publicly available expression quantitative trait loci (eQTL) results from GTEx v8 and BRAINEAC and transcriptomics data from AMP-PD. Overall, we found no genetic associations between KTN1 and PD in our cohorts but found potential evidence of differences in mRNA expression, which needs to be further explored.

SUMMARYBackgroundPersonalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multi-modal data is key moving forward. We build upon previous work to deliver multi-modal predictions of Parkinson’s Disease (PD).MethodsWe performed automated ML on multi-modal data from the Parkinson’s Progression Marker Initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson’s Disease Biomarker Program (PDBP) dataset. Finally, networks were built to identify gene communities specific to PD.FindingsOur initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification, increased the diagnosis prediction accuracy (balanced accuracy) and other metrics. Combining data modalities outperforms the single biomarker paradigm. UPSIT was the largest contributing predictor for the classification of PD. The transcriptomic data was used to construct a network of disease-relevant transcripts.InterpretationWe have built a model using an automated ML pipeline to make improved multi-omic predictions of PD. The model developed improves disease risk prediction, a critical step for better assessment of PD risk. We constructed gene expression networks for the next generation of genomics-derived interventions. Our automated ML approach allows complex predictive models to be reproducible and accessible to the community.FundingNational Institute on Aging, National Institute of Neurological Disorders and Stroke, the Michael J. Fox Foundation, and the Global Parkinson’s Genetics Program.RESEARCH IN CONTEXTEvidence before this studyPrior research into predictors of Parkinson’s disease (PD) has either used basic statistical methods to make predictions across data modalities, or they have focused on a single data type or biomarker model. We have done this using an open-source automated machine learning (ML) framework on extensive multi-modal data, which we believe yields robust and reproducible results. We consider this the first true multi-modality ML study of PD risk classification.Added value of this studyWe used a variety of linear, non-linear, kernel, neural networks, and ensemble ML algorithms to generate an accurate classification of both cases and controls in independent datasets using data that is not involved in PD diagnosis itself at study recruitment. The model built in this paper significantly improves upon our previous models that used the entire training dataset in previous work1. Building on this earlier work, we showed that the PD diagnosis can be refined using improved algorithmic classification tools that may yield potential biological insights. We have taken careful consideration to develop and validate this model using public controlled-access datasets and an open-source ML framework to allow for reproducible and transparent results.Implications of all available evidenceTraining, validating, and tuning a diagnostic algorithm for PD will allow us to augment clinical diagnoses or risk assessments with less need for complex and expensive exams. Going forward, these models can be built on remote or asynchronously collected data which may be important in a growing telemedicine paradigm. More refined diagnostics will also increase clinical trial efficiency by potentially refining phenotyping and predicting onset, allowing providers to identify potential cases earlier. Early detection could lead to improved treatment response and higher efficacy. Finally, as part of our workflow, we built new networks representing communities of genes correlated in PD cases in a hypothesis-free manner, showing how new and existing genes may be connected and highlighting therapeutic opportunities.

Parkinson’s disease (PD) is a complex neurodegenerative disorder. Males are on average ∼1.5 times more likely to develop PD compared to females. Over the years genome-wide association studies (GWAS) have identified numerous genetic risk factors for PD, however it is unclear whether genetics contribute to disease etiology in a sex-specific manner.In an effort to study sex-specific genetic factors associated with PD, we explored two large genetic datasets from the International Parkinson’s Disease Genomics Consortium and the UK Biobank consisting of 13,020 male PD cases, 7,936 paternal proxy cases, 89,660 male controls, 7,947 female PD cases, 5,473 maternal proxy cases and 90,662 female controls. We performed GWAS meta-analyses to identify distinct patterns of genetic risk contributing to disease in male versus female PD cases.In total 19 genome-wide significant regions were identified, and no sex-specific effects were observed. A high genetic correlation between the male and female PD GWASes was identified (rg=0.877) and heritability estimates were identical between male and female PD cases (∼20%).We did not detect any significant genetic differences between male or female PD cases. Our study does not support the notion that common genetic variation on the autosomes could explain the difference in prevalence of PD between males and females at least when considering the current sample size under study. Further studies are warranted to investigate the genetic architecture of PD explained by X and Y chromosomes and further evaluate environmental effects that could potentially contribute to PD etiology in male versus females.

Studies of the genetic basis of Parkinson’s disease (PD) have identified many disease-associated genetic variants, but the mechanisms linking variants to pathogenicity are largely unknown. PD risk is attributed to both coding mutations in the Leucine-rich repeat kinase 2 (LRRK2) gene and to common non-coding variation upstream of the LRRK2 locus. Here we show that the influence of genotype at non-coding variant rs76904798 on LRRK2 expression is propagated specifically through microglia, in contrast to evaluations based on general rather than genotype-dependent expression. We find evidence of microglia-specific regulatory regions that may modulate LRRK2 expression using single nuclei sequencing analyses of human frontal cortex and confirm these results in a human induced pluripotent stem cell-derived microglia model. Our study demonstrates that cell type is an important consideration in interrogation of the role of non-coding variation in disease pathogenesis.

SUMMARYBackgroundPrevious research using genome wide association studies (GWAS) has identified variants that may contribute to lifetime risk of multiple neurodegenerative diseases. However, whether there are common mechanisms that link neurodegenerative diseases is uncertain. Here, we focus on one gene, GRN, encoding progranulin, and the potential mechanistic interplay between genetic risk, gene expression in the brain and inflammation across multiple common neurodegenerative diseases.MethodsWe utilized GWAS, expression quantitative trait locus (eQTL) mapping and Bayesian colocalization analyses to evaluate potential causal and mechanistic inferences. We integrate various molecular data types from public resources to infer disease connectivity and shared mechanisms using a data driven process.FindingseQTL analyses combined with GWAS identified significant functional associations between increasing genetic risk in the GRN region and decreased expression of the gene in Parkinson’s, Alzheimer’s and amyotrophic lateral sclerosis. Additionally, colocalization analyses show a connection between blood based inflammatory biomarkers relating to platelets and GRN expression in the frontal cortex.InterpretationGRN expression mediates neuroinflammation function related to general neurodegeneration. This analysis suggests shared mechanisms for Parkinson’s, Alzheimer’s and amyotrophic lateral sclerosis.FundingNational Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J. Fox Foundation.

Understanding the molecular mechanisms underlying frontotemporal dementia (FTD) is essential for the development of successful therapies. Systematic studies on human post-mortem brain tissue of patients with genetic subtypes of FTD are currently lacking. The Risk and Modyfing Factors of Frontotemporal Dementia (RiMod-FTD) consortium therefore has generated a multi-omics dataset for genetic subtypes of FTD to identify common and distinct molecular mechanisms disturbed in disease. Here, we present multi-omics datasets generated from the frontal lobe of post-mortem human brain tissue from patients with mutations in MAPT, GRN and C9orf72 and healthy controls. This data resource consists of four datasets generated with different technologies to capture the transcriptome by RNA-seq, and small RNA-seq, and supplemented this date with CAGE-seq, and methylation profiling. We show concrete examples on how to use the resulting data and confirm current knowledge about FTD and identify new processes for further investigation. This extensive multi-omics dataset holds great value to reveal new research avenues for this devastating disease.

BackgroundWhole-genome sequencing (WGS) data is available from several large studies across a variety of diseases and traits. However, massive storage and computation resources are required to use these data, and, to achieve the sufficient power for discoveries, harmonization of multiple cohorts is critical.ObjectivesThe Accelerating Medicines Partnership Parkinson’s Disease (AMP PD) program has developed a research platform for Parkinson’s disease (PD) which integrates the storage and analysis of WGS data, RNA expression data, and clinical data, harmonized across multiple cohort studies.MethodsThe version 1 release contains WGS data derived from 3,941 participants from 4 cohorts. Samples underwent joint genotyping by the TOPMed Freeze 9 Variant Calling Pipeline. We performed descriptive analyses of these WGS data using the AMP PD platform.ResultsThe clinical diagnosis of participants in version 1 release includes 2,005 idiopathic PD patients, 963 healthy controls, 64 prodromal subjects, 62 clinically diagnosed PD subjects without evidence of dopamine deficit (SWEDD) and 705 participants of genetically enriched cohorts carrying PD risk associated GBA variants or LRRK2 variants in which 304 were affected. We did not observe a significant enrichment of pathogenic variants in the idiopathic PD group, but the polygenic risk score (PRS) was higher in PD both in non-genetically enriched cohorts and genetically enriched cohorts. The population analysis showed a correlation between genetically enriched cohorts and Ashkenazi Jewish ancestry.ConclusionsWe describe the genetic component of the AMP PD platform, a solution to democratise data access and analysis for the PD research community.(d) Financial Disclosure/CoI

ObjectiveTo fine map the association between human leukocyte antigen (HLA) genes and Parkinson’s disease (PD) that was discovered using genome-wide association studies (GWASs).MethodsWe performed a thorough analysis of the HLA locus in 13,770 PD patients, 20,214 proxy-cases and 490,861 controls of European origin. We used GWAS data to impute HLA types and performed multiple regression models to examine the association of specific HLA types, different haplotypes and specific amino acid changes. We further performed conditional analyzes to identify specific alleles or genetic variants that drive the association with PD.ResultsFour HLA types were associated with PD after correction for multiple comparisons, HLA-DQA1*03:01, HLA-DQB1*03:02, HLA-DRB1*04:01 and HLA-DRB1*04:04. Haplotype analyzes followed by amino-acid analysis and conditional analyzes suggested that the association is protective and primarily driven by three specific amino acid polymorphisms present in most HLA-DRB1*04 subtypes - 11V, 13H and 33H (OR=0.87 95%CI=0.83-0.90, p−9 for all three variants). No other effects were present after adjustment for these amino acids.ConclusionsOur results suggest that specific variants in the HLA-DRB1 gene are associated with reduced risk of PD, providing additional evidence for the role of the immune system in PD. Although effect size is small and has no diagnostic significance, understanding the mechanism underlying this association may lead to identification of new targets for therapeutics development.

Genetic risk factors are occasionally shared between different neurodegenerative diseases. Previous studies have linked ANG, a gene encoding angiogenin, to both Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS). Functional studies suggest ANG plays a neuroprotective role in both PD and ALS by reducing cell death. We further explored the genetic association between ANG and PD by analyzing genotype data from the International Parkinson’s Disease Genomics Consortium (IPDGC) (14,671 cases and 17,667 controls) and whole genome sequencing (WGS) data from the Accelerating Medicines Partnership - Parkinson’s disease initiative (AMP-PD, https://amp-pd.org/) (1,647 cases and 1,050 controls). Our analysis did not replicate the findings of previous studies and found no significant association between ANG variants and PD risk.

ImportanceBiomarkers for the early detection of Parkinson’s disease (PD) are needed. Patients with PD display differences in peripheral blood biomarkers of immune function, including leukocyte differential counts and C-reactive protein (CRP), compared to controls. These differences may be useful biomarkers to predict PD, and may shed light on PD pathogenesis.ObjectivesTo identify whether peripheral immune dysregulation is a pre-diagnostic feature of PD, and whether it plays a causal role.DesignCross-sectional association analysis of the relationship between differential leukocyte count and other markers of acute inflammation at enrolment, and incident cases of PD in UK Biobank. We used Mendelian randomization to establish whether differences in leukocyte differential counts have a causal influence on risk of PD.SettingUK Biobank; a population-based cohort with over 500,000 participants aged 40-69 recruited in the UK between 2006 and 2010.ParticipantsPD cases were defined as individuals with an ICD-10 coded diagnosis of PD. Cases were defined as ’incident’ if their age at diagnosis was greater than their age at recruitment to UKB. ’Controls’ were defined as individuals without a diagnosis of PD. After applying exclusion criteria for pre-existing health conditions that can influence blood counts, 507 incident PD cases and 328,280 controls were included in the analysis.ExposureBlood cell markers (absolute and relative counts) and other markers of inflammation were obtained from blood tests of participants taken at the initial visit.ResultsLower lymphocyte count was associated with increased odds of incident PD (odds ratio [OR] 0.77, 95% confidence interval [CI] 0.65-0.90). There was weaker evidence of association between lower eosinophil and monocyte counts, lower CRP, and higher neutrophil counts on risk of incident PD. The association between lymphopenia and incident PD remained robust to sensitivity analyses. Mendelian randomization analyses suggested that the effect of low lymphocyte count on PD risk was causal (OR 0.91, 95% CI 0.85 - 0.99).Conclusions and relevanceIn this large, prospective setting, lower lymphocyte count was associated with higher risk of subsequent PD diagnosis. Furthermore genetic evidence supported a causal role for lymphocyte count on PD risk.Key pointsQuestionIs the leukcoyte differential count a feature of pre-diagnostic Parkinson’s disease?FindingsIn the UK Biobank, a longitudinal cohort study with over 500,000 participants, lower lymphocyte count was associated with a 23% increased odds of incident PD, a significant difference. Mendelian randomisation revealed a convincing causal effect for low lymphocyte count on PD risk.MeaningPre-diagnostic Parkinson’s disease is associated with lower lymphocyte counts; the suggestion of causal effect may shed light on PD pathogenesis.

Parkinson’s disease (PD) is a genetically complex neurodegenerative disease with ~20 genes known to contain mutations that cause PD or atypical parkinsonism and 90 common genetic risk factors. Large-scale next-generation sequencing projects have revolutionized genomics research. Applying these data to PD, many genes have been reported to contain putative disease-causing mutations. In most instances, however, the results remain quite limited and rather preliminary, in large part because of an inability of any single group to validate findings in a large independent series of sequenced patients. We present here the Parkinson’s Disease Sequencing Browser: a Shiny-based web application that presents comprehensive summary-level frequency data from multiple large-scale genotyping and sequencing projects. The data is aggregated and involves a total of 102,127 participants, including 30,103 PD cases (including 1,650 proxy cases) and 72,024 controls. Our aim is to assist researchers on their search for PD-risk genes and variant candidates with an easily accessible and open summary-level genomic data browser for the PD research community, https://pdgenetics.shinyapps.io/VariantBrowser/.

The genetic basis of Lewy body dementia (LBD) is not well understood. Here, we performed whole-genome sequencing in large cohorts of LBD cases and neurologically healthy controls to study the genetic architecture of this understudied form of dementia and to generate a resource for the scientific community. Genome-wide association analysis identified five independent risk loci, whereas genome-wide gene-aggregation tests implicated mutations in the gene GBA. Genetic risk scores demonstrate that LBD shares risk profiles and pathways with Alzheimer’s and Parkinson’s disease, providing a deeper molecular understanding of the complex genetic architecture of this age-related neurodegenerative condition.

Biallelic PARK2 (Parkin) mutations cause autosomal recessive Parkinson’s (PD); however, the role of monoallelic PARK2 mutations as a risk factor for PD remains unclear. We investigated the role of single heterozygous PARK2 mutations in three large independent case-control cohorts totalling 10,858 PD cases and 8,328 controls. Overall, after exclusion of biallelic carriers, single PARK2 mutations were more common in PD than controls conferring a >1.5-fold increase in risk of PD (P=0.035), with meta-analysis (19,574 PD cases and 468,488 controls) confirming increased risk (OR=1.65, P=3.69E-07). Carriers were shown to have significantly younger ages at onset compared to non-carriers (NeuroX: 56.4 vs. 61.4 years; Exome: 38.5 vs. 43.1 years). Stratifying by mutation type, we provide preliminary evidence for a more pathogenic risk profile for single PARK2 copy number variant (CNV) carriers compared to single nucleotide variant carriers. Studies that did not assess biallelic PARK2 mutations or consist of predominantly early-onset cases may be biasing these estimates, and removal of these resulted in a loss of association (OR=1.23, P=0.614; n=4). Importantly, when we looked for additional CNVs in 30% of PD cases with apparent monoallellic PARK2 mutations we found that 44% had biallelic mutations suggesting that previous estimates may be influenced by cryptic biallelic mutation status. While this study supports the association of single PARK2 mutations with PD, it highlights confounding effects therefore caution is needed when interpreting current risk estimates. Together, we demonstrate that comprehensive assessment of biallelic mutation status is essential when elucidating PD risk associated with monoallelic PARK2 mutations.

OBJECTIVETo discover genetic determinants of Parkinson disease (PD) motor subtypes, including Tremor Dominant (TD) and Postural Instability/Gait Difficulty (PIGD) forms.METHODSIn 3,212 PD cases of European ancestry, we performed a genome-wide association study (GWAS) examining two complementary outcome traits derived from the Unified Parkinson’s Disease Rating Scale (UPDRS), including dichotomous motor subtype (TD vs. PIGD) or a continuous tremor / PIGD score ratio. Logistic or linear regression models were adjusted for sex, age of onset, disease duration, and 5 ancestry principal components, followed by meta-analysis.RESULTSAmong 71 established PD risk variants, we detected multiple suggestive associations with PD motor subtype, including GPNMB (rs199347, psubtype = 0.01, pratio = 0.03), SH3GL2 (rs10756907, psubtype = 0.02, pratio = 0.01), HIP1R (rs10847864, psubtype = 0.02), RIT2 (rs12456492, psubtype = 0.02), and FBRSL1 (rs11610045, psubtype = 0.02). A PD genetic risk score integrating all 71 PD risk variants was also associated with subtype ratio (p = 0.026, ß = −0.04, 95% CI = −0.07, 0). Based on top results of our GWAS, we identify a novel suggestive association at the STK32B locus (rs2301857, pratio = 6.6×10−7), which harbors an independent risk allele for essential tremor.CONCLUSIONSMultiple PD risk alleles may also modify clinical manifestations to influence PD motor subtype. The discovery of a novel variant at STK32B suggests a possible overlap between genetic risk for essential tremor and tremor-dominant PD.

Parkinson’s disease (PD) is a complex neurodegenerative disease with a strong genetic component in which both rare and common genetic variants contribute to disease risk, onset and progression. Despite that several genes have been associated with familial forms of disease, validation of novel genes associated with PD remains extremely challenging. Recently, a heterozygous loss-of-function variant in LIN28A was associated with PD pathogenesis in the Asian population. Here, we comprehensively assess the role of LIN28A variants in PD susceptibility using individual-level genotyping data from 14,671 PD cases and 17,667 controls, as well as whole-genome sequencing data from 1,647 PD patients and 1,050 controls. Additionally, we further assessed the summary statistics from the most recent GWAS meta-analyses to date for PD risk and age at onset. After evaluating these data, we did not find evidence to support a role for LIN28A as a major causal gene for PD. However, additional large-scale familial and case-control studies in non-European ancestry populations are necessary to further evaluate the role of LIN28A in PD etiology.

Background There are currently no treatments that stop or slow the progression of Parkinson's disease (PD). Case-control genome-wide association studies have identified variants associated with disease risk, but not progression. The objective of the current study was to identify genetic variants associated with PD progression. Methods We analyzed 3 large longitudinal cohorts: Tracking Parkinson's, Oxford Discovery, and the Parkinson's Progression Markers Initiative. We included clinical data for 3364 patients with 12,144 observations (mean follow-up 4.2 years). We used a new method in PD, following a similar approach in Huntington's disease, in which we combined multiple assessments using a principal components analysis to derive scores for composite, motor, and cognitive progression. These scores were analyzed in linear regression in genome-wide association studies. We also performed a targeted analysis of the 90 PD risk loci from the latest case-control meta-analysis. Results There was no overlap between variants associated with PD risk, from case-control studies, and PD age at onset versus PD progression. The APOE e4 tagging variant, rs429358, was significantly associated with composite and cognitive progression in PD. Conditional analysis revealed several independent signals in the APOE locus for cognitive progression. No single variants were associated with motor progression. However, in gene-based analysis, ATP8B2, a phospholipid transporter related to vesicle formation, was nominally associated with motor progression (P = 5.3 × 10-6 ). Conclusions We provide early evidence that this new method in PD improves measurement of symptom progression. We show that the APOE e4 allele drives progressive cognitive impairment in PD. Replication of this method and results in independent cohorts are needed. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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 pathways underlying PD using the largest currently available cohorts of genetic data 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 placed these insights into a cellular context. We applied large-scale pathway-specific polygenic risk score (PRS) analyses to assess the role of common variation on PD risk in a cohort of 457,110 individuals by focusing on a compilation of 2,199 publicly annotated gene sets representative of curated pathways, of which we nominate 46 pathways associated with PD risk. We assessed the impact of rare variation on PD risk in an independent cohort of whole-genome sequencing data, including 4,331 individuals. We explored enrichment linked to expression cell specificity patterns using single-cell gene expression data and demonstrated a significant risk pattern for adult dopaminergic neurons, serotonergic neurons, and radial glia. 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 1,612 PD patients, which revealed 54 connecting networks associated with PD. Our analyses highlight several promising pathways and genes for functional prioritization and provide a cellular context in which such work should be done.

ObjectiveTobacco smoking, alcohol intake, and high BMI have been identified in observational studies as potentially protective factors against developing Parkinson’s disease (PD). Because of the possibility of residual confounding and reverse causation, it is unclear whether such epidemiological associations are causal. Mendelian randomisation (MR) uses genetic variants to explore causal effects of exposures on outcomes; minimising these sources of bias. Using MR, this study sought to determine the causal relationship between tobacco smoking, alcohol intake, and high BMI, and the risk of PD.MethodsWe performed genome-wide association studies to identify single nucleotide polymorphisms associated with the exposures. MR analysis of the relationship between each exposure and PD was undertaken using a split-sample design. The inverse variance weighted (IVW) method was used to combine SNP-specific effect estimates.ResultsEver-smoking causally reduced risk of PD (OR 0.955; 95% confidence interval [CI] 0.921-0.991; p=0.013). An increase in daily alcohol intake causally increased risk of PD (OR 1.125, 95% CI 1.025-1.235; p=0.013) and a 1 kg/m2BMI causally reduced risk of PD (OR 0.988, 95% CI 0.979-0.997; p=0.008). Sensitivity analyses did not suggest bias from horizontal pleiotropy or invalid instruments.InterpretationUsing split-sample MR in over 2.4 million participants, we observed a protective effect of smoking on risk of PD, warranting the prioritisation of related therapeutic targets, such as nicotinic agonists, in prevention trials. In contrast to observational data, alcohol consumption causally increased risk of PD. Higher BMI had a protective effect on PD, but the effect was small.

ObjectiveTo systematically investigate the association of environmental risk factors and prodromal features with incident Parkinson’s disease (PD) diagnosis and the interaction of genetic risk with these factors. To evaluate existing risk prediction algorithms and the impact of including addition genetic risk on the performance of prediction.MethodsWe identified individuals with incident PD diagnoses (n=1276) and unmatched controls (n=500,406) in UK Biobank. We determined the association of risk factors with incident PD using adjusted logistic regression models. A polygenic risk score (PRS) was constructed and used to examine gene-environment interactions. The PRS was also incorporated into a previously-developed prediction algorithm for finding incident cases.ResultsStrong evidence of association (Pcorr2 0.0053, p=6.87×10−14). We found evidence of interaction between the PRS and diabetes.InterpretationHere we used UK Biobank data to reproduce several well-known associations with PD, to demonstrate the validity and predictive power of a polygenic risk score, and to demonstrate a novel gene-environment interaction, whereby the effect of diabetes on PD risk appears to depend on prior genetic risk for PD.

Multiple genes have been associated with monogenic Parkinson’s disease and Parkinsonism syndromes. Mutations in PINK1 (PARK6) have been shown to result in autosomal recessive early onset Parkinson’s disease. In the past decade, several studies have suggested that carrying a single heterozygous PINK1 mutation is associated with increased risk for Parkinson’s disease. Here we comprehensively assess the role of PINK1 variants in Parkinson’s disease susceptibility using several large datasets totalling 376,558 individuals including: 13,708 Parkinson’s disease cases and 362,850 controls. After combining these data, we did not find evidence to support a role for heterozygous PINK1 mutations as a risk factor for Parkinson’s disease.

Parkinson’s disease (PD) is a common incurable neurodegenerative disease. The identification of genetic variants via genome-wide association studies (GWAS) has considerably advanced our understanding of the PD genetic risk. Understanding the functional significance of the risk loci is now a critical step towards translating these genetic advances into an enhanced biological understanding of the disease. Impaired mitophagy is a key causative pathway in familial PD, but its relevance to idiopathic PD is unclear. We used a mitophagy screening assay to evaluate the functional significance of risk genes identified through GWAS. We identified two new regulators of PINK1-mitophagy, KAT8 and KANSL1, previously shown to modulate lysine acetylation. We show that KAT8 and KANSL1 modulate PINK1 gene expression and subsequent PINK1-mitophagy. These findings suggest PINK1-mitophagy is a contributing factor to idiopathic PD. KANSL1 is located on chromosome 17q21 where the risk associated gene has long been considered to be MAPT. While our data does not exclude a possible association between the MAPT gene and PD, it provides strong evidence that KANSL1 plays a crucial role in the disease. Finally, these results enrich our understanding of physiological events regulating mitophagy and establish a novel pathway for drug targeting in neurodegeneration.

SummaryNeuropathological and experimental evidence suggests that the cell-to-cell transfer of a-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 an siRNA, genome-wide high throughput screen to identify genes regulating the cell-to-cell transfer of a-synuclein. We transiently transfected HEK cells stably overexpressing a-synuclein with a construct encoding GFP-2a-aSynuclein-RFP. The cells expressing a-synuclein-RFP through transfection were double positive for GFP and RFP fluorescence, whereas the cells receiving it through transfer were positive only for RFP fluorescence. The amount of a-synuclein transfer was quantified by high content microscopy. A series of unbiased screens confirmed the involvement of 38 genes in the regulation of a-synuclein-RFP transfer. One of those hits was ITGA8, a candidate gene recently identified through a large PD genome wide association study (GWAS). Weighted gene co-expression network analysis (WGCNA) and weighted protein-protein network interaction analysis (WPPNIA) showed that the hits clustered in networks that included known PD Mendelian and GWAS risk genes more frequently than expected than random chance. Given the genetic overlap between a-synuclein transfer and PD, those findings provide supporting evidence for the importance of the cell-to-cell transfer of a-synuclein in the pathogenesis of PD, and expand our understanding of the mechanism of a-synuclein spread.

ObjectiveREM-sleep behavior disorder (RBD) is a prodromal synucleinopathy, as >80% will eventually convert to overt synucleinopathy. We performed an in-depth analysis of the SNCA locus to identify RBD-specific risk variants.MethodsFull sequencing and genotyping of SNCA was performed in isolated/idiopathic RBD (iRBD, n=1,076), Parkinson’s disease (PD, n=1,013), and dementia with Lewy bodies (DLB, n=415), and in control subjects (n=6,155). A replication cohort from 23andMe of PD patients with probable RBD (pRBD) was also analyzed (cases n=1,782, controls n=131,250). Adjusted logistic regression models and meta-analyses were performed. Effects on conversion rate were analyzed in 432 RBD patients with available data using Kaplan-Meier survival analysis.ResultsA 5’-region SNCA variant (rs10005233) was associated with iRBD (OR=1.43, p=1.1E-08), which was replicated in pRBD. This variant is in linkage disequilibrium (LD) with other 5’ risk variants across the different synucleinopathies. An independent iRBD-specific suggestive association (rs11732740) was detected at the 3’ of SNCA (OR=1.32, p=4.7E-04, not statistically significant after Bonferroni correction). Homozygous carriers of both iRBD-specific SNPs were at highly increased risk for iRBD (OR=5.74, p=2E-06). The known top PD-associated variant (3’ variant rs356182) had an opposite direction of effect in iRBD compared to PD.InterpretationThere is a distinct pattern of association at the SNCA locus in RBD as compared to PD, with an opposite direction of effect at the 3’ of SNCA. Several 5’ SNCA variants are associated with iRBD and with pRBD in overt synucleinopathies, and may suggest a cognitive component to this region.

We aimed to identify shared genetic background between multiple system atrophy (MSA) and autoimmune diseases by using the conjFDR approach. Our study showed significant genetic overlap between MSA and inflammatory bowel disease and identified DENND1B, C7, and RSP04 loci, which are linked to significant changes in methylation or expression levels of adjacent genes. We obtained evidence of enriched heritability involving immune/digestive categories. Finally, an MSA mouse model showed dysregulation of the C7 gene in the degenerating midbrain compared to wildtype mice. The results identify novel molecular mechanisms and implicate immune and gut dysfunction in MSA pathophysiology.

ObjectiveTo assess genetic modifiers of Parkinson’s disease (PD) age at onset (AAO) penetrance in individuals carrying common and rare LRRK2 risk allelesMethodsWe analysed reported genetic modifier DNM3 rs2421947 in 724 LRRK2 p.G2019S heterozygotes using linear regression of AAO. We meta-analysed our data with previously published data (n=754). VAMP4 is in close proximity to DNM3 and is associated with PD. We analysed the effect of the rs11578699 VAMP4 variant on pG2019S penetrance in 786 LRRK2 p.G2019S heterozygotes. We also evaluated the impact of VAMP4 variants using AAO regression in 4882 patients with PD carrying a common LRRK2 risk variant (rs10878226).ResultsThere was no evidence for linkage disequilibrium between DNM3 rs2421947 and VAMP4 rs11578699. Our linear regression AAO of 724 p.G2019S carriers showed no relationship between DNM3 rs2421947 and AAO (beta = −1.19, p = 0.55, n =708). Meta-analysis with previously published data did not indicate a significant effect on AAO (beta = −2.21, p = 0.083, n = 1304), but there was significant heterogeneity in the analyses of new and previously published data. VAMP4 rs11578699 was nominally associated with AAO in patients dichotomized by the common LRRK2 risk variant rs10878226 (beta=1.68, se=0.81 p=0.037).InterpretationAnalysis of DNM3 in previously unpublished data does not show an interaction between DNM3 and LRRK2 G2019S for AAO, however the inter-study heterogeneity may indicate ethnic-specific effects of DNM3 rs2421947. Analysis of sporadic PD patients stratified by the PD risk variant rs10878226 indicates a possible interaction between LRRK2 and VAMP4.

BackgroundSeveral reports have identified different patterns of Parkinson’s disease progression in individuals carrying missense variants in theGBAorLRRK2genes. The overall contribution of genetic factors to the severity and progression of Parkinson’s disease, however, has not been well studied.ObjectivesTo test the association between genetic variants and the clinical features and progression of Parkinson’s disease on a genome-wide scale.MethodsWe accumulated individual data from 12 longitudinal cohorts in a total of 4,093 patients with 25,254 observations over a median of 3.81 years. Genome-wide associations were evaluated for 25 cross-sectional and longitudinal phenotypes. Specific variants of interest, including 90 recently-identified disease risk variants, were also investigated for the associations with these phenotypes.ResultsTwo variants were genome-wide significant. Rs382940(T>A), within the intron ofSLC44A1, was associated with reaching Hoehn and Yahr stage 3 or higher faster (HR 2.04 [1.58, 2.62], P-value = 3.46E-8). Rs61863020(G>A), an intergenic variant and eQTL forADRA2A, was associated with a lower prevalence of insomnia at baseline (OR 0.63 [0,52, 0.75], P-value = 4.74E-8). In the targeted analysis, we found nine associations between known Parkinson’s risk variants and more severe motor/cognitive symptoms. Also, we replicated previous reports ofGBAcoding variants (rs2230288: p.E365K, rs75548401: p.T408M) being associated with greater motor and cognitive decline over time, andAPOEE4 tagging variant (rs429358) being associated with greater cognitive deficits in patients.ConclusionsWe identified novel genetic factors associated with heterogeneity of progression in Parkinson’s disease. The results provide new insights into the pathogenesis of Parkinson’s disease as well as patient stratification for clinical trials.

Despite considerable efforts to identify disease-causing and risk factors contributing to essential tremor (ET), no comprehensive assessment of heritable risk has been performed to date. We use GREML-LDMS to estimate narrow-sense heritability due to additive effects (h2) and GREMLd to calculate non-additive heritability due to dominance variance (δ2) using data from 1,748 ET cases and 5,302 controls. We evaluate heritability per 10Mb segments across the genome and assess the impact of Parkinson’s disease (PD) misdiagnosis on heritability estimates. We apply genetic risk score (GRS) from PD and restless legs syndrome (RLS) to explore its contribution to ET risk and further assess genetic correlations with 832 traits by Linkage disequilibrium score regression. Our results show for the first time that ET is a highly heritable condition (h2=0.755, s.e=0.075) in which additive common variability plays a prominent role. In contrast, dominance variance shows insignificant effect on the overall estimates. Heritability split by 10Mb regions revealed increased estimates at chromosomes 6 and 21 suggesting that these may contain causative risk variants influencing susceptibility to ET. The proportion of genetic variance due to PD misdiagnosed cases was estimated to be 5.33%. PD and RLS GRS were not significantly predictive of ET case-control status demonstrating that despite overlapping symptomatology, ET does not seem to share genetic etiologies with PD or RLS. Our study suggests that most of ET genetic component is yet to be discovered and future GWAS will reveal additional risk factors that will improve our understanding of this disabling disorder.

Mitochondrial dysfunction has been implicated in the aetiology of monogenic Parkinson’s disease (PD). Yet the role that mitochondrial processes play in the most common form of the disease; sporadic PD, is yet to be fully established. Here we comprehensively assessed the role of mitochondrial function associated genes in sporadic PD by leveraging improvements in the scale and analysis of PD GWAS data with recent advances in our understanding of the genetics of mitochondrial disease. First, we identified that a proportion of the “missing heritability” of the PD can be explained by common variation within genes implicated in mitochondrial disease (primary gene list) and mitochondrial function (secondary gene list). Next we calculated a mitochondrial-specific polygenic risk score (PRS) and showed that cumulative small effect variants within both our primary and secondary gene lists are significantly associated with increased PD risk. Most significantly we further report that the PRS of the secondary mitochondrial gene list was significantly associated with later age at onset. Finally, to identify possible functional genomic associations we implemented Mendelian randomisation, which showed that 14 of these mitochondrial function associated genes showed functional consequence associated with PD risk. Further analysis suggested that the 14 identified genes are not only involved in mitophagy but implicate new mitochondrial processes. Our data suggests that therapeutics targeting mitochondrial bioenergetics and proteostasis pathways distinct from mitophagy could be beneficial to treating the early stage of PD.

Increasing evidence supports an extensive and complex genetic contribution to Parkinson’s disease (PD). Previous genome-wide association studies (GWAS) have shed light on the genetic basis of risk for this disease. However, the genetic determinants of PD age of onset are largely unknown. Here we performed an age of onset GWAS based on 28,568 PD cases. We estimated that the heritability of PD age of onset due to common genetic variation was ~0.11, lower than the overall heritability of risk for PD (~0.27) likely in part because of the subjective nature of this measure. We found two genome-wide significant association signals, one at SNCA and the other a protein-coding variant in TMEM175, both of which are known PD risk loci and a Bonferroni corrected significant effect at other known PD risk loci, INPP5F/BAG3, FAM47E/SCARB2, and MCCC1. In addition, we identified that GBA coding variant carriers had an earlier age of onset compared to non-carriers. Notably, SNCA, TMEM175, SCARB2, BAG3 and GBA have all been shown to either directly influence alpha-synuclein aggregation or are implicated in alpha-synuclein aggregation pathways. Remarkably, other well-established PD risk loci such as GCH1, MAPT and RAB7L1/NUCKS1 (PARK16) did not show a significant effect on age of onset of PD. While for some loci, this may be a measure of power, this is clearly not the case for the MAPT locus; thus genetic variability at this locus influences whether but not when an individual develops disease. We believe this is an important mechanistic and therapeutic distinction. Furthermore, these data support a model in which alpha-synuclein and lysosomal mechanisms impact not only PD risk but also age of disease onset and highlights that therapies that target alpha-synuclein aggregation are more likely to be disease-modifying than therapies targeting other pathways.