Statistical analysis and simulations that account for simultaneous effects of positive, negative, and no crossover interference in multilocus recombination data

Statistical analysis and simulations that account for simultaneous effects of positive, negative, and no crossover interference in multilocus recombination data | 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 Statistical analysis and simulations that account for simultaneous effects of positive, negative, and no crossover interference in multilocus recombination data Shaul Sapielkin, Eyal Privman, Abraham B. Korol, Zeev Frenkel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7129389/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Dec, 2025 Read the published version in BMC Genomics → Version 1 posted 10 You are reading this latest preprint version Abstract Crossover interference (COI) is a widespread feature of homologous meiotic recombination. It can be quantified by the classical coefficient of coincidence (CoC), but this characteristic is highly variable and specific to the pair of chromosomal intervals considered. Several models have been proposed to characterize COI at the chromosomal level. In the gamma model, the strength of interference is characterized by a shape parameter ν . In contrast, the gamma-sprinkled two-pathway model (GS) accounts for both interference-dependent and independent crossover (CO) events by fitting a mixture of gamma distributions with v > 1 and v = 1 with proportions 1- p and p . In reality, COI may vary along the chromosome, resulting in а poor fit of the employed model to the analysed data. Additional inconsistency can be caused by the common neglect of possible negative crossover interference in the model, although the phenomenon was previously reported for several organisms. We present an extension of the GS model to account for possible negative COI and provide suitable software for estimating the parameters of the extended GS model. On real chromosome data from a conifer (larch, Larix principis-rupprechtii ), sugar beet ( Beta vulgaris ), and wheatgrass ( Thinopyrum intermedium ), we demonstrate a distinctive separation of three crossover (CO) processes on chromosomes: CO repulsion (positive COI), CO clustering (negative COI), and independent CO events (no COI). Genetic recombination crossover interference coefficient of coincidence extension of the gamma-sprinkled model negative crossover interference Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Dec, 2025 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 29 Aug, 2025 Reviews received at journal 10 Aug, 2025 Reviews received at journal 01 Aug, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviewers agreed at journal 20 Jul, 2025 Reviewers invited by journal 20 Jul, 2025 Editor invited by journal 18 Jul, 2025 Editor assigned by journal 17 Jul, 2025 Submission checks completed at journal 17 Jul, 2025 First submitted to journal 15 Jul, 2025 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-7129389","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":488817832,"identity":"1b7de1e5-3bb8-478d-8748-df11d02a9768","order_by":0,"name":"Shaul Sapielkin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACCSB+UAFi8TAwJNhABA88IKQl4QxMSxpUSwIhLYltUC0MUC0M+LTIz25+JpE4zyaxn4H34IcHCXYM/O0HGPHaYnDnmJlE4ra0xJkNfMkSCQnJDBJnEvA7zEAiAaTlsLHBAR4DicQfzAwMNwj4RX5G+jeJxDn/QVqMfyQk1DPIE9LCcCMHaEvDATmgFjOgww4zGBDSYnAjp9gi4ViynGQzj5lFQsJxHsMzQBMIOGzjjQ81djz87D3GN38kVMvJHT98+MMHfA6DA2YIBYwdxgaiNIyCUTAKRsEowA0AZm1L/a4+9M0AAAAASUVORK5CYII=","orcid":"","institution":"University of Haifa","correspondingAuthor":true,"prefix":"","firstName":"Shaul","middleName":"","lastName":"Sapielkin","suffix":""},{"id":488817835,"identity":"b7affbff-f739-4691-ad2a-9705f694f64a","order_by":1,"name":"Eyal Privman","email":"","orcid":"","institution":"University of Haifa","correspondingAuthor":false,"prefix":"","firstName":"Eyal","middleName":"","lastName":"Privman","suffix":""},{"id":488817838,"identity":"fbf4c751-6793-4a22-9552-d27e471ea8cd","order_by":2,"name":"Abraham B. 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It can be quantified by the classical coefficient of coincidence (CoC), but this characteristic is highly variable and specific to the pair of chromosomal intervals considered. Several models have been proposed to characterize COI at the chromosomal level. In the \u003cem\u003egamma\u003c/em\u003e model, the strength of interference is characterized by a shape parameter \u003cem\u003eν\u003c/em\u003e. In contrast, the \u003cem\u003egamma-sprinkled\u003c/em\u003e two-pathway model (GS) accounts for both interference-dependent and independent crossover (CO) events by fitting a mixture of gamma distributions with \u003cem\u003ev\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;1 and \u003cem\u003ev\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 with proportions 1-\u003cem\u003ep\u003c/em\u003e and \u003cem\u003ep\u003c/em\u003e. In reality, COI may vary along the chromosome, resulting in а poor fit of the employed model to the analysed data. 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