HoloSimR: a comprehensive framework for simulating breeding programs including the hologenome scenario

HoloSimR: a comprehensive framework for simulating breeding programs including the hologenome scenario | 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 HoloSimR: a comprehensive framework for simulating breeding programs including the hologenome scenario Cristina Casto-Rebollo, Ivan Pocrnic, Gregor Gorjanc, Noelia Ibáñez-Escriche This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5409986/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 Including microbiome information in breeding schemes may be helpful to improve the selection response of livestock populations. However, the complexity of the microbiome makes modelling across species and traits difficult. The estimation of the microbiability and the identification of the microbial species are highly dependent on the methodology used. Indeed, it is complicated to decide which is the best one because we fail to know the true underlying scenario. This study proposes an R package named HoloSimR for simulating the coevolution of the genome and the microbiota under a selection process. HoloSimR allows the user to explore the effect of the microbiota on the phenotypic response to selection and the effects of the environment, host genetics and symbiosis between microbial species on the composition of the microbiota. Results To illustrate the use of HoloSimR, a divergent selection process was simulated over ten generations. This example considered the most complete simulation that the HoloSimR package can perform; a divergent selection process, for six different scenarios, and with and without the symbiosis effect. The scenarios simulated the different approximations for calculating the phenotype (genome only, microbiota or both), as well as the host genetic effect on the microbiota composition. The most complex example took 898.47 minutes (~ 15 hours) on a standard laptop with 16 GB of RAM, for ten generations of selection, repeated ten times. Conclusion HoloSimR provides a valuable research platform, allowing researchers to test hypotheses and develop new approaches in a controlled in silico environment before applying them to real-world breeding programmes. This ultimately advances our understanding of host-microbiota interactions in the context of animal breeding. Animal Science Hologenome R package simulation breeding program Full Text Additional Declarations The authors declare no competing interests. Supplementary Files lastGboxplotsymbiosishigh.tiff Boxplot of effects at the final generation for the high-selected population across all scenarios 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-5409986","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":375325825,"identity":"df0a60e3-23ab-4f89-9e57-f2b9a1c530da","order_by":0,"name":"Cristina 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