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Parkinson's disease is a common incurable neurodegenerative disease. The identification of genetic variants via genome-wide association studies has considerably advanced our understanding of the Parkinson's disease 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 Parkinson's disease, but its relevance to idiopathic Parkinson's disease is unclear. We used a mitophagy screening assay to evaluate the functional significance of risk genes identified through genome-wide association studies. We identified two new regulators of PINK1-dependent mitophagy initiation, KAT8 and KANSL1, previously shown to modulate lysine acetylation. These findings suggest PINK1-mitophagy is a contributing factor to idiopathic Parkinson's disease. KANSL1 is located on chromosome 17q21 where the risk associated gene has long been considered to be MAPT. While our data do not exclude a possible association between the MAPT gene and Parkinson's disease, they provide 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. ispartof: BRAIN vol:145 issue:12 pages:4349-4367 ispartof: location:England status: published

Neurodegeneration with brain iron accumulation (NBIA) represents a group of neurodegenerative disorders characterized by abnormal iron accumulation in the brain. In Parkinson’s Disease (PD), iron accumulation is a cardinal feature of degenerating regions in the brain and seems to be a key player in mechanisms that precipitate cell death. The aim of this study was to explore the genetic and genomic connection between NBIA and PD. We screened for known and rare pathogenic mutations in autosomal dominant and recessive genes linked to NBIA in a total of 4481 PD cases and 10,253 controls from the Accelerating Medicines Partnership Parkinsons’ Disease Program and the UKBiobank. We examined whether a genetic burden of NBIA variants contributes to PD risk through single-gene, gene-set, and single-variant association analyses. In addition, we assessed publicly available expression quantitative trait loci (eQTL) data through Summary-based Mendelian Randomization and conducted transcriptomic analyses in blood of 1886 PD cases and 1285 controls. Out of 29 previously reported NBIA screened coding variants, four were associated with PD risk at a nominal p value < 0.05. No enrichment of heterozygous variants in NBIA-related genes risk was identified in PD cases versus controls. Burden analyses did not reveal a cumulative effect of rare NBIA genetic variation on PD risk. Transcriptomic analyses suggested that DCAF17 is differentially expressed in blood from PD cases and controls. Due to low mutation occurrence in the datasets and lack of replication, our analyses suggest that NBIA and PD may be separate molecular entities., National Institutes of Health (NIH)

Background Deep brain stimulation (DBS) is a well-established treatment option for select patients with Parkinson’s Disease (PD). However, response to DBS varies, therefore, the ability to predict who will have better outcomes can aid patient selection. Some PD-related monogenic mutations have been reported among factors that influence response to DBS. However, monogenic disease accounts for only a minority of patients with PD. The polygenic risk score (PRS) is an indication of cumulative genetic risk for disease. The PRS in PD has also been correlated with age of onset and symptom progression, but it is unknown whether correlations exist between PRS and DBS response. Here, we performed a pilot study to look for any such correlation. Methods We performed a retrospective analysis of 33 PD patients from the NIH PD Clinic and 13 patients from the Parkinson’s Progression Markers Initiative database who had genetic testing and underwent bilateral subthalamic nucleus DBS surgery and clinical follow-up. A PD-specific PRS was calculated for all 46 patients based on the 90 susceptibility variants identified in the latest PD genome-wide association study. We tested associations between PRS and pre- and post-surgery motor and cognitive measures using multiple regression analysis for up to two years after surgery. Results Changes in scores on the Beck Depression Inventory (BDI) were not correlated with PRS when derived from all susceptibility variants, however, when removing pathogenic and high-risk carriers from the calculation, higher PRS was significantly associated with greater reduction in BDI score at 3 months and with similar trend 24 months after DBS. PRS was not a significant predictor of Unified Parkinson’s Disease Rating Scale, Dementia Rating Scale, or phenomic and semantic fluency outcomes at 3- and 24-months after DBS surgery. Conclusions This exploratory study suggests that PRS may predict degree of improvement in depressive symptoms after DBS, though was not predictive of motor and other cognitive outcomes after DBS. Additionally, PRS may be most relevant in predicting DBS outcomes in patients lacking pathogenic or high-risk PD variants. However, this was a small preliminary study and response to DBS treatment is multifactorial, therefore, more standardized high-powered studies are needed.

PRKN mutations are the most common recessive cause of Parkinson’s disease and are a promising target for gene and cell replacement therapies. Identification of biallelic PRKN patients at the population scale, however, remains a challenge, as roughly half are copy number variants and many single nucleotide polymorphisms are of unclear significance. Additionally, the true prevalence and disease risk associated with heterozygous PRKN mutations is unclear, as a comprehensive assessment of PRKN mutations has not been performed at a population scale. To address these challenges, we evaluated PRKN mutations in two cohorts with near complete genotyping of both single nucleotide polymorphisms and copy number variants: the NIH-PD + AMP-PD cohort, the largest Parkinson’s disease case-control cohort with whole genome sequencing data from 4094 participants, and the UK Biobank, the largest cohort study with whole exome sequencing and genotyping array data from 200 606 participants. Using the NIH-PD participants, who were genotyped using whole genome sequencing, genotyping array, and multi-plex ligation-dependent probe amplification, we validated genotyping array for the detection of copy number variants. Additionally, in the NIH-PD cohort, functional assays of patient fibroblasts resolved variants of unclear significance in biallelic carriers and suggested that cryptic loss of function variants in monoallelic carriers are not a substantial confounder for association studies. In the UK Biobank, we identified 2692 PRKN copy number variants from genotyping array data from nearly half a million participants (the largest collection to date). Deletions or duplications involving exon 2 accounted for roughly half of all copy number variants and the vast majority (88%) involved exons 2, 3, or 4. In the UK Biobank, we found a pathogenic PRKN mutation in 1.8% of participants and two mutations in ∼1/7800 participants. Those with one PRKN pathogenic variant were as likely as non-carriers to have Parkinson’s disease [odds ratio = 0.91 (0.58–1.38), P-value 0.76] or a parent with Parkinson’s disease [odds ratio = 1.12 (0.94–1.31), P-value = 0.19]. Similarly, those in the NIH-PD + AMP + PD cohort with one PRKN pathogenic variant were as likely as non-carriers to have Parkinson’s disease [odds ratio = 1.29 (0.74–2.38), P-value = 0.43]. Together our results demonstrate that heterozygous pathogenic PRKN mutations are common in the population but do not increase the risk of Parkinson’s disease.

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.

Background Parkinson's disease (PD) is a highly age-related disorder, where common genetic risk variants affect both disease risk and age at onset. A statistical approach that integrates these effects across all common variants may be clinically useful for individual risk stratification. A polygenic hazard score methodology, leveraging a time-to-event framework, has recently been successfully applied in other age-related disorders. Objectives We aimed to develop and validate a polygenic hazard score model in sporadic PD. Methods Using a Cox regression framework, we modeled the polygenic hazard score in a training data set of 11,693 PD patients and 9841 controls. The score was then validated in an independent test data set of 5112 PD patients and 5372 controls and a small single-study sample of 360 patients and 160 controls. Results A polygenic hazard score predicts the onset of PD with a hazard ratio of 3.78 (95% confidence interval 3.49-4.10) when comparing the highest to the lowest risk decile. Combined with epidemiological data on incidence rate, we apply the score to estimate genetically stratified instantaneous PD risk across age groups. Conclusions We demonstrate the feasibility of a polygenic hazard approach in PD, integrating the genetic effects on disease risk and age at onset in a single model. In combination with other predictive biomarkers, the approach may hold promise for risk stratification in future clinical trials of disease-modifying therapies, which aim at postponing the onset of PD. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The relationship between APOE polymorphisms and Parkinson’s disease (PD) in black Africans has not been previously investigated. We evaluated the association between APOE polymorphic variability and self-declared cognition in 1100 Nigerians with PD and 1097 age-matched healthy controls. Cognition in PD was assessed using the single item cognition question (item 1.1) of the MDS-UPDRS. APOE genotype and allele frequencies did not differ between PD and controls (p > 0.05). No allelic or genotypic association was observed between APOE and age at onset of PD. In PD, APOE ε4/ε4 conferred a two-fold risk of cognitive impairment compared to one or no ε4 (HR: 2.09 (95% CI: 1.13–3.89; p = 0.02)), while APOE ε2 was associated with modest protection against cognitive impairment (HR: 0.41 (95% CI 0.19–0.99, p = 0.02)). Of 773 PD with motor phenotype and APOE characterized, tremor-dominant (TD) phenotype predominated significantly in ε2 carriers (87/135, 64.4%) compared to 22.2% in persons with postural instability/gait difficulty (PIGD) (30/135) and 13.3% in indeterminate (ID) (18/135, 13.3%) (p = 0.037). Although the frequency of the TD phenotype was highest in homozygous ε2 carriers (85.7%), the distribution of motor phenotypes across the six genotypes did not differ significantly (p = 0.18). Altogether, our findings support previous studies in other ethnicities, implying a role for APOE ε4 and ε2 as risk and protective factors, respectively, for cognitive impairment in 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.

Parkinson's disease (PD) is a complex neurodegenerative condition in which genetic and environmental factors interact to contribute to its etiology. Remarkable progress has been made in deciphering disease etiology through genetic approaches, but there is limited data about how environmental and genetic factors interact to modify penetrance, risk, and disease severity. Here, we provide insights into environmental modifiers of PD, discussing precedents from other neurological and non-neurological conditions. Based on these examples, we outline genetic and environmental factors contributing to PD and review potential environmental modifiers of penetrance and clinical variability in monogenic and idiopathic PD. We also highlight the potential challenges and propose how future studies might tackle these important questions. ANN NEUROL 2022;92:715-724., This work was supported in part by the Global Parkinson's Genetics Program (GP2). GP2 is funded by the Aligning Science Against Parkinson's (ASAP) initiative and implemented by The Michael J. Fox Foundation for Parkinson's Research (https://gp2.org). For a complete list of GP2 members see https://gp2.org. This research was supported in part by the Intramural Research Program of the National Institutes of Health (National Institute on Aging, Z01-AG000949-02). The Preventive Neurology Unit is funded by the Barts Charity.

Background Whole‐genome sequencing data are 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 sufficient power for discoveries, harmonization of multiple cohorts is critical. Objectives The Accelerating Medicines Partnership Parkinson's Disease program has developed a research platform for Parkinson's disease (PD) that integrates the storage and analysis of whole‐genome sequencing data, RNA expression data, and clinical data, harmonized across multiple cohort studies. Methods The version 1 release contains whole‐genome sequencing data derived from 3941 participants from 4 cohorts. Samples underwent joint genotyping by the TOPMed Freeze 9 Variant Calling Pipeline. We performed descriptive analyses of these whole‐genome sequencing data using the Accelerating Medicines Partnership Parkinson's Disease platform. Results The clinical diagnosis of participants in version 1 release includes 2005 idiopathic PD patients, 963 healthy controls, 64 prodromal subjects, 62 clinically diagnosed PD subjects without evidence of dopamine deficit, and 705 participants of genetically enriched cohorts carrying PD risk‐associated GBA variants or LRRK2 variants, of whom 304 were affected. We did not observe significant enrichment of pathogenic variants in the idiopathic PD group, but the polygenic risk score was higher in PD both in nongenetically enriched cohorts and genetically enriched cohorts. The population analysis showed a correlation between genetically enriched cohorts and Ashkenazi Jewish ancestry. Conclusions We describe the genetic component of the Accelerating Medicines Partnership Parkinson's Disease platform, a solution to democratize data access and analysis for the PD research community. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article is a U.S. Government work and is in the public domain in the USA.

Objectives Patients with established Parkinson's disease (PD) display differences in peripheral blood markers of immune function, including leukocyte differential counts, compared with controls. These differences may be useful biomarkers to predict PD and may 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, baseline differential leukocyte count and other blood markers of acute inflammation in UK Biobank (UKB), 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 odds ratio [OR] = 1.18, 95% confidence interval [CI] = 1.07-1.32, padjusted = 0.01). There was some evidence that reductions in eosinophil counts, monocyte counts and C-reactive protein (CRP) were associated with increased PD risk, and that higher neutrophil count was also associated. 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. ANN NEUROL 2021;89:803-812.

Background Parkinson's disease (PD) is a genetically complex neurodegenerative disease with ~20 genes known to contain mutations that cause PD or atypical parkinsonism. 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. Our aim was 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. Methods Sequencing and imputed genotype data were obtained from multiple sources and harmonized and aggregated. Results In total we included a total of 102,127 participants, including 28,453 PD cases, 1650 proxy cases, and 72,024 controls. Conclusions 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 https://pdgenetics.shinyapps.io/VariantBrowser/. Published © 2021 This article is a U.S. Government work and is in the public domain in the USA. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The association between glucocerebrosidase, encoded by GBA, and Parkinson’s disease (PD) highlights the role of the lysosome in PD pathogenesis. Genome-wide association studies in PD have revealed multiple associated loci, including the GALC locus on chromosome 14. GALC encodes the lysosomal enzyme galactosylceramidase, which plays a pivotal role in the glycosphingolipid metabolism pathway. It is still unclear whether GALC is the gene driving the association in the chromosome 14 locus and, if so, by which mechanism. We first aimed to examine whether variants in the GALC locus and across the genome are associated with galactosylceramidase activity. We performed a genome-wide association study in two independent cohorts from (i) Columbia University; and (ii) the Parkinson’s Progression Markers Initiative study, followed by a meta-analysis with a total of 976 PD patients and 478 controls with available data on galactosylceramidase activity. We further analysed the effects of common GALC variants on expression and galactosylceramidase activity using genomic colocalization methods. Mendelian randomization was used to study whether galactosylceramidase activity may be causal in PD. To study the role of rare GALC variants, we analysed sequencing data from 5028 PD patients and 5422 controls. Additionally, we studied the functional impact of GALC knockout on alpha-synuclein accumulation and on glucocerebrosidase activity in neuronal cell models and performed in silico structural analysis of common GALC variants associated with altered galactosylceramidase activity. The top hit in PD genome-wide association study in the GALC locus, rs979812, is associated with increased galactosylceramidase activity (b = 1.2; SE = 0.06; P = 5.10 × 10−95). No other variants outside the GALC locus were associated with galactosylceramidase activity. Colocalization analysis demonstrated that rs979812 was also associated with increased galactosylceramidase expression. Mendelian randomization suggested that increased galactosylceramidase activity may be causally associated with PD (b = 0.025, SE = 0.007, P = 0.0008). We did not find an association between rare GALC variants and PD. GALC knockout using CRISPR–Cas9 did not lead to alpha-synuclein accumulation, further supporting that increased rather than reduced galactosylceramidase levels may be associated with PD. The structural analysis demonstrated that the common variant p.I562T may lead to improper maturation of galactosylceramidase affecting its activity. Our results nominate GALC as the gene associated with PD in this locus and suggest that the association of variants in the GALC locus may be driven by their effect of increasing galactosylceramidase expression and activity. Whether altering galactosylceramidase activity could be considered as a therapeutic target should be further studied.

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 whether existing risk prediction algorithms are improved by the inclusion of genetic risk scores.MethodsWe identified individuals with an incident diagnosis of PD (n=1276) and controls (n=500 406) in UK Biobank. We determined the association of risk factors with incident PD using adjusted logistic regression models. We constructed polygenic risk scores (PRSs) using external weights and selected the best PRS from a subset of the cohort (30%). The PRS was used in a separate testing set (70%) to examine gene–environment interactions and compare predictive models for PD.ResultsStrong evidence of association (false discovery rate InterpretationHere, we used UK Biobank data to reproduce several well-known associations with PD, to demonstrate the validity of a PRS and to demonstrate a novel gene–environment interaction, whereby the effect of diabetes on PD risk appears to depend on background genetic risk for PD.

Author(s): Alcalay, Roy N; Wolf, Pavlina; Chiang, Ming Sum Ruby; Helesicova, Karolina; Zhang, Xiaokui Kate; Merchant, Kalpana; Hutten, Samantha J; Scherzer, Clemens; Caspell-Garcia, Chelsea; Blauwendraat, Cornelis; Foroud, Tatiana; Nudelman, Kelly; Gan-Or, Ziv; Simuni, Tanya; Chahine, Lana M; Levy, Oren; Zheng, Dandi; Li, Gen; Sardi, Sergio Pablo; Parkinson’s Progression Markers Initiative | Abstract: ObjectiveReduction in glucocerebrosidase (GCase; encoded by GBA) enzymatic activity has been linked to Parkinson's disease (PD). Here, we correlated GCase activity and PD phenotype in the Parkinson's Progression Markers Initiative (PPMI) cohort.MethodsWe measured GCase activity in dried blood spots from 1559 samples of participants in the inception PPMI cohort, collected in four annual visits (from baseline visit to Year-3). Participants (PD, nn=n392; controls, nn=n175) were fully sequenced for GBA variants by means of genome-wide genotyping arrays, whole-exome sequencing, whole-genome sequencing, Sanger sequencing, and RNA-sequencing.ResultsFifty-two PD participants (13.4%) and 13 (7.4%) controls carried a GBA variant. GBA status was strongly associated with GCase activity. Among noncarriers, GCase activity was similar between PD and controls. Among GBA p.E326K carriers (PD, nn=n20; controls, nn=n5), activity was significantly lower in PD carriers than control carriers (9.53nµmol/L/h vs. 11.68nµmol/L/h, Pn=n0.035). Glucocerebrosidase activity was moderately (rn=n0.45) associated with white blood cell (WBC) count. Next, we divided the noncarriers with PD to tertiles based on WBC count-corrected enzymatic activity. Members of the lower tertile had higher MDS-Unified Parkinson's Disease Rating Scale motor score in the "off" medication examination at year-III exam. Longitudinal analyses demonstrated slight reduction of activity in samples collected earlier on in the study, likely because of longer storage time.InterpretationGCase activity is associated with GBA genotype, WBC count, and among p.E326K variant carriers, with PD status. Reduced activity may also be associated with worse phenotype but longer follow up is required to confirm this observation.

PPP2R5D-related neurodevelopmental disorder is characterized by a range of neurodevelopmental and behavioral manifestations. We report the association of early-onset parkinsonism with the PPP2R5D p.E200K mutation. Clinical characterization and exome sequencing were performed on three patients, with postmortem neuropathologic examination for one patient. All patients had mild developmental delay and developed levodopa-responsive parkinsonism between the ages of 25 and 40 years. The PPP2R5D c.598G>A (p.E200K) mutation was identified in all patients. Neuropathologic examination demonstrated uneven, focally severe neuronal loss and gliosis in the substantia nigra pars compacta, without Lewy bodies. Our findings suggest the PPP2R5D p.E200K mutation to be a possible new cause of early-onset parkinsonism. ANN NEUROL 2020;88:1028-1033.

Polygenic inheritance plays a central role in Parkinson disease (PD). A priority in elucidating PD etiology lies in defining the biological basis of genetic risk. Unraveling how risk leads to disruption will yield disease-modifying therapeutic targets that may be effective. Here, we utilized a high-throughput and hypothesis-free approach to determine biological processes underlying PD using the largest currently available cohorts of genetic and gene expression data from International Parkinson’s Disease Genetics Consortium (IPDGC) and the Accelerating Medicines Partnership-Parkinson’s disease initiative (AMP-PD), among other sources. We applied large-scale gene-set specific polygenic risk score (PRS) analyses to assess the role of common variation on PD risk focusing on publicly annotated gene sets representative of curated pathways. We nominated specific molecular sub-processes underlying protein misfolding and aggregation, post-translational protein modification, immune response, membrane and intracellular trafficking, lipid and vitamin metabolism, synaptic transmission, endosomal–lysosomal dysfunction, chromatin remodeling and apoptosis mediated by caspases among the main contributors to PD etiology. We assessed the impact of rare variation on PD risk in an independent cohort of whole-genome sequencing data and found evidence for a burden of rare damaging alleles in a range of processes, including neuronal transmission-related pathways and immune response. We explored enrichment linked to expression cell specificity patterns using single-cell gene expression data and demonstrated a significant risk pattern for dopaminergic neurons, serotonergic neurons, hypothalamic GABAergic neurons, and neural progenitors. Subsequently, we created a novel way of building de novo pathways by constructing a network expression community map using transcriptomic data derived from the blood of PD patients, which revealed functional enrichment in inflammatory signaling pathways, cell death machinery related processes, and dysregulation of mitochondrial homeostasis. Our analyses highlight several specific promising pathways and genes for functional prioritization and provide a cellular context in which such work should be done. Electronic supplementary material The online version of this article (10.1007/s00401-020-02181-3) contains supplementary material, which is available to authorized users.

Objective: Rapid eye movement 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. Methods: Full sequencing and genotyping of SNCA was performed in isolated/idiopathic RBD (iRBD, n = 1,076), Parkinson disease (PD, n = 1,013), dementia with Lewy bodies (DLB, n = 415), and control subjects (n = 6,155). The iRBD cases were diagnosed with RBD prior to neurodegeneration, although some have since converted. A replication cohort from 23andMe of PD patients with probable RBD (pRBD) was also analyzed (n = 1,782 cases; n = 131,250 controls). 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. Results: A 5′-region SNCA variant (rs10005233) was associated with iRBD (odds ratio [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. Interpretation: There 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. ANN NEUROL 2020;87:584–598.

Parkinson's disease is a complex neurodegenerative disorder for which both rare and common genetic variants contribute to disease risk, onset, and progression. Mutations in more than 20 genes have been associated with the disease, most of which are highly penetrant and often cause early onset or atypical symptoms. Although our understanding of the genetic basis of Parkinson's disease has advanced considerably, much remains to be done. Further disease-related common genetic variability remains to be identified and the work in identifying rare risk alleles has only just begun. To date, genome-wide association studies have identified 90 independent risk-associated variants. However, most of them have been identified in patients of European ancestry and we know relatively little of the genetics of Parkinson's disease in other populations. We have a limited understanding of the biological functions of the risk alleles that have been identified, although Parkinson's disease risk variants appear to be in close proximity to known Parkinson's disease genes and lysosomal-related genes. In the past decade, multiple efforts have been made to investigate the genetic architecture of Parkinson's disease, and emerging technologies, such as machine learning, single-cell RNA sequencing, and high-throughput screens, will improve our understanding of genetic risk.

BACKGROUND: Tobacco smoking and alcohol intake have been identified in observational studies as potentially protective factors against developing Parkinson's disease (PD); the impact of body mass index (BMI) on PD risk is debated. Whether such epidemiological associations are causal remains unclear. Mendelian randomsation (MR) uses genetic variants to explore the effects of exposures on outcomes; potentially reducing bias from residual confounding and reverse causation.OBJECTIVE: Using MR, we examined relationships between PD risk and three unhealthy behaviours: tobacco smoking, alcohol intake, and higher BMI.METHODS: 19,924 PD cases and 2,413,087 controls were included in the analysis. We performed genome-wide association studies to identify single nucleotide polymorphisms associated with tobacco smoking, alcohol intake, and BMI. MR analysis of the relationship between each exposure and PD was undertaken using a split-sample design.RESULTS: Ever-smoking reduced the risk of PD (OR 0.955; 95%confidence interval [CI] 0.921-0.991; p = 0.013). Higher daily alcohol intake increased the risk of PD (OR 1.125, 95%CI 1.025-1.235; p = 0.013) and a 1 kg/m2 higher BMI reduced the 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.CONCLUSION: Using split-sample MR in over 2.4 million participants, we observed a protective effect of smoking on risk of PD. In contrast to observational data, alcohol consumption appeared to increase the risk of PD. Higher BMI had a protective effect on PD, but the effect was small.

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson’s disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug–gene interactions. We performed automated ML on multimodal 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. Our 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 and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available.

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.

Background Increasing evidence supports an extensive and complex genetic contribution to 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 at onset are largely unknown. Objectives To identify the genetic determinants of PD age at onset. Methods Using genetic data of 28,568 PD cases, we performed a genome-wide association study based on PD age at onset. Results We estimated that the heritability of PD age at onset attributed to common genetic variation was similar to 0.11, lower than the overall heritability of risk for PD (similar to 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, GBA, INPP5F/BAG3, FAM47E/SCARB2, and MCCC1. Notably, SNCA, TMEM175, SCARB2, BAG3, and GBA have all been shown to be implicated in alpha-synuclein aggregation pathways. Remarkably, other well-established PD risk loci, such as GCH1 and MAPT, did not show a significant effect on age at onset of PD. Conclusions Overall, we have performed the largest age at onset of PD genome-wide association studies to date, and our results show that not all PD risk loci influence age at onset with significant differences between risk alleles for age at onset. This provides a compelling picture, both within the context of functional characterization of disease-linked genetic variability and in defining differences between risk alleles for age at onset, or frank risk for disease. (c) 2019 International Parkinson and Movement Disorder Society

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.

BACKGROUND: Cytoplasmic inclusions of α-synuclein in brainstem neurons are characteristic of idiopathic Parkinson’ disease. Parkinson’s disease also entails α-synuclein buildup in sympathetic nerves. Among genetic forms of Parkinson’s disease the relative extents of sympathetic intra-neuronal accumulation of α-synuclein have not been reported. OBJECTIVE: This cross-sectional observational study compared magnitudes of intra-neuronal deposition of α-synuclein in common and rare genetic forms of Parkinson’s disease. METHODS: α-Synuclein deposition was quantified by the α-synuclein-tyrosine hydroxylase colocalization index in C2 cervical skin biopsies from 65 subjects. These included 30 subjects with pathogenic mutations in SNCA (N=3), PRKN (biallelic [N=7] and monoallelic [N=3]), LRRK2 (N=7), GBA (N=7), or PARK7/DJ1 (biallelic [N=1] and monoallelic [N=2]). 25 of the mutation carriers had Parkinson’s disease and 5 did not. Data were also analyzed from 19 idiopathic Parkinson’s disease patients and 16 control participants. RESULTS: α-Synuclein deposition varied as a function of genotype (F=16.7, p

Summary Background The genetic basis of variation in the progression of primary tauopathies has not been determined. We aimed to identify genetic determinants of survival in progressive supranuclear palsy (PSP). Methods In stage one of this two stage genome-wide association study (GWAS), we included individuals with PSP, diagnosed according to pathological and clinical criteria, from two separate cohorts: the 2011 PSP GWAS cohort, from brain banks based at the Mayo Clinic (Jacksonville, FL, USA) and in Munich (Germany), and the University College London PSP cohort, from brain banks and the PROSPECT study, a UK-wide longitudinal study of patients with atypical parkinsonian syndromes. Individuals were included if they had clinical data available on sex, age at motor symptom onset, disease duration (from motor symptom onset to death or to the date of censoring, Dec 1, 2019, if individuals were alive), and PSP phenotype (with reference to the 2017 Movement Disorder Society criteria). Genotype data were used to do a survival GWAS using a Cox proportional hazards model. In stage two, data from additional individuals from the Mayo Clinic brain bank, which were obtained after the 2011 PSP GWAS, were used for a pooled analysis. We assessed the expression quantitative trait loci (eQTL) profile of variants that passed genome-wide significance in our GWAS using the Functional Mapping and Annotation of GWAS platform, and did colocalisation analyses using the eQTLGen and PsychENCODE datasets. Findings Data were collected and analysed between Aug 1, 2016, and Feb 1, 2020. Data were available for 1001 individuals of white European ancestry with PSP in stage one. We found a genome-wide significant association with survival at chromosome 12 (lead single nucleotide polymorphism rs2242367, p=7·5 × 10−10, hazard ratio 1·42 [95% CI 1·22–1·67]). rs2242367 was associated with survival in the individuals added in stage two (n=238; p=0·049, 1·22 [1·00–1·48]) and in the pooled analysis of both stages (n=1239; p=1·3 × 10−10, 1·37 [1·25–1·51]). An eQTL database screen revealed that rs2242367 is associated with increased expression of LRRK2 and two long intergenic non-coding RNAs (lncRNAs), LINC02555 and AC079630.4, in whole blood. Although we did not detect a colocalisation signal for LRRK2, analysis of the PSP survival signal and eQTLs for LINC02555 in the eQTLGen blood dataset revealed a posterior probability of hypothesis 4 of 0·77, suggesting colocalisation due to a single shared causal variant. Interpretation Genetic variation at the LRRK2 locus was associated with survival in PSP. The mechanism of this association might be through a lncRNA-regulated effect on LRRK2 expression because LINC02555 has previously been shown to regulate LRRK2 expression. LRRK2 has been associated with sporadic and familial forms of Parkinson's disease, and our finding suggests a genetic overlap with PSP. Further functional studies will be important to assess the potential of LRRK2 modulation as a disease-modifying therapy for PSP and related tauopathies. Funding PSP Association, CBD Solutions, Medical Research Council (UK).

Introduction There have been no specific guidelines regarding which genes should be tested in the clinical setting for Parkinson's disease (PD) or parkinsonism. We evaluated the types of clinical genetic testing offered for PD as the first step of our gene curation. Methods The National Institutes of Health (NIH) Genetic Testing Registry (GTR) was queried on 12/7/2020 to identify current commercial PD genetic test offerings by clinical laboratories, internationally. Results We identified 502 unique clinical genetic tests for PD, from 28 Clinical Laboratory Improvement Amendments (CLIA)-approved clinical laboratories. These included 11 diagnostic PD panels. The panels were notable for their differences in size, ranging from 5 to 62 genes. Five genes for variant query were included in all panels (SNCA, PRKN, PINK-1, PARK7 (DJ1), and LRRK2). Notably, the addition of the VPS35 and GBA genes was variable. Panel size differences stemmed from inclusion of genes linked to atypical parkinsonism and dystonia disorders, and genes in which the link to PD causation is controversial. Conclusion There is an urgent need for expert opinion regarding which genes should be included in a commercial laboratory multi-gene panel for PD. Deborah Mascalzoni is part of Movement Society Disorder (MDS) Task Force on Recommendations for Clinical Genetic Testing in Parkinson's Disease

International Parkinson’s Disease Genomics Consortium (IPDGC)., Genetic variation within the mitochondrial pathway contributes to the risk of Parkinson’s disease (PD). Recent genetic analyses have investigated the association between the RHOT1 and RHOT2 genes and PD etiology. Furthermore, 4 mutations in the RHOT1 gene (p.R272Q, p.R450C, p.T351A, p.T610A) have been reported to be potentially associated with disease risk. As part of the International Parkinson Disease Genomics Consortium efforts to evaluate reported PD risk factors, we assessed the role of common and low frequency variants in both RHOT1 and also RHOT2 according to the high degree of homology in their amino acid sequences. Utilizing large-scale genotyping and whole-genome sequencing data from the International Parkinson Disease Genomics Consortium and the Accelerating Medicines Partnership – Parkinson Disease initiative, our analyses did not identify evidence to support the hypothesis that RHOT1 and RHOT2 are disease causing or modifying genes for PD risk or age at onset., This work was supported in part by the Intramural Research Programs of the National Institute of Neurological Disorders and Stroke (NINDS), the National Institute on Aging (NIA), and the National Institute of Environmental Health Sciences both part of the National Institutes of Health, Department of Health and Human Services; project numbers 1ZIA-NS003154, Z01-AG000949-02, and Z01-ES101986. In addition, this work was supported by the Department of Defense (award W81XWH-09-2-0128), and The Michael J. Fox Foundation for Parkinson’s Research. Data used in the preparation of this article were obtained from the AMP PD Knowledge Platform. For up-to-date information on the study, visit https://www.amp-pd.org. AMP PD—a public-private partnership—is managed by the FNIH and funded by Celgene, GSK, the Michael J. Fox Foundation for Parkinson’s Research, the National Institute of Neurological Disorders and Stroke, Pfizer, and Verily.

We fine mapped the leukocyte antigen (HLA) region in 13,770 Parkinson’s disease (PD) patients, 20,214 proxy-cases, and 490,861 controls of European origin. Four 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 analyses followed by amino acid analysis and conditional analyses 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. Our results suggest that specific HLA-DRB1 variants 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 the identification of new targets for therapeutics development.

ObjectivesPatients with established Parkinson’s disease (PD) display differences in blood biomarkers of immune function, including leukocyte differentials, compared to controls. These may be useful biomarkers to predict PD and inform pathogenesis. We sought to identify whether peripheral immune dysregulation was associated with risk of subsequent PD.MethodsWe examined the relationship between incident PD and baseline leukocyte differential count 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.ResultsAfter excluding individuals with comorbidities which could influence inflammation biomarkers, 465 incident PD cases and 312,125 controls remained. Lower lymphocyte count was associated with increased risk of PD diagnosis (per 1-SD decrease in count OR 1.18, 95% CI 1.07–1.32, padjusted=0.01). 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).ConclusionsWe provide converging evidence from observational analyses in UKB and MR that lower lymphocyte count is associated with an increased risk of subsequent PD.m.jensen@cantab.net

IntroductionObservational studies have flagged smoking, alcohol and high body mass index (BMI) as potential protective factors against Parkinson’s disease (PD), however it is unclear whether such asso- ciations are causal. Mendelian randomisation (MR) uses gene variants to explore causal trait-outcome effects, minimising bias from observational studies. Using MR, we sought to determine the causal relation- ship between unhealthy traits (tobacco smoking, alcohol intake, and high BMI) and PD risk.MethodsWe used genome-wide association studies to find single nucleotide polymorphisms associated with each trait. Using split-sample MR, we used inverse weighted (IVW) methods to determine the effect of traits on PD. We used MR-Egger and weighted mean methods to check for bias in IVW analyses.ResultsEver-smoking and high BMI causally reduced PD risk (OR 0.96 95%CI 0.92–0.99 p=0.01; OR 0.99 95%CI 0.98–1.00 per 1 kg/m2 p=0.01, respectively). Alcohol causally increased PD risk (OR 1.13 95%CI 1.03–1.24 p=0.01). Sensitivity analyses did not reveal bias from pleiotropy or invalid instruments.ConclusionsMR analysis in over 2.4 million participants showed a protective effect of smoking on PD; war- ranting prioritisation of related therapeutic targets in clinical trials. Conversely, alcohol causally increased PD risk. Higher BMI protected against PD, but the effect was marginal.m.jensen@cantab.net

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.

International Parkinson's Disease Genomics Consortium (IPDGC)., [Background] Parkinson's disease (PD) is a neurodegenerative disease with an often complex component identifiable by genome‐wide association studies. The most recent large‐scale PD genome‐wide association studies have identified more than 90 independent risk variants for PD risk and progression across more than 80 genomic regions. One major challenge in current genomics is the identification of the causal gene(s) and variant(s) at each genome‐wide association study locus. The objective of the current study was to create a tool that would display data for relevant PD risk loci and provide guidance with the prioritization of causal genes and potential mechanisms at each locus., [Methods] We included all significant genome‐wide signals from multiple recent PD genome‐wide association studies including themost recent PD risk genome‐wide association study, age‐at‐onset genome‐wide association study, progression genome‐wide association study, and Asian population PD risk genome‐wide association study. We gathered data for all genes 1 Mb up and downstream of each variant to allow users to assess which gene(s) are most associated with the variant of interest based on a set of self‐ranked criteria. Multiple databases were queried for each gene to collect additional causal data., [Results] We created a PD genome‐wide association study browser tool (https://pdgenetics.shinyapps.io/GWASBrowser/) to assist the PD research community with the prioritization of genes for follow‐up functional studies to identify potential therapeutic targets., [Conclusions] Our PD genome‐wide association study browser tool provides users with a useful method of identifying potential causal genes at all known PD risk loci from large‐scale PD genome‐wide association studies. We plan to update this tool with new relevant data as sample sizes increase and new PD risk loci are discovered. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA., This work was supported in part by the Intramural Research Programs of the National Institute of Neurological Disorders and Stroke (NINDS), the National Institute on Aging (NIA), and the National Institute of Environmental Health Sciences, both part of the National Institutes of Health, Department of Health and Human Services (project numbers 1ZIA‐NS003154, Z01‐AG000949‐02, and Z01‐ES101986). We thank the research participants and employees of 23andMe for making this work possible. C.W. is supported by the UK Dementia Research Institute funded by the Medical Research Council (MRC), Alzheimer's Society and Alzheimer's Research UK. C.S. is supported by the Ser Cymru II program, which is partly funded by Cardiff University and the European Regional Development Fund through the Welsh Government. Data were generated as part of the PsychENCODE Consortium supported by: U01MH103339, U01MH103365, U01MH103392, U01MH103340, U01MH103346, R01MH105472, R01MH094714, R01MH105898, R21MH102791, R21MH105881, R21MH103877, and P50MH106934 awarded to Schahram Akbarian (Icahn School of Medicine at Mount Sinai), Gregory Crawford (Duke), Stella Dracheva (Icahn School of Medicine at Mount Sinai), Peggy Farnham (USC), Mark Gerstein (Yale), Daniel Geschwind (UCLA), Thomas M. Hyde (LIBD), Andrew Jaffe (LIBD), James A. Knowles (USC), Chunyu Liu (UIC), Dalila Pinto (Icahn School of Medicine at Mount Sinai), Nenad Sestan (Yale), Pamela Sklar (Icahn School of Medicine at Mount Sinai), Matthew State (UCSF), Patrick Sullivan (UNC), Flora Vaccarino (Yale), Sherman Weissman (Yale), Kevin White (UChicago), and Peter Zandi (JHU). The Genotype‐Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this article were obtained from the GTEx Portal on February 12, 2020. Molecular data for the Trans‐Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI). Genome sequencing for “NHLBI TOPMed: Atherosclerosis Risk in Communities (ARIC)” (phs001211.v2.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1)and at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for the “NHLBI TOPMed: Cleveland Clinic Atrial Fibrillation (CCAF) Study” (phs001189.v1.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for “NHLBI TOPMed: Trans‐Omics for Precision Medicine (TOPMed) Whole Genome Sequencing Project: Cardiovascular Health Study (phs001368.v1.p1) was performed at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for “NHLBI TOPMed: Partners HealthCare Biobank” (phs001024.v3.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for “NHLBI TOPMed: Whole Genome Sequencing of Venous Thromboembolism (WGS of VTE)” (phs001402.v1.p1) was performed at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for “NHLBI TOPMed: Novel Risk Factors for the Development of Atrial Fibrillation in Women” (phs001040.v3.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for “NHLBI TOPMed: The Genetics and Epidemiology of Asthma in Barbados” (phs001143.v2.p1) was performed by Illumina Genomic Services (3R01HL104608‐04S1). Genome sequencing for “NHLBI TOPMed: The Vanderbilt Genetic Basis of Atrial Fibrillation” (phs001032.v4.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for “NHLBI TOPMed: Heart and Vascular Health Study (HVH)” (phs000993.v3.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1) and at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for “NHLBI TOPMed: Genetic Epidemiology of COPD (COPDGene)” (phs000951.v3.p3) was performed at the University of Washington Northwest Genomics Center (3R01HL089856‐08S1) and at the Broad Institute of MIT and Harvard (HHSN268201500014C). Genome sequencing for “NHLBI TOPMed: The Vanderbilt Atrial Fibrillation Ablation Registry” (phs000997.v3.p2) was performed at the Broad Institute of MIT and Harvard (3U54HG003067‐12S2, 3U54HG003067‐13S1). Genome sequencing for “NHLBI TOPMed: The Jackson Heart Study” (phs000964.v3.p1) was performed at the University of Washington Northwest Genomics Center (HHSN268201100037C). Genome sequencing for “NHLBI TOPMed: Genetics of Cardiometabolic Health in the Amish” (phs000956.v3.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL121007‐01S1). Genome sequencing for “NHLBI TOPMed: Massachusetts General Hospital Atrial Fibrillation (MGH AF) Study” (phs001062.v3.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1, 3U54HG003067‐12S2, 3U54HG003067‐13S1, 3UM1HG008895‐01S2). Genome sequencing for “NHLBI TOPMed: The Framingham Heart Study” (phs000974.v3.p2) was performed at the Broad Institute of MIT and Harvard (3U54HG003067‐12S2). Core support including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering, were provided by the TOPMed Informatics Research Center (3R01HL‐117626‐02S1; contract HHSN268201800002I). Core support including phenotype harmonization, data management, sample‐identity QC, and general program coordination were provided by the TOPMed Data Coordinating Center (R01HL‐120393; U01HL‐120393; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. The Atherosclerosis Risk in Communities study has been funded in whole or in part with federal funds from the National Heart, Lung, and Blood Institute, National Institute of Health, Department of Health and Human Services, under contract numbers (HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I, and HHSN268201700005I). The authors thank the staff and participants of the ARIC study for their important contributions. The research reported in this article was supported by grants from the National Institutes of Health (NIH) National Heart, Lung, and Blood Institute grants R01 HL090620 and R01 HL111314, the NIH National Center for Research Resources for Case Western Reserve University and Cleveland Clinic Clinical and Translational Science Award (CTSA) UL1‐RR024989, the Department of Cardiovascular Medicine philanthropic research fund, Heart and Vascular Institute, Cleveland Clinic, the Fondation Leducq grant 07‐CVD 03, and the Atrial Fibrillation Innovation Center, state of Ohio. This research was supported by contracts HHSN268201200036C, HHSN268200800007C, N01‐HC85079, N01‐HC‐85080, N01‐HC‐85081, N01‐HC‐85082, N01‐HC‐85083, N01‐HC‐85084, N01‐HC‐85085, N01‐HC‐85086, N01‐HC‐35129, N01‐HC‐15103, N01‐HC‐55222, N01‐HC‐75150, N01‐HC‐45133, and N01‐ HC‐85239; grant numbers U01 HL080295 and U01 HL130014 from the National Heart, Lung, and Blood Institute, and R01 AG023629 from the National Institute on Aging, with additional contribution from the National Institute of Neurological Disorders and Stroke. A full list of principal CHS investigators and institutions can be found at https://chs-nhlbi.org/pi. This article was not prepared in collaboration with CHS investigators and does not necessarily reflect the opinions or views of CHS or the NHLBI. We thank the Broad Institute for generating high‐quality sequence data supported by NHLBI grant 3R01HL092577‐06S1 to Dr. Patrick Ellinor. Funded in part by grants from the National Institutes of Health, National Heart, Lung, and Blood Institute (HL66216 and HL83141), and the National Human Genome Research Institute (HG04735). The Women's Genome Health Study (WGHS) is supported by HL 043851 and HL099355 from the National Heart, Lung, and Blood Institute and CA 047988 from the National Cancer Institute, the Donald W. Reynolds Foundation with collaborative scientific support and funding for genotyping provided by Amgen. AF end‐point confirmation was supported by HL‐093613 and a grant from the Harris Family Foundation and Watkin's Foundation. The Genetics and Epidemiology of Asthma in Barbados is supported by National Institutes of Health (NIH) National Heart, Lung, and Blood Institute TOPMed (R01 HL104608‐S1), and R01 AI20059, K23 HL076322, and RC2 HL101651. The research reported in this article was supported by grants from the American Heart Association to Dr. Darbar (EIA 0940116N), and grants from the National Institutes of Health (NIH) to Dr. Darbar (HL092217), and Dr. Roden (U19 HL65962, and UL1 RR024975). This project was also supported by a CTSA award (UL1TR000445) from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences of the NIH. The research reported in this article was supported by grants HL068986, HL085251, HL095080, and HL073410 from the National Heart, Lung, and Blood Institute. This article was not prepared in collaboration with Heart and Vascular Health (HVH) Study investigators and does not necessarily reflect the opinions or views of the HVH Study or the NHLBI. This research used data generated by the COPDGene study, which was supported by NIH grants U01 HL089856 and U01 HL089897. The COPDGene project is also supported by the COPD Foundation through contributions made by an Industry Advisory Board composed of Pfizer, AstraZeneca, Boehringer Ingelheim, Novartis, and Sunovion. Centralized read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL‐117626‐02S1; contract HHSN268201800002I). Phenotype harmonization, data management, sample‐identity QC, and general study coordination were provided by the TOPMed Data Coordinating Center (3R01HL‐120393‐02S1; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. This study is part of the Centers for Common Disease Genomics (CCDG) program, a large‐scale genome sequencing effort to identify rare risk and protective alleles that contribute to a range of common disease phenotypes. The CCDG program is funded by the National Human Genome Research Institute (NHGRI) and the National Heart, Lung, and Blood Institute (NHLBI). Sequencing was completed at the Human Genome Sequencing Center at Baylor College of Medicine under NHGRI grant UM1 HG008898. The research reported in this article was supported by grants from the American Heart Association to Dr. Shoemaker (11CRP742009) and Dr. Darbar (EIA 0940116N), and grants from the National Institutes of Health (NIH) to Dr. Darbar (R01 HL092217) and Dr. Roden (U19 HL65962 and UL1 RR024975). The project was also supported by a CTSA award (UL1 TR00045) from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the NIH. The Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I/HHSN26800001), and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I, and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute for Minority Health and Health Disparities (NIMHD). The authors also thank the staffs and participants of the JHS. The Amish studies on which these data are based were supported by NIH grants R01 AG18728, U01 HL072515, R01 HL088119, R01 HL121007, and P30 DK072488. See publication PMID: 18440328. The research reported in this article was supported by NIH grants K23HL071632, K23HL114724, R21DA027021, R01HL092577, R01HL092577S1, R01HL104156, K24HL105780, and U01HL65962. The research has also been supported by an Established Investigator Award from the American Heart Association (13EIA14220013) and by support from the Fondation Leducq (14CVD01). This article was not prepared in collaboration with MGH AF Study investigators and does not necessarily reflect the opinions or views of the MGH AF Study investigators or the NHLBI. The Framingham Heart Study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with Boston University (contract nos. N01‐HC‐25195, HHSN268201500001I, and 75N92019D00031). This article was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, Boston University, or NHLBI.

Item does not contain fulltext BACKGROUND: Several reports have identified different patterns of Parkinson's disease progression in individuals carrying missense variants in GBA or LRRK2 genes. The overall contribution of genetic factors to the severity and progression of Parkinson's disease, however, has not been well studied. OBJECTIVES: To test the association between genetic variants and the clinical features of Parkinson's disease on a genomewide scale. METHODS: We accumulated individual data from 12 longitudinal cohorts in a total of 4093 patients with 22,307 observations for a median of 3.81 years. Genomewide associations were evaluated for 25 cross-sectional and longitudinal phenotypes. Specific variants of interest, including 90 recently identified disease-risk variants, were also investigated post hoc for candidate associations with these phenotypes. RESULTS: Two variants were genomewide significant. Rs382940(T>A), within the intron of SLC44A1, was associated with reaching Hoehn and Yahr stage 3 or higher faster (hazard ratio 2.04 [1.58-2.62]; P value = 3.46E-8). Rs61863020(G>A), an intergenic variant and expression quantitative trait loci for alpha-2A adrenergic receptor, was associated with a lower prevalence of insomnia at baseline (odds ratio 0.63 [0.52-0.75]; P value = 4.74E-8). In the targeted analysis, we found 9 associations between known Parkinson's risk variants and more severe motor/cognitive symptoms. Also, we replicated previous reports of GBA coding variants (rs2230288: p.E365K; rs75548401: p.T408M) being associated with greater motor and cognitive decline over time, and an APOE E4 tagging variant (rs429358) being associated with greater cognitive deficits in patients. CONCLUSIONS: We identified novel genetic factors associated with heterogeneity of Parkinson's disease. The results can be used for validation or hypothesis tests regarding Parkinson's disease. (c) 2019 International Parkinson and Movement Disorder Society.

OBJECTIVE The goal of this study was to refine our understanding of disease risk attributable to common genetic variation in SNCA, a major locus in Parkinson disease, with potential implications for clinical trials targeting α-synuclein. We aimed to dissect the multiple independent association signals, stratify individuals by SNCA-specific risk profiles, and explore expression quantitative trait loci. METHODS We analyzed participant-level data from 12,503 patients and 12,502 controls, optimizing a risk model and assessing SNCA-specific risk scores and haplotypes as predictors of individual risk. We also explored hypotheses about functional mechanisms and correlated risk variants to gene expression in human brain and protein levels in cerebrospinal fluid. RESULTS We report and replicate a novel, third independent association signal at genome-wide significance level downstream of SNCA (rs2870004, p = 3.0*10-8 , odds ratio [OR] = 0.88, 95% confidence interval [CI] = 0.84-0.92). SNCA risk score stratification showed a 2-fold difference in disease susceptibility between top and bottom quintiles (OR = 1.99, 95% CI = 1.78-2.23). Contrary to previous reports, we provide evidence supporting top variant rs356182 as functional in itself and associated with a specific SNCA 5' untranslated region transcript isoform in frontal cortex. INTERPRETATION The SNCA locus harbors a minimum of 3 independent association signals for Parkinson disease. We demonstrate a fine-grained stratification of α-synuclein-related genetic burden in individual patients of potential future clinical relevance. Further efforts to pinpoint the functional mechanisms are warranted, including studies of the likely causal top variant rs356182 and its role in regulating levels of specific SNCA mRNA transcript variants. Ann Neurol 2018;83:117-129.

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 amyotrophic lateral sclerosis 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 (14,671 cases and 17,667 controls) and whole genome sequencing 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 identified no significant association between ANG variants and PD risk.

Background: Tubulin mutations are a cause of neuronal migrational disorders referred to as tubulinopathies. Mutations in tubulin genes can have a severe impact on microtubule function and result in heterogeneous clinical presentations. Current understanding of the clinical spectrum of tubulinopathies is predominantly based on research in fetal tissue and early-childhood cases. Methods: Testing of candidate genes followed by whole-exome sequencing was performed in an adult woman with a neurodevelopmental, hyperkinetic movement disorder, to identify the underlying genetic cause. Bioinformatic modeling and a systematic review of literature was conducted to investigate genotype-phenotype correlations. Results: The patient was found to carry a heterozygous, de novo c.722G>A, p.R241H mutation in a conserved domain of TUBB2B, encoding the β-isoform of tubulin. In silico analysis indicated that this mutation was pathogenic. On neuroimaging, the patient had asymmetric pachygyria and dysmorphic basal ganglia. Her neurological examination demonstrated mild cognitive impairment, myoclonus-dystonia, and skeletal anomalies. Conclusions: Here, we report the unique phenotype of an adult TUBB2B mutation carrier. This case illustrates a relatively mild phenotype compared to previously described fetal and early childhood cases. This highlights the importance of obtaining molecular genetic testing in individuals with a high probability of a genetic disease, including undiagnosed adult patients.

Multiple genes have been implicated in Parkinson’s disease (PD), including causal gene variants and risk variants typically identified using genome-wide association studies (GWAS). Variants in the alcohol dehydrogenase genes ADH1C and ADH1B are among the genes that have been associated with PD, suggesting that this family of genes may be important in PD. As part of the International Parkinson’s Disease Genomics Consortium’s (IPDGC) efforts to scrutinize previously reported risk factors for PD, we explored genetic variation in the alcohol dehydrogenase genes ADH1A, ADH1B, ADH1C, ADH4, ADH5, ADH6, and ADH7 using imputed GWAS data from 15,097 cases and 17,337 healthy controls. Rare-variant association tests and single-variant score tests did not show any statistically significant association of alcohol dehydrogenase genetic variation with the risk for PD.

Mendelian randomization is a method for exploring observational associations to find evidence of causality. 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. 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. 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 PD. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society., AJN reports grants from Parkinson’s UK, Barts Charity, Leonard Wolfson Experimental Neurology Centre, UCL Movement Disorders Centre and the Virginia Kieley Benefaction; honoraria or consultancy fees from Britannia, Global Kinetics Corporation, Profile Pharmaceuticals, Guide point, Biogen and Roche. KH and DAH are employees of 23andMe and hold stock or stock options in 23andMe. DAL reports grants from the Medical Research Council, numerous charitable funders,Medtronic and Roche. ZG-O reports consultancy fees from Inceptions Sciences,Idorsia, Denali, Lysosomal Therapeutics inc. HM reports reports consultancy from Biogen, UCB, Abbvie, Denali, Biohaven; lecture fees/honoraria from Biogen, UCB,C4X Discovery, GE-Healthcare, Welcome Trust, Movement Disorders Society; Research Grants from Parkinson’s UK, Cure Parkinson’s Trust, PSP Association, CBD Solutions, Drake Foundation, Medical Research Council. Dr Morris is a co-applicanton a patent application related to C9ORF72 (PCT/GB2012/052140).

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.

Parkinson's disease (PD) is a complex neurodegenerative disease with a strong genetic component. To date, several genes have been associated with monogenic forms of the disease, but these only explain a small fraction of the observed familial aggregation in PD. Recently, a heterozygous loss-of-function variant in LIN28A was associated with PD pathogenesis in the Asian population. Here, we comprehensively investigate the role of LIN28A variants in PD patients of European ancestry and assess susceptibility using individual-level genotyping data from 14,671 PD cases and 17,667 controls, as well as whole-genome sequencing data from 1647 patients with PD and 1050 controls. In addition, we further assess the summary statistics from the most recent genome-wide association studies 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.

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 2 complementary outcome traits derived from the Unified Parkinson's Disease Rating Scale, including dichotomous motor subtype (TD vs PIGD) or a continuous tremor/PIGD score ratio. Logistic or linear regression models were adjusted for sex, age at 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 (rs199351, 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% confidence interval = −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.