Genetic Fine-Mapping With Dense Linkage Disequilibrium Blocks

Genetic Fine-Mapping With Dense Linkage Disequilibrium Blocks | 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 Genetic Fine-Mapping With Dense Linkage Disequilibrium Blocks Chen Mo, Zhenyao Ye, Kathryn Hatch, Yuan Zhang, Qiong Wu, Song Liu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-649530/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 Background: Fine-mapping is an analytical step for causal prioritization of the polymorphic variants in a trait-associated genomic region observed in genome-wide association studies (GWAS). Prioritization of causal variants can be challenging due to linkage disequilibrium (LD) patterns among hundreds to thousands of polymorphisms associated with a trait. Hence, we propose an ℓ0 graph norm shrinkage algorithm to disentangle LD patterns by dense LD blocks consisting of highly correlated single nucleotide polymorphisms (SNPs). We further incorporate the dense LD structure for fine-mapping. Based on graph theory, the concept of "dense" refers to a condition where a block is composed mainly of highly correlated SNPs. We demonstrated the application of our new fine-mapping method using a large UK Biobank (UKBB) sample related to nicotine addiction. We also evaluated and compared its performance with existing fine-mapping algorithms using simulations. Results: Our results suggested that polymorphic variances in both neighboring and distant variants can be consolidated into dense blocks of highly correlated loci. Dense-LD outperformed comparable fine-mapping methods with increased sensitivity and reduced false-positive error rate for causal variant selection. Applying to a UKBB sample, this method replicated the loci reported in previous findings and suggested a strong association with nicotine addiction. Conclusion: We found that the dense LD block structure can guide fine-mapping and accurately determine a parsimonious set of potential causal variants. Our approach is computationally efficient and allows fine-mapping of thousands of polymorphisms. Bioinformatics ℓ0 graph norm shrinkage fine-mapping GWAS linkage disequilibrium nicotine addiction regression shrinkage Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Full Text Additional Declarations No competing interests reported. Supplementary Files AdditionalFile1.docx Additional file 1 AdditionalFile2.pdf Additional file 2 AdditionalFile3.xlsx Additional file 3 AdditionalFile4.xlsx Additional file 4 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-649530","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":47380590,"identity":"f09ba7ad-233c-4a40-b3e1-58b50e5c3783","order_by":0,"name":"Chen Mo","email":"","orcid":"","institution":"University of Maryland School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Mo","suffix":""},{"id":47380591,"identity":"84c0950d-8274-439e-a163-fed2a20a7dae","order_by":1,"name":"Zhenyao Ye","email":"","orcid":"","institution":"University of Maryland School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhenyao","middleName":"","lastName":"Ye","suffix":""},{"id":47380592,"identity":"9381ba40-0047-4a00-8168-4e1ae3b6ac16","order_by":2,"name":"Kathryn Hatch","email":"","orcid":"","institution":"University of Maryland School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kathryn","middleName":"","lastName":"Hatch","suffix":""},{"id":47380593,"identity":"0c3586eb-8886-4e90-b26d-8143dc210c97","order_by":3,"name":"Yuan Zhang","email":"","orcid":"","institution":"The Ohio State University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Zhang","suffix":""},{"id":47380594,"identity":"ea520bbf-4de5-4cc4-9917-c47fa002c9f7","order_by":4,"name":"Qiong Wu","email":"","orcid":"","institution":"University of Maryland, College Park","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qiong","middleName":"","lastName":"Wu","suffix":""},{"id":47380595,"identity":"66f6082b-ae9c-4b0a-bb0b-636f5197788a","order_by":5,"name":"Song Liu","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"Liu","suffix":""},{"id":47380596,"identity":"0cf4d81e-82be-4710-9f00-b55e42c13932","order_by":6,"name":"Qing Lu","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Lu","suffix":""},{"id":47380597,"identity":"a77b2b98-b5d3-4b91-9245-409ddb2ffcdd","order_by":7,"name":"Braxton Mitchell","email":"","orcid":"","institution":"University of Maryland School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Braxton","middleName":"","lastName":"Mitchell","suffix":""},{"id":47380599,"identity":"6a882d0d-3d30-4caa-b560-b776df88898c","order_by":8,"name":"L. Elliot Hong","email":"","orcid":"","institution":"University of Maryland School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"L.","middleName":"Elliot","lastName":"Hong","suffix":""},{"id":47380601,"identity":"60ffc050-64b6-4bb1-a29f-ffa77790e65c","order_by":9,"name":"Peter Kochunov","email":"","orcid":"","institution":"University of Maryland School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Kochunov","suffix":""},{"id":47380603,"identity":"14c79123-6606-4aa8-a30f-52eec5ae33a7","order_by":10,"name":"Tianzhou Ma","email":"","orcid":"","institution":"University of Maryland, College Park","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tianzhou","middleName":"","lastName":"Ma","suffix":""},{"id":47380605,"identity":"abcff345-f8a4-4794-ae65-20462150362c","order_by":11,"name":"Shuo Chen","email":"data:image/png;base64,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","orcid":"","institution":"University of Maryland School of Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shuo","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2021-06-23 03:59:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-649530/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-649530/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":12730952,"identity":"b9be3f2e-2e48-4ad3-ba77-a9a4d10e6f97","added_by":"auto","created_at":"2021-08-24 20:44:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2839691,"visible":true,"origin":"","legend":"LD decay (in Kb) plot. A) displays the pairwise correlations (r2) of all SNPs pairs in the selected region from CPD data. The zoom plot in B) shows the relationship between r2 and the physical distance of pairs of SNPs within PD = 100 Kb and shows a non-monotonic and nonlinear pattern.","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-649530/v1/c8e233337a405aec6528bf03.png"},{"id":12730850,"identity":"dadecaad-1f39-4623-b164-2cf3299a4481","added_by":"auto","created_at":"2021-08-24 20:41:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6410448,"visible":true,"origin":"","legend":"Manhattan plots and heatmaps before and after LD pattern detection via Dense-LD. (A) shows the raw heatmap of the genomic region. (B) shows the heatmaps of the haplotype block (left) and the dense block (right) learned from (A). (C) shows the Manhattan plots with SNPs ordered according to haplotype block (left) and dense block (right). The vertical lines in the Manhattan plots indicate the block boundaries. The plots on the left represent the SNPs in their natural physical position. On the contrary, the plots on the right reorder the SNPs based on rankings based on LD pattern detection via graph norm shrinkage. SNPs are colored along with their block ID. The blue rectangle highlights an example of SNPs with distinct levels of trait associations, having moderate correlations (r2) ranging from 0.5 to 0.6, which are assigned to two different blocks. (D) shows the histograms of r2 inside (red) and outside (blue) blocks detected by different LD block detection methods. The results of haplotype blocks detected by ldetect algorithm is provided in Additional file 1 Figure S3)","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-649530/v1/6f48f1b82b093c058ae54708.png"},{"id":12730992,"identity":"0a196924-49ad-4a24-8f76-cee247900d82","added_by":"auto","created_at":"2021-08-24 20:47:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":609688,"visible":true,"origin":"","legend":"Overview of Dense-LD procedure. A genomic region selected based on\ngenome-wide analysis results is passed to Dense-LD fine-mapping following two steps: i) detection of the LD structure of SNPs, and ii) selection of SNPs based on the SNPs data and the LD structure.","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-649530/v1/8ffc5933b6119874e12cd432.png"},{"id":12730956,"identity":"929891d6-acd9-4642-98ab-62460751df76","added_by":"auto","created_at":"2021-08-24 20:44:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2314397,"visible":true,"origin":"","legend":"Results of Dense-LD for CPD. (A) and (B) are the Manhattan plot and heatmap for SNPs ordered according to Dense-LD, respectively. The Manhattan plot shows the non-selected SNPs in grey and highlights the selected SNPs in other colors. The SNPs are colored according to the genes they are located in. The horizontal dashed line (at − log10 (p-value) = 8) corresponds to the commonly used genome-wide significance level (p-value = 5 × 10−8). The block ID of selected SNPs is shown on top of the plot. The volcano plot (C) shows that the SNPs in the 18th block gathered relatively close to each other, indicating similar trait associations within the same block in terms of p-values and effect sizes.","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-649530/v1/458587c616646bceed16645c.png"},{"id":12730852,"identity":"2315d7ad-71cc-42ad-a885-5a6bd9b45908","added_by":"auto","created_at":"2021-08-24 20:41:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":399004,"visible":true,"origin":"","legend":"Comparison of different methods in the simulation. The x-axis is the number of selected SNPs, and the y-axis is the proportion of causal variants included across the simulations.","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-649530/v1/a1be6cc4523dc28f13d2e61e.png"},{"id":17800717,"identity":"703632c2-686e-4bd0-b91b-1a57022d90d5","added_by":"auto","created_at":"2022-01-31 11:14:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1265566,"visible":true,"origin":"","legend":"","description":"","filename":"DenseLDmainv3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-649530/v1_covered.pdf"},{"id":13671338,"identity":"bec619b6-a2e5-4fb9-b222-b6713baa1e24","added_by":"auto","created_at":"2021-09-17 11:08:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1785790,"visible":true,"origin":"","legend":"","description":"","filename":"DenseLDmainv3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-649530/v1_covered.pdf"},{"id":12731004,"identity":"90b90346-6a6c-44b2-8c3a-504a8aa22c1e","added_by":"auto","created_at":"2021-08-24 20:50:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1782185,"visible":true,"origin":"","legend":"","description":"","filename":"DenseLDmainv3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-649530/v1_covered.pdf"},{"id":12730955,"identity":"df3d526a-b061-4ce8-b61b-c1f68a2d0c9b","added_by":"auto","created_at":"2021-08-24 20:44:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":658560,"visible":true,"origin":"","legend":"Additional file 1","description":"","filename":"AdditionalFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-649530/v1/e68600730068a30ea0800bb2.docx"},{"id":12730848,"identity":"3cc985f3-e8de-4f69-a802-980473f21260","added_by":"auto","created_at":"2021-08-24 20:41:20","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1759155,"visible":true,"origin":"","legend":"Additional file 2","description":"","filename":"AdditionalFile2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-649530/v1/b0fa126f471676031daf1ece.pdf"},{"id":12730844,"identity":"72c2c3c0-56ed-488b-af4b-d9c93189a833","added_by":"auto","created_at":"2021-08-24 20:41:20","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":43473,"visible":true,"origin":"","legend":"Additional file 3","description":"","filename":"AdditionalFile3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-649530/v1/bd3c8ae0921e432f361e6180.xlsx"},{"id":12730996,"identity":"dd31fbef-d02b-4177-8e56-d4053b3ab128","added_by":"auto","created_at":"2021-08-24 20:50:20","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":30009,"visible":true,"origin":"","legend":"Additional file 4","description":"","filename":"AdditionalFile4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-649530/v1/ae8b319a6da5f86ef6fe23a7.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eGenetic Fine-Mapping With Dense Linkage Disequilibrium Blocks\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-649530/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"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":"ℓ0 graph norm shrinkage, fine-mapping, GWAS, linkage disequilibrium, nicotine addiction, regression shrinkage","lastPublishedDoi":"10.21203/rs.3.rs-649530/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-649530/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Fine-mapping is an analytical step for causal prioritization of the polymorphic variants in a trait-associated genomic region observed in genome-wide association studies (GWAS). Prioritization of causal variants can be challenging due to linkage disequilibrium (LD) patterns among hundreds to thousands of polymorphisms associated with a trait. Hence, we propose an ℓ0 graph norm shrinkage algorithm to disentangle LD patterns by \u003cem\u003edense\u003c/em\u003e LD blocks consisting of highly correlated single nucleotide polymorphisms (SNPs). We further incorporate the dense LD structure for fine-mapping. Based on graph theory, the concept of \u003cstrong\u003e\"dense\"\u003c/strong\u003e refers to a condition where a block is composed mainly of highly correlated SNPs. We demonstrated the application of our new fine-mapping method using a large UK Biobank (UKBB) sample related to nicotine addiction. We also evaluated and compared its performance with existing fine-mapping algorithms using simulations.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOur results suggested that polymorphic variances in both neighboring and distant variants can be consolidated into dense blocks of highly correlated loci. Dense-LD outperformed comparable fine-mapping methods with increased sensitivity and reduced false-positive error rate for causal variant selection. Applying to a UKBB sample, this method replicated the loci reported in previous findings and suggested a strong association with nicotine addiction.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eWe found that the dense LD block structure can guide fine-mapping and accurately determine a parsimonious set of potential causal variants. Our approach is computationally efficient and allows fine-mapping of thousands of polymorphisms.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Genetic Fine-Mapping With Dense Linkage Disequilibrium Blocks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-08-24 20:41:17","doi":"10.21203/rs.3.rs-649530/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":"2f26e8f4-9f11-4326-8521-eaad9ed8f9dc","owner":[],"postedDate":"August 24th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":6673232,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2022-01-31T11:14:20+00:00","versionOfRecord":[],"versionCreatedAt":"2021-08-24 20:41:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-649530","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-649530","identity":"rs-649530","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}