The trait coding rule in phenotype space

The trait coding rule in phenotype space | 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Download PDF Article The trait coding rule in phenotype space Jianguo Wang, Xionglei He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1297947/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 Is it possible to infer the genetically determined value of a quantitative trait from other traits? To answer the question we need to know how traits are coded in phenotype space (P) which can be partitioned into a subspace determined by genetic factors (PG) and a subspace affected by non-genetic factors (PNG). Evolutionary theory predicts PG is composed of limited dimensions while PNG may have infinite dimensions, which suggests a novel dimension decomposition method to separate them. After validating the method using simulation we applied it to a yeast phenotype space comprising 405 traits. The obtained yeast PG matches the actual genetic components of the yeast traits, explains the broad-sense heritability, and facilitates the mapping of quantitative trait loci, highlighting the success of the subspace separation. A limited number of latent dimensions in the PG were found to be recurrently used for coding the diverse yeast traits, while dimensions in the PNG tend to be trait specific and increase constantly with trait sampling. Similar results were obtained by analyzing the UK Biobank human brain phenome, which elucidated the genetic versus non-genetic origins of the left-right asymmetry of brain. In sum, phenotypic traits are coded by a rather small set of genetically determined basic dimensions and numerous trait-specific dimensions that are shaped by non-genetic factors, a rule enabling the identification of genetic components of quantitative traits based solely on phenotype. Full Text Additional Declarations There is NO Competing Interest. Supplementary Files rs.pdf Reporting Summary 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. 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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-1297947","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":84121043,"identity":"835ae814-03fa-4ed4-a0c7-d5f166f1e2aa","order_by":0,"name":"Jianguo Wang","email":"","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jianguo","middleName":"","lastName":"Wang","suffix":""},{"id":84121044,"identity":"4383f5f9-de39-4e3b-9fd4-7d3892cd1368","order_by":1,"name":"Xionglei He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBACAyA+AMQy/CDGA5gwDxFaeCQbgIwEYrWA1RiANBKlxZy9x/DAxx21PMbXDj8E2lKXOH9GAuODt20M8uY4tFj2HEs4OPPMcR6z22kGQC2HEzfcSGA2nNvGYLizAYfDbiQfOMzbdgyoJQGk5UDiBokENmneNoYEsFOxabn/sAGsxXh2+geYw9h/49VygxlkSw2PgXQOyBbmxIYbCWzMeLWcSQP6pe0Aj8TtnIIDCQaHjTecedgsOeechOEGXFqOnzH+8LGtTo5/dvrmDx8q6mTntycf/PCmzEYely1QcBhmAoNjAwNjA5AlgVc9ENTBWfaElI6CUTAKRsHIAwAUp2UE2gmZtwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-1050-802X","institution":"Sun Yat-Sen University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xionglei","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2022-01-26 06:25:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1297947/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1297947/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":18287981,"identity":"45e3756e-c112-421c-91c1-937a0fce734b","added_by":"auto","created_at":"2022-02-16 15:58:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3815345,"visible":true,"origin":"","legend":"","description":"","filename":"MS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1297947/v1_covered.pdf"},{"id":18287980,"identity":"a04120b1-37ad-4ca5-b1cf-d3b3600f909a","added_by":"auto","created_at":"2022-02-16 15:58:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":337674,"visible":true,"origin":"","legend":"Reporting Summary","description":"","filename":"rs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1297947/v1/3da251cf267902f8e79dd981.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"The trait coding rule in phenotype space","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1297947/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":false,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-1297947/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1297947/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Is it possible to infer the genetically determined value of a quantitative trait from other traits? 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