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GBA1 variants are among the most common genetic risk factors for Parkinson's disease (PD). GBA1 variants can be classified into three categories based on their role in Gaucher's disease (GD) or PD: severe, mild, and risk variant (for PD).This review aims to generate and share a comprehensive database for GBA1 variants reported in PD to support future research and clinical trials.We performed a literature search for all GBA1 variants that have been reported in PD. The data have been standardized and complemented with variant classification, odds ratio if available, and other data.We found 371 GBA1 variants reported in PD: 22 mild, 84 severe, 3 risk variants, and 262 of unknown status. We created a browser containing up-to-date information on these variants (https://pdgenetics.shinyapps.io/GBA1Browser/).The classification and browser presented in this work should inform and support basic, translational, and clinical research on GBA1-PD. © 2023 International Parkinson and Movement Disorder Society.

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

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.

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

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

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.

Parkinson’s disease is a complex neurodegenerative disorder that is about 1.5 times more prevalent in males than females. Extensive work has been done to identify the genetic risk factors behind Parkinson’s disease on autosomes and more recently on Chromosome X, but work remains to be done on the male-specific Y chromosome. In an effort to explore the role of the Y chromosome in Parkinson’s disease, we analysed whole-genome sequencing data from the Accelerating Medicines Partnership—Parkinson’s disease initiative (1466 cases and 1664 controls), genotype data from NeuroX (3491 cases and 3232 controls) and genotype data from UKBiobank (182 517 controls, 1892 cases and 3783 proxy cases), all consisting of male European ancestry samples. We classified sample Y chromosomes by haplogroup using three different tools for comparison (Snappy, Yhaplo and Y-LineageTracker) and meta-analysed this data to identify haplogroups associated with Parkinson’s disease. This was followed up with a Y-chromosome association study to identify specific variants associated with disease. We also analysed blood-based RNASeq data obtained from the Accelerating Medicines Partnership—Parkinson’s disease initiative (1020 samples) and RNASeq data obtained from the North American Brain Expression Consortium (171 samples) to identify Y-chromosome genes differentially expressed in cases, controls, specific haplogroups and specific tissues. RNASeq analyses suggest Y-chromosome gene expression differs between brain and blood tissues but does not differ significantly in cases, controls or specific haplogroups. Overall, we did not find any strong associations between Y-chromosome genetics and Parkinson’s disease, suggesting the explanation for the increased prevalence in males may lie elsewhere.

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

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

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

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

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.

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

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.

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

SECONDARY infection is a major hazard to the hospitalized patient.1 , 2 Numerous institutionacquired infections, attributed to a wide variety of sources and micro-organisms, have been described. Th...