Rough Sets for Phenotype-Based Prioritization of Causative Variants

Rough Sets for Phenotype-Based Prioritization of Causative Variants | 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 Rough Sets for Phenotype-Based Prioritization of Causative Variants Jyoti Sharma, Khadija Sana Hafeez, Third Sushmita Paul This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4022077/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 Genome-wide association studies (GWAS) are essential for understanding the genetic basis of complex traits by identifying single nucleotide polymorphisms (SNPs) associated with phenotypes of interest. GWAS employ statistical methods to identify SNPs associated with phenotypes above a predetermined significance threshold. However, this threshold approach may inadvertently exclude highly significant SNPs, posing a potential limitation. GWAS datasets contain many SNPs, which can lead to ambiguity in association results. To resolve these discrepancies, several feature selection (FS) methods have been implemented prior to association tests. However, these FS methods do not effectively illustrate significant biological relevance of the resulting SNPs. Our work introduces a pipeline that combines a feature selection strategy based on the Rough Set theory with an association test using a machine learning approach. This innovative approach is applied to identify SNPs associated with blood cholesterol levels, focusing on low-density and high-density lipoprotein (LDL and HDL) cholesterol. The efficiency of the pipeline is evaluated using a cohort dataset from the American population to showcase the comparative efficacy of the pipeline. Our pipeline demonstrates excellent performance on datasets with low sample sizes, outperforming existing PLINK approach. Moreover, to enhance the biological relevance of selected SNPs, we extend our investigation to closely related SNPs, followed by rigorous enrichment studies annotating genes, biological processes, and pathways. This comprehensive exploration unveils the intricate cellular mechanisms and genetic determinants influencing LDL and HDL cholesterol levels. Our findings not only contribute valuable insights to the understanding of these traits but also suggest potential personalised treatment modalities. Genome-wide Association Studies Feature Selection Rough-set Support Vector Regression Complex Trait Full Text Additional Declarations No competing interests reported. Supplementary Files SupplimentaryRBFS.pdf 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-4022077","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":277223127,"identity":"9c1cbbe5-405a-45e6-9ba5-e5f845614276","order_by":0,"name":"Jyoti Sharma","email":"","orcid":"","institution":"Indian Institute of Technology Jodhpur","correspondingAuthor":false,"prefix":"","firstName":"Jyoti","middleName":"","lastName":"Sharma","suffix":""},{"id":277223128,"identity":"4ec9dff1-2a05-4bf4-b69a-8ba8daed59e1","order_by":1,"name":"Khadija Sana Hafeez","email":"","orcid":"","institution":"Unit of Genomic, Department of Medical Sciences, University of Turin","correspondingAuthor":false,"prefix":"","firstName":"Khadija","middleName":"Sana","lastName":"Hafeez","suffix":""},{"id":277223129,"identity":"b8401454-fe5c-42c6-902a-03b8ae7cfc35","order_by":2,"name":"Third Sushmita Paul","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYHACNgYGAzCD8UCCwX85EOvAA8JaIHoOfKhgNgYzEghqgVpzcMYZ5sQGEAufFvkZ6c8e3Sj4I2/OfsbgMG8bW/r8sMMPgbbYyek2YNdicCPH3DjHwMBwZ08OSAtP7sbbaQZALcnGZgdwaJHIYZMGamHccACsRSJ34+wEkJYDidtwaAE5DKTFfsP5NyAtBumGs9M/4NXCcCPBDKQlccONHAOg9xMS5IFcvFoMzrwBaTFO3jnjWQEwkA8YbpDOKQBGEG6/yLeDHPZHznY7f/LGB0BfyMvPTt/84UOFnRwuLQjr4IwDKFxitMg3EKF6FIyCUTAKRhQAAFm7ZmJH6UnEAAAAAElFTkSuQmCC","orcid":"","institution":"Indian Institute of Technology Jodhpur","correspondingAuthor":true,"prefix":"","firstName":"Third","middleName":"Sushmita","lastName":"Paul","suffix":""}],"badges":[],"createdAt":"2024-03-06 19:31:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4022077/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4022077/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52446967,"identity":"d4a030f8-2ccf-4378-b017-87030db80193","added_by":"auto","created_at":"2024-03-11 18:22:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9530553,"visible":true,"origin":"","legend":"","description":"","filename":"RBFSJournalofCommunityGenetics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4022077/v1_covered_e920bca0-fa1f-4ede-bd3c-fe9fb664f6d7.pdf"},{"id":52445170,"identity":"ab4691d5-e8d1-47c8-b258-7b45f0eb40d3","added_by":"auto","created_at":"2024-03-11 17:57:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1111717,"visible":true,"origin":"","legend":"","description":"","filename":"SupplimentaryRBFS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4022077/v1/5a4b977b966f9423c5faa381.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rough Sets for Phenotype-Based Prioritization of Causative Variants","fulltext":[],"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":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":"Genome-wide Association Studies, Feature Selection, Rough-set, Support Vector Regression, Complex Trait","lastPublishedDoi":"10.21203/rs.3.rs-4022077/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4022077/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGenome-wide association studies (GWAS) are essential for understanding the genetic basis of complex traits by identifying single nucleotide polymorphisms (SNPs) associated with phenotypes of interest. GWAS employ statistical methods to identify SNPs associated with phenotypes above a predetermined significance threshold. However, this threshold approach may inadvertently exclude highly significant SNPs, posing a potential limitation. GWAS datasets contain many SNPs, which can lead to ambiguity in association results. To resolve these discrepancies, several feature selection (FS) methods have been implemented prior to association tests. However, these FS methods do not effectively illustrate significant biological relevance of the resulting SNPs.\u003c/p\u003e\n\u003cp\u003eOur work introduces a pipeline that combines a feature selection strategy based on the Rough Set theory with an association test using a machine learning approach. This innovative approach is applied to identify SNPs associated with blood cholesterol levels, focusing on low-density and high-density lipoprotein (LDL and HDL) cholesterol. 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