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      Proteome wide association studies of LRRK2 variants identify novel causal and druggable proteins for Parkinson’s disease

      research-article
      1 , 2 , 3 , 1 , 2 , 3 , 1 , 2 , 3 , 1 , 2 , 3 , 1 , 2 , 3 , 1 , 2 , 3 , 1 , 2 , 3 , 1 , 2 , 3 , 4 , 1 , 2 , 3 , 5 , 5 , 6 , 3 , 7 , 8 , 7 , 3 , 7 , 9 , 10 , 9 , 10 , 9 , 10 , 9 , 10 , 9 , 10 , 9 , 10 , 3 , 7 , Dominantly Inherited Alzheimer Network (DIAN) Consortia, 1 , 2 , 7 , 3 , 7 , 8 , 1 , 2 , 3 , 4 , 1 , 2 , 3 ,
      NPJ Parkinson's Disease
      Nature Publishing Group UK
      Genomics, Biomarkers

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          Abstract

          Common and rare variants in the LRRK2 locus are associated with Parkinson’s disease (PD) risk, but the downstream effects of these variants on protein levels remain unknown. We performed comprehensive proteogenomic analyses using the largest aptamer-based CSF proteomics study to date (7006 aptamers (6138 unique proteins) in 3107 individuals). The dataset comprised six different and independent cohorts (five using the SomaScan7K (ADNI, DIAN, MAP, Barcelona-1 (Pau), and Fundació ACE (Ruiz)) and the PPMI cohort using the SomaScan5K panel). We identified eleven independent SNPs in the LRRK2 locus associated with the levels of 25 proteins as well as PD risk. Of these, only eleven proteins have been previously associated with PD risk (e.g., GRN or GPNMB). Proteome-wide association study (PWAS) analyses suggested that the levels of ten of those proteins were genetically correlated with PD risk, and seven were validated in the PPMI cohort. Mendelian randomization analyses identified GPNMB, LCT, and CD68 causal for PD and nominate one more (ITGB2). These 25 proteins were enriched for microglia-specific proteins and trafficking pathways (both lysosome and intracellular). This study not only demonstrates that protein phenome-wide association studies (PheWAS) and trans-protein quantitative trail loci (pQTL) analyses are powerful for identifying novel protein interactions in an unbiased manner, but also that LRRK2 is linked with the regulation of PD-associated proteins that are enriched in microglial cells and specific lysosomal pathways.

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          Most cited references34

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          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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            PLINK: a tool set for whole-genome association and population-based linkage analyses.

            Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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              The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

              Abstract Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.
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                Author and article information

                Contributors
                cruchagac@wustl.edu
                Journal
                NPJ Parkinsons Dis
                NPJ Parkinsons Dis
                NPJ Parkinson's Disease
                Nature Publishing Group UK (London )
                2373-8057
                8 July 2023
                8 July 2023
                2023
                : 9
                : 107
                Affiliations
                [1 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Psychiatry, , Washington University School of Medicine, ; St. Louis, MO 63110 USA
                [2 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, NeuroGenomics and Informatics Center, , Washington University School of Medicine, ; St. Louis, MO 63110 USA
                [3 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Hope Center for Neurological Disorders, , Washington University School of Medicine, ; St. Louis, MO 63110 USA
                [4 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Division of Biostatistics, , Washington University, ; St. Louis, MO 63110 USA
                [5 ]GRID grid.414875.b, ISNI 0000 0004 1794 4956, Memory Disorders Unit, Department of Neurology, , University Hospital Mutua Terrassa, ; Terrassa, Spain
                [6 ]GRID grid.411438.b, ISNI 0000 0004 1767 6330, Unit of Neurodegenerative diseases, Department of Neurology, , University Hospital Germans Trias i Pujol and The Germans Trias i Pujol Research Institute (IGTP) Badalona, ; Barcelona, Spain
                [7 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Neurology, , Washington University School of Medicine, ; St. Louis, MO 63110 USA
                [8 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Pathology and Immunology, , Washington University School of Medicine, ; St. Louis, MO 63110 USA
                [9 ]GRID grid.410675.1, ISNI 0000 0001 2325 3084, Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, ; Barcelona, Spain
                [10 ]GRID grid.418264.d, ISNI 0000 0004 1762 4012, Networking Research Center On Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, ; Madrid, Spain
                Author information
                http://orcid.org/0000-0002-5261-689X
                http://orcid.org/0000-0003-3725-9553
                http://orcid.org/0000-0003-4577-5590
                http://orcid.org/0000-0002-8537-3935
                http://orcid.org/0000-0002-7493-8777
                http://orcid.org/0000-0002-1680-1465
                http://orcid.org/0000-0002-1191-5893
                http://orcid.org/0000-0003-0125-5403
                http://orcid.org/0000-0002-2148-381X
                http://orcid.org/0000-0002-0660-0950
                http://orcid.org/0000-0003-2617-3009
                http://orcid.org/0000-0003-2633-2495
                http://orcid.org/0000-0003-3263-8917
                http://orcid.org/0000-0002-3443-7716
                http://orcid.org/0000-0002-8021-4070
                http://orcid.org/0000-0002-0276-2899
                Article
                555
                10.1038/s41531-023-00555-4
                10329646
                37422510
                b0e9f485-d290-4ad8-9bde-d6f524ec56d9
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 10 January 2023
                : 29 June 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000864, Michael J. Fox Foundation for Parkinson’s Research (Michael J. Fox Foundation);
                Funded by: FundRef https://doi.org/10.13039/100000957, Alzheimer’s Association;
                Award ID: ZEN-22-848604
                Award Recipient :
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                © Springer Nature Limited 2023

                genomics,biomarkers
                genomics, biomarkers

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