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

Knowledge of genomic features specific to the human lineage may provide insights into brain-related diseases. We leverage high-depth whole genome sequencing data to generate a combined annotation identifying regions simultaneously depleted for genetic variation (constrained regions) and poorly conserved across primates. We propose that these constrained, non-conserved regions (CNCRs) have been subject to human-specific purifying selection and are enriched for brain-specific elements. We find that CNCRs are depleted from protein-coding genes but enriched within lncRNAs. We demonstrate that per-SNP heritability of a range of brain-relevant phenotypes are enriched within CNCRs. We find that genes implicated in neurological diseases have high CNCR density, including APOE, highlighting an unannotated intron-3 retention event. Using human brain RNA-sequencing data, we show the intron-3-retaining transcript to be more abundant in Alzheimer’s disease with more severe tau and amyloid pathological burden. Thus, we demonstrate potential association of human-lineage-specific sequences in brain development and neurological disease., Knowledge of genomic features specific to humans may be important for understanding disease. Here the authors demonstrate a potential role for these human-lineage-specific sequences in brain development and neurological disease.

OBJECTIVE: Parkinson's disease (PD) is a complex neurodegenerative disorder. Men are on average ~ 1.5 times more likely to develop PD compared to women with European ancestry. Over the years, genomewide 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. METHODS: In an effort to study sex-specific genetic factors associated with PD, we explored 2 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. RESULTS: In total, 19 genomewide significant regions were identified and no sex-specific effects were observed. A high genetic correlation between the male and female PD GWAS were identified (rg = 0.877) and heritability estimates were identical between male and female PD cases (~ 20%). INTERPRETATION: 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 cases 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 female patients. ANN NEUROL 2021;90:41-48.

Genome-wide association studies have generated an increasing number of common genetic variants associated with neurological and psychiatric disease risk. An improved understanding of the genetic control of gene expression in human brain is vital considering this is the likely modus operandum for many causal variants. However, human brain sampling complexities limit the explanatory power of brain-related expression quantitative trait loci (eQTL) and allele-specific expression (ASE) signals. We address this, using paired genomic and transcriptomic data from putamen and substantia nigra from 117 human brains, interrogating regulation at different RNA processing stages and uncovering novel transcripts. We identify disease-relevant regulatory loci, find that splicing eQTLs are enriched for regulatory information of neuron-specific genes, that ASEs provide cell-specific regulatory information with evidence for cellular specificity, and that incomplete annotation of the brain transcriptome limits interpretation of risk loci for neuropsychiatric disease. This resource of regulatory data is accessible through our web server, http://braineacv2.inf.um.es/. © 2020, The Author(s).

Special Issue: Focused Ultrasound in Parkinson's Disease., [Background] Single nucleotide polymorphisms (SNPs) in the α‐synuclein (SNCA ) gene are associated with differential risk and age at onset (AAO) of both idiopathic and Leucine‐rich repeat kinase 2 (LRRK2)‐associated Parkinson's disease (PD). Yet potential combinatory or synergistic effects among several modulatory SNPs for PD risk or AAO remain largely underexplored., [Objectives] The mechanistic target of rapamycin (mTOR ) signaling pathway is functionally impaired in PD. Here we explored whether SNPs in the mTOR pathway, alone or by epistatic interaction with known susceptibility factors, can modulate PD risk and AAO., [Methods] Based on functional relevance, we selected a total of 64 SNPs mapping to a total of 57 genes from the mTOR pathway and genotyped a discovery series cohort encompassing 898 PD patients and 921 controls. As a replication series, we screened 4170 PD and 3014 controls available from the International Parkinson's Disease Genomics Consortium., [Results] In the discovery series cohort, we found a 4‐loci interaction involving STK11 rs8111699, FCHSD1 rs456998, GSK3B rs1732170, and SNCA rs356219, which was associated with an increased risk of PD (odds ratio = 2.59, P, [Conclusions] These findings indicate that genetic variability in the mTOR pathway contributes to SNCA effects in a nonlinear epistatic manner to modulate differential AAO in PD, unraveling the contribution of this cascade in the pathogenesis of the disease. © 2019 International Parkinson and Movement Disorder Society, Funding Information; Fundació la Marató de TV3. Grant Number: 60510; Michael J. Fox Foundation for Parkinson's Research. Grant Numbers: Dyskinesia Challenge 2014, MJF_PPMI_10_001, PI044024; National Institutes of Health. Grant Number: LM010098; Secretaría de Estado de Investigación, Desarrollo e Innovación. Grant Number: SAF2014‐57160R and SAF2017‐88812R.

Mitochondrial dysfunction has been implicated in the etiology 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. 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. We further reported 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 randomization, 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. Additional information International Parkinson’s Disease Genomics Consortium (IPDGC) Members A. Noyce13, A. Tucci14, B. Middlehurst1, D. Kia15, M. Tan16, H. Houlden14, H. R. Morris16, H. Plun-Favreau14, P. Holmans17, J. Hardy14, D. Trabzuni14,18, J. Bras19, K. Mok14, K. Kinghorn20, N. Wood15, P. Lewis21, R. Guerreiro14,19, R. Lovering22, L. R’Bibo14, M. Rizig14, V. Escott-Price22,23, V. Chelban14, T. Foltynie6, N. Williams24, A. Brice25, F. Danjou25, S. Lesage25, M. Martinez26, A. Giri27,28, C. Schulte27,28, K. Brockmann27,28, J. Simón-Sánchez27,28, P. Heutink27,28, P. Rizzu28, M. Sharma29, T. Gasser27,28, A. Nicolas2, M. Cookson2, F. Faghri2,30, D. Hernandez2, J. Shulman31,32, L. Robak33, S. Lubbe34, S. Finkbeiner35,36,37, N. Mencacci38, C. Lungu39, S. Scholz40, X. Reed2, H. Leonard2, G. Rouleau7, L. Krohan41, J. van Hilten42, J. Marinus42, A. Adarmes-Gómez43, M. Aguilar44, I. Alvarez44, V. Alvarez45, F. Javier Barrero46, J. Bergareche Yarza47, I. Bernal-Bernal43, M. Blazquez45, M. Bonilla-Toribio Bernal43, M. Boungiorno44, Dolores Buiza-Rueda43, A. Cámara48, M. Carcel44, F. Carrillo43, M. Carrión-Claro43, D. Cerdan49, J. Clarimón50,51, Y. Compta48, M. Diez-Fairen44, O. Dols-Icardo50,51, J. Duarte49, R. l. Duran52, F. Escamilla-Sevilla53, M. Ezquerra48, M. Fernández48, R. Fernández-Santiago48, C. Garcia45, P. García-Ruiz54, P. Gómez-Garre43, M. Gomez Heredia55, I. Gonzalez-Aramburu56, A. Gorostidi Pagola57, J. Hoenicka58, J. Infante51,56, S. Jesús43, A. Jimenez-Escrig59, J. Kulisevsky51,60, M. Labrador-Espinosa43, J. Lopez-Sendon59, A. López de Munain Arregui59, D. Macias43, I. Martínez Torres61, J. Marín51,60, M. Jose Marti48, J. Martínez-Castrillo59, C. Méndez-del-Barrio43, M. Menéndez González43, A. Mínguez53, P. Mir43, E. Mondragon Rezola57, E. Muñoz48, J. Pagonabarraga51,60, P. Pastor44, F. Perez Errazquin55, T. Periñán-Tocino43, J. Ruiz-Martínez57, C. Ruz52, A. Sanchez Rodriguez56, M. Sierra56, E. Suarez-Sanmartin4, C. Tabernero59, J. Pablo Tartari44, C. Tejera-Parrado43, E. Tolosa48, F. Valldeoriola48, L. Vargas-González43, L. Vela62, F. Vives52, A. Zimprich63, L. Pihlstrom64, P. Taba65, K. Majamaa66,67, A. Siitonen66, N. Okubadejo68, O. Ojo68 1 Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, UK 2Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, 20892, USA 3Department of Medical and Molecular Genetics, King’s College London School of Basic and Medical Biosciences, London, SE1 9RT, UK 4Clinical Genetics Unit, Guys and St. Thomas’ NHS Foundation Trust, London, SE1 9RT, UK 5Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, 30100, Murcia, Spain 6Department of Neurodegenerative Disease, UCL Institute of Neurology, 10-12 Russell Square House, London, UK 7Montreal Neurological Institute, McGill University, Montréal, QC, Canada 8Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada 9Department of Human Genetics, McGill University, Montréal, QC, Canada 10Data Tecnica International, Glen Echo, MD, 20812, USA 11The Perron Institute for Neurological and Translational Science, 8 Verdun Street, Nedlands, WA, 6009, Australia 12Centre for Comparative Genomics, Murdoch University, Murdoch, 6150, Australia 13Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, QMUL, London, UK 14Department of Molecular Neuroscience, UCL Institute of Neurology, London, UK 15UCL Genetics Institute; and Department of Molecular Neuroscience, UCL Institute of Neurology, London, UK 16Department of Clinical Neuroscience, University College London, London, UK 17Biostatistics & Bioinformatics Unit, Institute of Psychological Medicine and Clinical Neuroscience, MRC Centre for Neuropsychiatric Genetics & Genomics, Cardiff, UK 18Department of Genetics, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia 19UK Dementia Research Institute at UCL and Department of Molecular Neuroscience, UCL Institute of Neurology, London, UK 20Institute of Healthy Ageing, University College London, London, UK 21University of Reading, Reading, UK 22University College London, London, UK 23MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff, UK 24Cardiff University School of Medicine, Cardiff, UK 25Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS, UMR 7225, Sorbonne Universités, UPMC University Paris 06, UMR S 1127, AP-HP, Pitié-Salpêtrière Hospital, Paris, France 26INSERM UMR 1220; and Paul Sabatier University, Toulouse, France 27Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany 28DZNE, German Center for Neurodegenerative Diseases, Tübingen, Germany 29Centre for Genetic Epidemiology, Institute for Clinical Epidemiology and Applied Biometry, University of Tubingen, Tubingen, Germany 30Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA 31Departments of Neurology, Neuroscience, and Molecular & Human Genetics, Baylor College of Medicine, Houston, TX, USA 32Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX, USA 33Baylor College of Medicine, Houston, TX, USA 34Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA 35Departments of Neurology and Physiology, University of California, San Francisco, CA, USA 36Gladstone Institute of Neurological Disease, San Francisco, CA, USA 37Taube/Koret Center for Neurodegenerative Disease Research, San Francisco, CA, USA) 38 (Northwestern University Feinberg School of Medicine, Chicago, IL, USA) 39 (National Institutes of Health Division of Clinical Research, NINDS, National Institutes of Health, Bethesda, MD, USA) 40Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA 41Department of Human Genetics, McGill University, Montréal, QC H3A 0G4, Canada 42Department of Neurology, Leiden University Medical Center, Leiden, Netherlands 43Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/ Universidad de Sevilla, Seville, Spain 44Fundació Docència i Recerca Mútua de Terrassa and Movement Disorders Unit, Department of Neurology, University Hospital Mutua de Terrassa, Terrassa, Barcelona, Spain 45Hospital Universitario Central de Asturias, Oviedo, Spain 46Hospital Universitario Parque Tecnologico de la Salud, Granada, Spain 47Instituto de Investigación Sanitaria Biodonostia, San Sebastián, Spain 48Hospital Clinic de Barcelona, Barcelona, Spain 49Hospital General de Segovia, Segovia, Spain 50Memory Unit, Department of Neurology, IIB Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain 51Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain 52Centro de Investigacion Biomedica, Universidad de Granada, Granada, Spain 53Hospital Universitario Virgen de las Nieves, Instituto de Investigación Biosanitaria de Granada, Granada, Spain 54Instituto de Investigación Sanitaria Fundación Jiménez Díaz, Madrid, Spain 55Hospital Universitario Virgen de la Victoria, Malaga, Spain 56Hospital Universitario Marqués de Valdecilla-IDIVAL, Santander, Spain 57Instituto de Investigación Sanitaria Biodonostia, San Sebastián, Spain 58Institut de Recerca Sant Joan de Déu, Barcelona, Spain 59Hospital Universitario Ramón y Cajal Madrid, Madrid, Spain 60Movement Disorders Unit, Department of Neurology, IIB Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain 61Department of Neurology, Instituto de Investigación Sanitaria La Fe, Hospital Universitario y Politécnico La Fe, Valencia, Spain 62Department of Neurology, Hospital Universitario Fundación Alcorcón, Madrid, Spain 63Department of Neurology, Medical University of Vienna, Vienna, Austria 64Department of Neurology, Oslo University Hospital, Oslo, Norway 65Department of Neurology and Neurosurgery, University of Tartu, Tartu, Estonia 66Institute of Clinical Medicine, Department of Neurology, University of Oulu, Oulu, Finland 67Department of Neurology and Medical Research Center, Oulu University Hospital, Oulu, Finland 68University of Lagos, Yaba, Lagos State, Nigeria

Background Single nucleotide polymorphisms (SNPs) in the alpha-synuclein (SNCA) gene are associated with differential risk and age at onset (AAO) of both idiopathic and Leucine-rich repeat kinase 2 (LRRK2)-associated Parkinson's disease (PD). Yet potential combinatory or synergistic effects among several modulatory SNPs for PD risk or AAO remain largely underexplored. Objectives The mechanistic target of rapamycin (mTOR) signaling pathway is functionally impaired in PD. Here we explored whether SNPs in the mTOR pathway, alone or by epistatic interaction with known susceptibility factors, can modulate PD risk and AAO. Methods Based on functional relevance, we selected a total of 64 SNPs mapping to a total of 57 genes from the mTOR pathway and genotyped a discovery series cohort encompassing 898 PD patients and 921 controls. As a replication series, we screened 4170 PD and 3014 controls available from the International Parkinson's Disease Genomics Consortium. Results In the discovery series cohort, we found a 4-loci interaction involving STK11 rs8111699, FCHSD1 rs456998, GSK3B rs1732170, and SNCA rs356219, which was associated with an increased risk of PD (odds ratio = 2.59, P < .001). In addition, we also found a 3-loci epistatic combination of RPTOR rs11868112 and RPS6KA2 rs6456121 with SNCA rs356219, which was associated (odds ratio = 2.89; P < .0001) with differential AAO. The latter was further validated (odds ratio = 1.56; P = 0.046-0.047) in the International Parkinson's Disease Genomics Consortium cohort. Conclusions These findings indicate that genetic variability in the mTOR pathway contributes to SNCA effects in a nonlinear epistatic manner to modulate differential AAO in PD, unraveling the contribution of this cascade in the pathogenesis of the disease. (c) 2019 International Parkinson and Movement Disorder Society

Parkinson's disease (PD), with its characteristic loss of nigrostriatal dopaminergic neurons and deposition of α-synuclein in neurons, is often considered a neuronal disorder. However, in recent years substantial evidence has emerged to implicate glial cell types, such as astrocytes and microglia. In this study, we used stratified LD score regression and expression-weighted cell-type enrichment together with several brain-related and cell-type-specific genomic annotations to connect human genomic PD findings to specific brain cell types. We found that PD heritability attributable to common variation does not enrich in global and regional brain annotations or brain-related cell-type-specific annotations. Likewise, we found no enrichment of PD susceptibility genes in brain-related cell types. In contrast, we demonstrated a significant enrichment of PD heritability in a curated lysosomal gene set highly expressed in astrocytic, microglial, and oligodendrocyte subtypes, and in LoF-intolerant genes, which were found highly expressed in almost all tested cellular subtypes. Our results suggest that PD risk loci do not lie in specific cell types or individual brain regions, but rather in global cellular processes detectable across several cell types.