Integration of GWAS-Derived Polygenic Risk Scores with Single-Cell RNA Sequencing to Identify Cell-Type–Specific Genetic Risk in Type 2 Diabetes.

Integration of GWAS-Derived Polygenic Risk Scores with Single-Cell RNA Sequencing to Identify Cell-Type–Specific Genetic Risk in Type 2 Diabetes. | 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 Research Article Integration of GWAS-Derived Polygenic Risk Scores with Single-Cell RNA Sequencing to Identify Cell-Type–Specific Genetic Risk in Type 2 Diabetes. Rupanjali Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9396739/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Type 2 Diabetes (T2D) is a complicated disease, influenced by both genetic factors and cellular dysfunction inside the pancreatic tissue. Over the years, researchers have identified many genetic variants associated with T2D risk through Genome Wide Association Studies (GWAS), but it’s still tough to determine exactly how these variants affect different cell types. This study introduces a framework that integrates GWAS derived Polygenic Risk Scores (PRS), single-cell RNA sequencing (scRNA-seq) data, and graph based machine learning to explore how genetic risk unfolds at the cell type level in T2D. We started by filtering GWAS summary statistics, which cover about 29.7 million variants, to focus on high confidence variants that map to chromosome 22 variants subsequently mapped to specific genes [ 1 ]. With single-cell RNA sequencing, we identified several types of pancreatic cells: alpha, beta, delta, acinar, ductal, fibroblast, endothelial, macrophage, and perivascular cells [ 2 ]. When we looked at which genes were active in these cells, we found a lot of variation, especially in alpha cells, macrophages, and fibroblasts, which had the most genes showing large differences [ 3 ]. By connecting GWAS genes with single-cell gene expression data, we identified 161 genes that overlapped between the two[ 4 ]. We then calculated PRS scores for each gene and combined them with gene expression results to identify which cell types carried greater genetic risk. Interestingly, immune and stromal cells, especially macrophages and fibroblasts, showed higher risk scores than the traditional focus on beta cells. To dig deeper, we used a Graph Neural Network to examine protein interactions, which highlighted key genes in the network, including SOX10, SHANK3, NCF4, and OSM[ 5 ]. When we checked which biological functions were most involved, immune responses and cytokine signalling pathways came up over and over again[ 6 ]. Altogether, by merging genetic data, single-cell RNA profiles, and network based machine learning, we get a clearer picture of how T2D unfolds at the cellular level. This approach also reveals that immune and stromal cells might play a bigger part in the disease than previously thought. Systems Biology Computational Biology Bioinformatics Epigenetics & Genomics Type 2 Diabetes Mellitus Genome-Wide Association Studies (GWAS) Polygenic Risk Score (PRS) Single-cell RNA sequencing (scRNA-seq) Graph Neural Network Gene–environment interaction Cellular heterogeneity Pancreatic cell types Multi-omics integration Machine learning Protein–protein interaction network Immune response Cytokine signaling Full Text Additional Declarations The authors declare no competing interests. Supplementary Files celltypeprsscores.csv degfiltered.csv deggwasoverlapgenes.csv genecoordinates.tsv geneembeddings.csv geneinteractiongraph.gml genelevelprs.csv generiskscores.csv gnngeneanalysis.csv gnngeneprs.csv gwaschr1.tsv gwaschr2.tsv gwaschr3.tsv gwaschr4.tsv gwaschr5.tsv gwaschr6.tsv gwaschr7.tsv gwaschr8.tsv gwaschr9.tsv gwaschr10.tsv gwaschr11.tsv gwaschr12.tsv gwaschr13.tsv gwaschr14.tsv gwaschr15.tsv gwaschr16.tsv gwaschr17.tsv gwaschr18.tsv gwaschr19.tsv gwaschr20.tsv gwaschr21.tsv gwaschr22geneannotated.tsv gwaschr22.tsv simulatedclinicaldataset.csv stringedges.csv topcelltypest2d.csv topgenest2d.csv geneembeddings.pt prsgnnmodel.pt T2Dprioritizedtargets.tsv Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9396739","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621803475,"identity":"ff81cfd5-8bfa-4da6-941b-bbc1ab939f8b","order_by":0,"name":"Rupanjali 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09:33:36","extension":"tsv","order_by":40,"title":"","display":"","copyAsset":false,"role":"supplement","size":2328,"visible":true,"origin":"","legend":"","description":"","filename":"T2Dprioritizedtargets.tsv","url":"https://assets-eu.researchsquare.com/files/rs-9396739/v1/f1761fd4e34b22110fd14b6d.tsv"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIntegration of GWAS-Derived Polygenic Risk Scores with Single-Cell RNA Sequencing to Identify Cell-Type–Specific Genetic Risk in Type 2 Diabetes.\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Type 2 Diabetes Mellitus, Genome-Wide Association Studies (GWAS), Polygenic Risk Score (PRS), Single-cell RNA sequencing (scRNA-seq), Graph Neural Network, Gene–environment interaction, Cellular heterogeneity, Pancreatic cell types, Multi-omics integration, Machine learning, Protein–protein interaction network, Immune response, Cytokine signaling","lastPublishedDoi":"10.21203/rs.3.rs-9396739/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9396739/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eType 2 Diabetes (T2D) is a complicated disease, influenced by both genetic factors and cellular dysfunction inside the pancreatic tissue. Over the years, researchers have identified many genetic variants associated with T2D risk through Genome Wide Association Studies (GWAS), but it\u0026rsquo;s still tough to determine exactly how these variants affect different cell types. This study introduces a framework that integrates GWAS derived Polygenic Risk Scores (PRS), single-cell RNA sequencing (scRNA-seq) data, and graph based machine learning to explore how genetic risk unfolds at the cell type level in T2D. We started by filtering GWAS summary statistics, which cover about 29.7\u0026nbsp;million variants, to focus on high confidence variants that map to chromosome 22 variants subsequently mapped to specific genes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. With single-cell RNA sequencing, we identified several types of pancreatic cells: alpha, beta, delta, acinar, ductal, fibroblast, endothelial, macrophage, and perivascular cells [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. When we looked at which genes were active in these cells, we found a lot of variation, especially in alpha cells, macrophages, and fibroblasts, which had the most genes showing large differences [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBy connecting GWAS genes with single-cell gene expression data, we identified 161 genes that overlapped between the two[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. We then calculated PRS scores for each gene and combined them with gene expression results to identify which cell types carried greater genetic risk. Interestingly, immune and stromal cells, especially macrophages and fibroblasts, showed higher risk scores than the traditional focus on beta cells. To dig deeper, we used a Graph Neural Network to examine protein interactions, which highlighted key genes in the network, including SOX10, SHANK3, NCF4, and OSM[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. When we checked which biological functions were most involved, immune responses and cytokine signalling pathways came up over and over again[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAltogether, by merging genetic data, single-cell RNA profiles, and network based machine learning, we get a clearer picture of how T2D unfolds at the cellular level. This approach also reveals that immune and stromal cells might play a bigger part in the disease than previously thought.\u003c/p\u003e","manuscriptTitle":"Integration of GWAS-Derived Polygenic Risk Scores with Single-Cell RNA Sequencing to Identify Cell-Type–Specific Genetic Risk in Type 2 Diabetes.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 07:47:54","doi":"10.21203/rs.3.rs-9396739/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6602574a-959a-46b2-90ca-f555b390442b","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66167807,"name":"Systems Biology"},{"id":66167808,"name":"Computational Biology"},{"id":66167809,"name":"Bioinformatics"},{"id":66167810,"name":"Epigenetics \u0026 Genomics"}],"tags":[],"updatedAt":"2026-04-14T07:47:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 07:47:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9396739","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9396739","identity":"rs-9396739","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}