A network-based systems genetics framework identifies pathobiology and drug repurposing in Parkinson’s disease

A network-based systems genetics framework identifies pathobiology and drug repurposing in Parkinson’s disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A network-based systems genetics framework identifies pathobiology and drug repurposing in Parkinson’s disease Feixiong Cheng, Lijun Dou, Zhenxin Xu, Jielin Xu, Chang Su, Andrew Pieper, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4869009/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Jan, 2025 Read the published version in npj Parkinson's Disease → Version 1 posted 11 You are reading this latest preprint version Abstract Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder. However, current treatments are directed at symptoms and lack ability to slow or prevent disease progression. Large-scale genome-wide association studies (GWAS) have identified numerous genomic loci associated with PD, which may guide the development of disease-modifying treatments. We presented a systems genetics approach to identify potential risk genes and repurposable drugs for PD. First, we leveraged non-coding GWAS loci effects on multiple human brain-specific quantitative trait loci (xQTLs) under the protein-protein interactome (PPI) network. We then prioritized a set of PD likely risk genes (pdRGs) by integrating five types of molecular xQTLs: expression (eQTLs), protein (pQTLs), splicing (sQTLs), methylation (meQTLs), and histone acetylation (haQTLs). We also integrated network proximity-based drug repurposing and patient electronic health record (EHR) data observations to propose potential drug candidates for PD treatments. We identified 175 pdRGs from QTL-regulated GWAS findings, such as SNCA , CTSB , LRRK2, DGKQ , CD38 and CD44 . Multi-omics data validation revealed that the identified pdRGs are likely to be druggable targets, differentially expressed in multiple cell types and impact both the parkin ubiquitin-proteasome and alpha-synuclein (a-syn) pathways. Based on the network proximity-based drug repurposing followed by EHR data validation, we identified usage of simvastatin as being significantly associated with reduced incidence of PD (fall outcome: hazard ratio (HR) = 0.91, 95% confidence interval (CI): 0.87–0.94; for dementia outcome: HR = 0.88, 95% CI: 0.86-0.89), after adjusting for 267 covariates. Our network-based systems genetics framework identifies potential risk genes and repurposable drugs for PD and other neurodegenerative diseases if broadly applied. Health sciences/Neurology/Neurological disorders/Parkinson's disease Biological sciences/Computational biology and bioinformatics/Gene regulatory networks Drug repurposing Electronic Health Record (EHR) Genome-Wide Association Studies (GWAS) Parkinson’s disease (PD) Quantitative trait loci (QTL) Protein-protein interactions (PPI) Risk genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Full Text Additional Declarations There is a conflict of interest Dr. Cummings has provided consultation to Acadia, Acumen, ALZpath, Aprinoia, Artery, Biogen, Biohaven, BioXcel, Bristol-Myers Squib, Eisai, Fosun, GAP Foundation, Janssen, Karuna, Kinoxis, Lighthouse, Lilly, Lundbeck, LSP/eqt, Merck, MoCA Cognition, New Amsterdam, Novo Nordisk, Optoceutics, Otsuka, Oxford Brain Diagnostics, Praxis, Prothena, ReMYND, Roche, Scottish Brain Sciences, Signant Health, Simcere, sinaptica, TrueBinding, and Vaxxinity pharmaceutical, assessment, and investment companies. JC owns the copyright of the Neuropsychiatric Inventory. JC has stocks/options in Artery, Vaxxinity, Behrens, Alzheon, MedAvante-Prophase, Acumen. Dr. Leverenz has received consulting fees from consulting fees from Vaxxinity, grant support from GE Healthcare and serves on a Data Safety Monitoring Board for Eisai. Supplementary Files SupplementaryFigures12.pdf Supplementary Materials Supplementary Figure Legends Figure S1 Network-based pdRGs prediction by incorporating PD GWAS and xQTLs findings. (A) Bar plot showing the number of mapped genome wide xQTLs for each type. (B) Venn plot depicting the mapped 124 gene, where green, orange and blue represent the genes associated with lead SNPs, proxy SNPs and other SNPs, respectively. (C) Disease enrichment analysis for the predicted pdRGs (DisGeNET database, q 0) were used to perform Fisher’s test (i.e.,pdRGs compared with non-pdRGs). (E) Dot plot of canonical marker genes for cell type annotation. The dot size encodes the proportion of cells that express the gene (Pct. Exp), while the color encodes the scaled average expression level across those cells (Avg. exp). Figure S2 Demonstration of mechanism of actions (MOAs) for nine promising drug candidates for PD treatment, ordered by predicted score. SupplementaryTable1.zip Supplementary Table Legends Table S1 Mapped results of PD GWAS findings regulated by five types of brain-specific xQTLs. SupplementaryTable2.zip Table S2 Collected 127 PD-associated genes from TIGA database for model performance evaluation. SupplementaryTable3.zip Table S3 List of the predicted 175 putative pdRGs. SupplementaryTable4.zip Table S4 Disease enrichments of the predicted pdRGs based on DisGeNET database. SupplementaryTable5.zip Table S5 Summary of multi-omics validation for the inferred pdRGs. SupplementaryTable6.zip Table S6 Transcriptome dysregulation of pdRGs at single-nuclei resolution through snRNA-seq dataset GSE178265. SupplementaryTable7.zip Table S7 Prioritized PD drug candidates based on network proximity method (Z < -3). SupplementaryTable8.zip Table S8 Diagnosis code for PD and ND (fall or dementia). SupplementaryTable9.zip Table S9 Baseline comorbidity ICD codes. SupplementaryTable10.zip Table S10 EHR validation results of simvastatin with consideration of two types of PD outcomes (fall and dementia). Cite Share Download PDF Status: Published Journal Publication published 22 Jan, 2025 Read the published version in npj Parkinson's Disease → Version 1 posted Editorial decision: revise 15 Sep, 2024 Review # 2 received at journal 11 Sep, 2024 Review # 3 received at journal 29 Aug, 2024 Reviewer # 3 agreed at journal 19 Aug, 2024 Reviewer # 2 agreed at journal 19 Aug, 2024 Review # 1 received at journal 15 Aug, 2024 Reviewer # 1 agreed at journal 15 Aug, 2024 Reviewers invited by journal 15 Aug, 2024 Editor assigned by journal 07 Aug, 2024 Submission checks completed at journal 07 Aug, 2024 First submitted to journal 06 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4869009","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":340577977,"identity":"3399f7f2-01c4-440e-9dd6-08bbb1aa757d","order_by":0,"name":"Feixiong Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYDACCRBhY8PAwHyAJC1paQwMbAmkaTlMghb+2c3HHn5JOC/P38bA+LjiFzGW3DmWbiyTcNtwxjEGZsOzfURoMZDIMZOW/HE7wUC+gU2ysYcoLfnfpCUSziUYsDEQrSWHTfJDwgGIloYfRGiRuJFmJs2QkAz0C2OzYWMDEVr4ZyQ/k/yRYAcMMeaDDxv+EKEFBJh5wBRjAwNjG5FaGBE+INaWUTAKRsEoGFEAAKT8MmqgJc+hAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-1736-2847","institution":"Cleveland Clinic","correspondingAuthor":true,"prefix":"","firstName":"Feixiong","middleName":"","lastName":"Cheng","suffix":""},{"id":340577978,"identity":"f20ccf9b-7682-4e86-9eb1-ce637391af91","order_by":1,"name":"Lijun Dou","email":"","orcid":"","institution":"Cleveland Clinic","correspondingAuthor":false,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Dou","suffix":""},{"id":340577979,"identity":"b96ae26a-2837-43f7-aacb-2664c6b24eae","order_by":2,"name":"Zhenxin Xu","email":"","orcid":"","institution":"Cornell University","correspondingAuthor":false,"prefix":"","firstName":"Zhenxin","middleName":"","lastName":"Xu","suffix":""},{"id":340577980,"identity":"1030b118-da13-4b41-bd89-838ba090d6fc","order_by":3,"name":"Jielin Xu","email":"","orcid":"","institution":"Cleveland Clinic","correspondingAuthor":false,"prefix":"","firstName":"Jielin","middleName":"","lastName":"Xu","suffix":""},{"id":340577981,"identity":"be2ef48c-8757-4dad-aa04-431081816544","order_by":4,"name":"Chang Su","email":"","orcid":"","institution":"Weill Cornell Medical College","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Su","suffix":""},{"id":340577982,"identity":"8257ca44-936e-4cad-b13d-9a1516551939","order_by":5,"name":"Andrew Pieper","email":"","orcid":"","institution":"Case Western Reserve University","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Pieper","suffix":""},{"id":340577983,"identity":"25450888-ed7f-4c82-8720-eb6226d5707e","order_by":6,"name":"Xiongwei Zhu","email":"","orcid":"","institution":"Case Western Reserve University","correspondingAuthor":false,"prefix":"","firstName":"Xiongwei","middleName":"","lastName":"Zhu","suffix":""},{"id":340577984,"identity":"77a33353-a1e9-4f74-8045-03ecb50f68cb","order_by":7,"name":"James Leverenz","email":"","orcid":"","institution":"Cleveland Clinic","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Leverenz","suffix":""},{"id":340577985,"identity":"18e58c60-2d44-425d-8ad7-6e4d84357ea1","order_by":8,"name":"Fei Wang","email":"","orcid":"https://orcid.org/0000-0001-9459-9461","institution":"Weill Cornell Medicine","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Wang","suffix":""},{"id":340577986,"identity":"eb6d88d1-3713-4faa-acca-66035cafccea","order_by":9,"name":"Jeffrey Cummings","email":"","orcid":"","institution":"University of Nevada, Las Vegas","correspondingAuthor":false,"prefix":"","firstName":"Jeffrey","middleName":"","lastName":"Cummings","suffix":""}],"badges":[],"createdAt":"2024-08-06 13:55:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4869009/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4869009/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41531-025-00870-y","type":"published","date":"2025-01-22T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66561558,"identity":"aa44a215-ba17-41ee-ac80-a59d4cb6a706","added_by":"auto","created_at":"2024-10-14 10:18:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":155601,"visible":true,"origin":"","legend":"\u003cp\u003eA diagram of genome-wide xQTL-associated risk gene prediction and drug repurposing in PD, created with BioRender.com. It comprises four steps: (1) Deep learning (DL)-based pdRGs prediction by integrating GWAS findings and five types of xQTL findings, including expression QTLs (eQTLs), protein QTLs (pQTLs), splicing QTLs (sQTLs), methylation QTLs (meQTLs), and histone acetylation QTLs (haQTLs); (2) multi-omics validation of putative pdRGs through single nuclei transcriptome data, parkin-dependent substrates, alpha-synuclein (α-syn) modifiers, etc; (3) network proximity-based drug repurposing within human protein-protein interactome; and (4) drug validation via real-world EHR data.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4869009/v1/287fdcbf41ec18a383822801.jpg"},{"id":66563549,"identity":"a9b1ea09-a5f0-4c5b-b4cb-97141dd20dfb","added_by":"auto","created_at":"2024-10-14 10:26:54","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88202,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLandscape of PD GWAS loci regulated by five types of molecular xQTL data.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)\u0026nbsp;\u0026nbsp; Upset plot illustrating the intersection of gene sets regulated by different types of xQTLs. The bar plot on the left depicts the size of gene sets linked to each xQTL data, while the bar plot on the top shows the intersection size of gene sets regulated by multiple types of xQTLs. Well-known PD genes are labeled in bold.\u003c/p\u003e\n\u003cp\u003e(B)\u0026nbsp;\u0026nbsp; Landscape of GWAS loci (GRCh37, LD clumping r\u003csup\u003e2\u003c/sup\u003e_cutoff=0.1) for five types of xQTL-regulated genes, including eQTLs, meQTLs, haQTLs, pQTLs and sQTLs (i.e., 178 connection between 85 genes and 50 lead SNPs) from outside to inside (Methods and Materials, \u003cstrong\u003eTables S1\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4869009/v1/23394d3bb80da32f849de374.jpg"},{"id":66564130,"identity":"0c0c957e-0f8c-437c-9358-8e60d519367f","added_by":"auto","created_at":"2024-10-14 10:34:54","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":161103,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrediction of pdRGs based on the network-topology DL framework and validation using multi-omics data\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e(A) Model performance (i.e., AUCROC) for PD risk gene prediction. The dark red line represents the predicted score for our final model utilizing aggregated xQTL data. The other colored lines depict the respective model performance with single xQTL data.\u003c/p\u003e\n\u003cp\u003e(B) Circular plot showing eight types of evidence for identified 175 pdRGs, which are displayed clockwise from high to low according to the predicted Z score. Validations are illustrated from outside to inside, including (1) Drug targets; (2) Cell type-specific differentially expressed genes (DEGs, PD vs. HC); (3) DA neuron-specific genes when comparing DA neurons and non-DA cells in PD patients’ brains (4) Gene expression specificity in human substantia nigra (SN) region (Z\u0026gt;0); (5) PD/LBD-associated genes from GWAS catalog; (6) PARKIN-dependent substrate; (7) alpha-synuclein map; and (8) literatures. Expression profiles of pdRGs in SN are shown in the innermost circle colored by Z score from GTEX database (Z = 0 if Z \u0026lt; 0). The accumulated counts of evidence are plotted in the outermost circle.\u003c/p\u003e\n\u003cp\u003e(C) Locus zoom plot of the predicted pdRGs \u003cem\u003eGPNMB \u003c/em\u003e[97] colored by LD r2 (EnsDb.Hsapiens.v75, window_size=1Mb).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4869009/v1/9edc1fb271aef91381414e41.jpg"},{"id":66565975,"identity":"48b5f421-789b-4a27-9aa5-114163b68df2","added_by":"auto","created_at":"2024-10-14 10:42:54","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":106153,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-nuclei transcriptome validation of pdRGs within human SN region.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Uniform manifold approximation and projection (UMAP) embeddings of seven main cell types of 288,988 nuclei from GSE178265, including dopamine neurons (DA neurons), non-DA neurons, astrocytes (Astro), microglia (MG), oligodendrocyte precursor cells (OPC), oligodendrocytes (ODC), endothelial cells/pericytes/fibroblasts (Endo_Peri_Fib).\u003c/p\u003e\n\u003cp\u003e(B) Stacked bar plot showing the number of cell-type-specific DEGs (PD vs. HC).\u003c/p\u003e\n\u003cp\u003e(C) Upset plot depicting the transcriptome dysregulation of 50 identified pdRGs, along with fisher’s test results on the left (* p \u0026lt;0.05, ** p\u0026lt;0.01, *** p\u0026lt;0.001). The bar plot on the left showed the size of dysregulated pdRGs in different cell types, while the bar plot on the top shows the intersection size of dysregulated pdRG sets observed in multiple cell types. Well-known PD genes are labeled in bold.\u003c/p\u003e\n\u003cp\u003e(D) Transcriptional expression patterns of selected pdRGs in DA neurons (upper panel) and MG (lower panel).\u003c/p\u003e\n\u003cp\u003e(E) Venn plot showing the intersection of pdRGs and DA neuron-specific genes in PD patents.\u003c/p\u003e\n\u003cp\u003e(F) Dot plot of selected seven pdRGs with different expression patterns in DA neurons compared to non-DA nuclei.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4869009/v1/104da6d3a21d8bbecb92d545.jpg"},{"id":66561564,"identity":"e15c9907-70a4-4076-af22-df8b25c30426","added_by":"auto","created_at":"2024-10-14 10:18:54","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":72178,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVisualization of pdRGs-derived protein-protein interactome (PPI) network for PD\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e(A) The overall PD module covering 191 non-isolated PPI pairs (edges) within 103 pdRG-encoded proteins (nodes). All pdRGs with existing evidence are labeled by protein symbol without border (where orange nodes gave well-known PD risk genes), while the size illustrates the predicted score. Parts of nodes were colored by the protein family they belong to. Three well-known pdRGs formed a clique (\u003cem\u003eLRRK2\u003c/em\u003e-\u003cem\u003eSNCA\u003c/em\u003e-\u003cem\u003eMAPT\u003c/em\u003e, red edges in bold).\u003c/p\u003e\n\u003cp\u003e(B-C) Source of three types of external evidence for pdRGs, including parkin substrates and α-syn network (B) and cell-type specific DEGs (PD vs. HC).\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4869009/v1/4b5638244018f39e3bfd575e.jpg"},{"id":66564129,"identity":"e9a3d047-70be-43a8-b59b-1b4b4402d906","added_by":"auto","created_at":"2024-10-14 10:34:54","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":89171,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepurposable drug candidates for PD treatment based on network proximity method\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e(A) Sankey plot illustrating targets and pdRGs for prioritized twelve PD drugs (\u003cstrong\u003eTable S7\u003c/strong\u003e). The grey symbols in front of the drug name characterize the anatomical therapeutic chemical code (ATC).\u003c/p\u003e\n\u003cp\u003e(B-C) Mechanism of actions (MOAs) of two predicted drugs, including (A) riluzole and (B) azathioprine.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4869009/v1/65adfe267ec53c59ee837c03.jpg"},{"id":66563558,"identity":"56e2a7d7-127b-42b0-a140-9494eae24934","added_by":"auto","created_at":"2024-10-14 10:26:54","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":110132,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLongitudinal patient data validation for potential PD drug candidates\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e(A) Basic characteristics of INSIGHT database.\u003c/p\u003e\n\u003cp\u003e(B) Trial emulation for each drug with consideration of two kinds of PD outcomes, i.e., fall and dementia.\u003c/p\u003e\n\u003cp\u003e(C) Perform propensity score matching (PSM) to control confounding factors and build balanced trials.\u003c/p\u003e\n\u003cp\u003eMOAs of simvastatin, along with the forest plot of the hazard ratio (HR).\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4869009/v1/36d233db094e9e04dfede3d1.jpg"},{"id":74426217,"identity":"e1376770-8f9e-412d-89aa-5d2ce2b5161d","added_by":"auto","created_at":"2025-01-22 08:07:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1321755,"visible":true,"origin":"","legend":"","description":"","filename":"MainTextCheng.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4869009/v1_covered_1912d614-64b0-429b-b227-6da955fe65b1.pdf"},{"id":66561561,"identity":"57cc0cde-2506-435c-859b-c1d74946a1d8","added_by":"auto","created_at":"2024-10-14 10:18:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":791831,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Materials\u003c/p\u003e\n\u003cp\u003eSupplementary Figure Legends\u003c/p\u003e\n\u003cp\u003eFigure S1 Network-based pdRGs prediction by incorporating PD GWAS and xQTLs findings.\u003c/p\u003e\n\u003cp\u003e(A) Bar plot showing the number of mapped genome wide xQTLs for each type.\u003c/p\u003e\n\u003cp\u003e(B) Venn plot depicting the mapped 124 gene, where green, orange and blue represent the genes associated with lead SNPs, proxy SNPs and other SNPs, respectively.\u003c/p\u003e\n\u003cp\u003e(C) Disease enrichment analysis for the predicted pdRGs (DisGeNET database, q \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e(D) Gene expression patterns in brain SN region from GTEX database. All highly expressed genes in SN (Z \u0026gt;0) were used to perform Fisher’s test (i.e.,pdRGs compared with non-pdRGs).\u003c/p\u003e\n\u003cp\u003e(E) Dot plot of canonical marker genes for cell type annotation. The dot size encodes the proportion of cells that express the gene (Pct. Exp), while the color encodes the scaled average expression level across those cells (Avg. exp).\u003c/p\u003e\n\u003cp\u003eFigure S2 Demonstration of mechanism of actions (MOAs) for nine promising drug candidates for PD treatment, ordered by predicted score.\u003c/p\u003e","description":"","filename":"SupplementaryFigures12.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4869009/v1/201252ae4da849ab990e8fb5.pdf"},{"id":66563550,"identity":"cbacfcf4-a9b4-4c7a-9390-8f983de93113","added_by":"auto","created_at":"2024-10-14 10:26:54","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":232029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table Legends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S1 \u003c/strong\u003eMapped results of PD GWAS findings regulated by five types of\u003c/p\u003e\n\u003cp\u003ebrain-specific xQTLs.\u003c/p\u003e","description":"","filename":"SupplementaryTable1.zip","url":"https://assets-eu.researchsquare.com/files/rs-4869009/v1/d3d0dccb3e5c5025a6064bc7.zip"},{"id":66563560,"identity":"8b69c77e-8bb8-46e7-a5cf-085288973873","added_by":"auto","created_at":"2024-10-14 10:26:55","extension":"zip","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1177,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S2 \u003c/strong\u003eCollected 127 PD-associated genes from TIGA database for model\u003c/p\u003e\n\u003cp\u003eperformance evaluation.\u003c/p\u003e","description":"","filename":"SupplementaryTable2.zip","url":"https://assets-eu.researchsquare.com/files/rs-4869009/v1/37a6da45a13c7fed3f1730ea.zip"},{"id":66563552,"identity":"13f7e723-9bbd-4fba-98c6-db2b41c6d1ab","added_by":"auto","created_at":"2024-10-14 10:26:54","extension":"zip","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2969,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S3 \u003c/strong\u003eList of the predicted 175 putative pdRGs.\u003c/p\u003e","description":"","filename":"SupplementaryTable3.zip","url":"https://assets-eu.researchsquare.com/files/rs-4869009/v1/1177927625207470bb962ec6.zip"},{"id":66561559,"identity":"afba58a7-eea2-4cda-9b79-67ac298e47a0","added_by":"auto","created_at":"2024-10-14 10:18:54","extension":"zip","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":28018,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S4 \u003c/strong\u003eDisease enrichments of the predicted pdRGs based on DisGeNET database.\u003c/p\u003e","description":"","filename":"SupplementaryTable4.zip","url":"https://assets-eu.researchsquare.com/files/rs-4869009/v1/c30112fcb7ff002c5b80c4fc.zip"},{"id":66561574,"identity":"94c31b0d-0929-4e53-a28b-4e740a619498","added_by":"auto","created_at":"2024-10-14 10:18:56","extension":"zip","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":5603,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S5 \u003c/strong\u003eSummary of multi-omics validation for the inferred pdRGs.\u003c/p\u003e","description":"","filename":"SupplementaryTable5.zip","url":"https://assets-eu.researchsquare.com/files/rs-4869009/v1/6c49b395d8f4e209db232817.zip"},{"id":66564132,"identity":"9e06b43d-cc8e-4e4c-b0c9-09b8d4e2285f","added_by":"auto","created_at":"2024-10-14 10:34:54","extension":"zip","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":857842,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S6 \u003c/strong\u003eTranscriptome dysregulation of pdRGs at single-nuclei resolution through snRNA-seq dataset GSE178265.\u003c/p\u003e","description":"","filename":"SupplementaryTable6.zip","url":"https://assets-eu.researchsquare.com/files/rs-4869009/v1/86f80c05812dfa02894e7574.zip"},{"id":66563553,"identity":"b534a92c-fc50-452d-96dd-0fc8c5225697","added_by":"auto","created_at":"2024-10-14 10:26:54","extension":"zip","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":10729,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S7 \u003c/strong\u003ePrioritized PD drug candidates based on network proximity method (Z \u0026lt; -3).\u003c/p\u003e","description":"","filename":"SupplementaryTable7.zip","url":"https://assets-eu.researchsquare.com/files/rs-4869009/v1/2fd010b00333fa735ae19cfc.zip"},{"id":66561573,"identity":"e04b0eee-a1de-4525-81e8-a91bbb3bd4f1","added_by":"auto","created_at":"2024-10-14 10:18:54","extension":"zip","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":16621,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S8 \u003c/strong\u003eDiagnosis code for PD and ND (fall or dementia).\u003c/p\u003e","description":"","filename":"SupplementaryTable8.zip","url":"https://assets-eu.researchsquare.com/files/rs-4869009/v1/850281332f6f009c8aeb03fe.zip"},{"id":66561572,"identity":"3986985d-9a00-409f-b216-4a6e97d88c1a","added_by":"auto","created_at":"2024-10-14 10:18:54","extension":"zip","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":109440,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S9 \u003c/strong\u003eBaseline comorbidity ICD codes.\u003c/p\u003e","description":"","filename":"SupplementaryTable9.zip","url":"https://assets-eu.researchsquare.com/files/rs-4869009/v1/4378f2d1301b913745156adc.zip"},{"id":66563554,"identity":"5fc2dcdd-f463-4617-acea-2c426eb179a3","added_by":"auto","created_at":"2024-10-14 10:26:54","extension":"zip","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":7712,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S10 \u003c/strong\u003eEHR validation results of simvastatin with consideration of two types of PD outcomes (fall and dementia).\u003c/p\u003e","description":"","filename":"SupplementaryTable10.zip","url":"https://assets-eu.researchsquare.com/files/rs-4869009/v1/cf16da742cc0dff88c92c41c.zip"}],"financialInterests":"There is a conflict of interest\nDr. Cummings has provided consultation to Acadia, Acumen, ALZpath, Aprinoia, Artery, Biogen, Biohaven, BioXcel, Bristol-Myers Squib, Eisai, Fosun, GAP Foundation, Janssen, Karuna, Kinoxis, Lighthouse, Lilly, Lundbeck, LSP/eqt, Merck, MoCA Cognition, New Amsterdam, Novo Nordisk, Optoceutics, Otsuka, Oxford Brain Diagnostics, Praxis, Prothena, ReMYND, Roche, Scottish Brain Sciences, Signant Health, Simcere, sinaptica, TrueBinding, and Vaxxinity pharmaceutical, assessment, and investment companies. JC owns the copyright of the Neuropsychiatric Inventory. JC has stocks/options in Artery, Vaxxinity, Behrens, Alzheon, MedAvante-Prophase, Acumen. Dr. Leverenz has received consulting fees from consulting fees from Vaxxinity, grant support from GE Healthcare and serves on a Data Safety Monitoring Board for Eisai.","formattedTitle":"A network-based systems genetics framework identifies pathobiology and drug repurposing in Parkinson’s disease","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-parkinsons-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjparkd","sideBox":"Learn more about [npj Parkinson's Disease](http://www.nature.com/npjparkd/)","snPcode":"41531","submissionUrl":"https://submission.springernature.com/new-submission/41531/3","title":"npj Parkinson's Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Drug repurposing, Electronic Health Record (EHR), Genome-Wide Association Studies (GWAS), Parkinson’s disease (PD), Quantitative trait loci (QTL), Protein-protein interactions (PPI), Risk genes","lastPublishedDoi":"10.21203/rs.3.rs-4869009/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4869009/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParkinson’s disease (PD) is the second most prevalent neurodegenerative disorder. However, current treatments are directed at symptoms and lack ability to slow or prevent disease progression. Large-scale genome-wide association studies (GWAS) have identified numerous genomic loci associated with PD, which may guide the development of disease-modifying treatments. We presented a systems genetics approach to identify potential risk genes and repurposable drugs for PD. First, we leveraged non-coding GWAS loci effects on multiple human brain-specific quantitative trait loci (xQTLs) under the protein-protein interactome (PPI) network. We then prioritized a set of PD likely risk genes (pdRGs) by integrating five types of molecular xQTLs: expression (eQTLs), protein (pQTLs), splicing (sQTLs), methylation (meQTLs), and histone acetylation (haQTLs). We also integrated network proximity-based drug repurposing and patient electronic health record (EHR) data observations to propose potential drug candidates for PD treatments. We identified 175 pdRGs from QTL-regulated GWAS findings, such as \u003cem\u003eSNCA\u003c/em\u003e, \u003cem\u003eCTSB\u003c/em\u003e, \u003cem\u003eLRRK2, DGKQ\u003c/em\u003e, \u003cem\u003eCD38 \u003c/em\u003eand \u003cem\u003eCD44\u003c/em\u003e. Multi-omics data validation revealed that the identified pdRGs are likely to be druggable targets, differentially expressed in multiple cell types and impact both the parkin ubiquitin-proteasome and alpha-synuclein (a-syn) pathways. Based on the network proximity-based drug repurposing followed by EHR data validation, we identified usage of simvastatin as being significantly associated with reduced incidence of PD (fall outcome: hazard ratio (HR) = 0.91, 95% confidence interval (CI): 0.87–0.94; for dementia outcome: HR = 0.88, 95% CI: 0.86-0.89), after adjusting for 267 covariates. Our network-based systems genetics framework identifies potential risk genes and repurposable drugs for PD and other neurodegenerative diseases if broadly applied.\u003c/p\u003e","manuscriptTitle":"A network-based systems genetics framework identifies pathobiology and drug repurposing in Parkinson’s disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-14 10:18:49","doi":"10.21203/rs.3.rs-4869009/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-09-16T01:08:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-09-11T18:04:03+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-08-29T13:45:26+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-08-19T09:06:29+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-08-19T07:09:12+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-08-15T11:41:11+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-08-15T09:57:16+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-08-15T07:15:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-07T15:27:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-07T07:31:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Parkinson's Disease","date":"2024-08-06T13:51:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-parkinsons-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjparkd","sideBox":"Learn more about [npj Parkinson's Disease](http://www.nature.com/npjparkd/)","snPcode":"41531","submissionUrl":"https://submission.springernature.com/new-submission/41531/3","title":"npj Parkinson's Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ad3f5c77-38bb-4bc9-b568-2566c61f1bf3","owner":[],"postedDate":"October 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36083416,"name":"Health sciences/Neurology/Neurological disorders/Parkinson's disease"},{"id":36083417,"name":"Biological sciences/Computational biology and bioinformatics/Gene regulatory networks"}],"tags":[],"updatedAt":"2025-01-22T08:07:36+00:00","versionOfRecord":{"articleIdentity":"rs-4869009","link":"https://doi.org/10.1038/s41531-025-00870-y","journal":{"identity":"npj-parkinsons-disease","isVorOnly":false,"title":"npj Parkinson's Disease"},"publishedOn":"2025-01-22 05:00:00","publishedOnDateReadable":"January 22nd, 2025"},"versionCreatedAt":"2024-10-14 10:18:49","video":"","vorDoi":"10.1038/s41531-025-00870-y","vorDoiUrl":"https://doi.org/10.1038/s41531-025-00870-y","workflowStages":[]},"version":"v1","identity":"rs-4869009","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4869009","identity":"rs-4869009","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}