Elite germplasm introduction, training set composition, and genetic optimization algorithms effect in genomic selection-based breeding programs: a stochastic simulation study in self-pollinated crops

Elite germplasm introduction, training set composition, and genetic optimization algorithms effect in genomic selection-based breeding programs: a stochastic simulation study in self-pollinated crops | 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 Elite germplasm introduction, training set composition, and genetic optimization algorithms effect in genomic selection-based breeding programs: a stochastic simulation study in self-pollinated crops Roberto Fritsche-Neto, Rafael Massahiro Yassue, Allison Vieira da Silva, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4355565/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 In genomic selection, the prediction accuracy is heavily influenced by the training set (TS) composition. Currently, two primary strategies for building TS are in use: one involves accumulating historical phenotypic records from multiple years, while the other is the “test-and-shelf” approach. Additionally, studies have suggested that optimizing TS composition using genetic algorithms can improve the accuracy of prediction models. Most breeders operate in open systems, introducing new genetic variability into their populations as needed. However, the impact of elite germplasm introduction in GS models remains unclear. Therefore, we conducted a case study in self-pollinated crops using stochastic simulations to understand the effects of elite germplasm introduction, TS composition, and its optimization in long-term breeding programs. Overall, introducing external elite germplasm reduces the prediction accuracy. In this context, Test and Shelf seem more stable regarding accuracy in dealing with introductions despite the origin and rate, being useful in programs where the introductions come from different sources over the years. Conversely, using historical data, if the introductions come from the same source over the cycles, this negative effect is reduced as long as the cycles and this approach become the best. Thus, it may support public breeding programs in establishing networks of collaborations, where the exchange of germplasm will occur at a pre-defined rate and flow. In either case, the use of algorithms of optimization to trim the genetic variability does not bring a substantial advantage in the medium to long term. GS-based methods breeding networks breeding consortium PVmean genetic variability Full Text Supplementary Files RFNINTOTS.r 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-4355565","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299333993,"identity":"a805b115-4de6-4ff0-898c-65603fd3d3a4","order_by":0,"name":"Roberto 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