Optimisation of a microfluidic SNP assay for accurate hybrid class detection in the European Wildcat (Felis silvestris)

Optimisation of a microfluidic SNP assay for accurate hybrid class detection in the European Wildcat (Felis silvestris) | 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 Method Article Optimisation of a microfluidic SNP assay for accurate hybrid class detection in the European Wildcat (Felis silvestris) Lina S. Martin, Gregor Rolshausen, Paulo C. Alves, Federica Mattucci, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4902208/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 May, 2025 Read the published version in Conservation Genetics Resources → Version 1 posted 7 You are reading this latest preprint version Abstract Anthropogenic hybridisation between wild and domestic taxa poses a significant threat to species integrity, including the endangered European wildcat. To enable reliable molecular assessment of admixture with domestic cats and to increase the accuracy of hybrid class assignment we optimised an existing reduced microfluidic 96 Single Nucleotide Polymorphism (SNP) panel. We selected SNPs from a genome-wide dataset for maximum FST between both taxa and replaced 60 SNPs from the previous 96 SNP panel. Comparison of both panels based on simulated hybrid genotypes and real-world genotypes proof the higher discriminatory power of the optimised panel, which allows for reliable assignment of F1 and F2 hybrids, as well as 1st and 2nd generation backcrosses. Additionally, we successfully tested the panel for both tissue and non-invasively collected hair samples, demonstrating the suitability of the new panel for implementation in wildcat monitoring programmes. reduced SNP panel microfluidic hybridisation Felidae Figures Figure 1 Main Text Anthropogenic hybridisation poses a serious conservation challenge for wild taxa (Allendorf et al. 2011). Hybridisation with domestic cats ( Felis catus ), for instance, imperils the genetic integrity of the endangered European wildcat ( Felis silvestris ) (Nussberger et al. 2023). In some areas of its distribution, such as Scotland, ongoing mating of both species and their hybrid progeny has led to extensive introgression, ultimately leading to the formation of a hybrid swarm (Senn et al. 2019). As genetic assessments of this elusive species often rely on non-invasively collected hair samples with low-quality DNA (Steyer et al. 2013), a microfluidic single nucleotide polymorphism (SNP) genotyping panel has been utilised to monitor hybridisation in wildcat populations based on single hairs (Nussberger et al. 2014). Among 96 SNP markers, the panel comprises 72 nuclear markers for hybrid detection and has been used to identify wildcats, domestic cats and their most recent hybrid generations (F1, F2, and backcrosses; Nussberger et al. 2013, 2014; Tiesmeyer et al. 2020). Here we aimed to optimise the aforementioned panel to improve the statistical power for accurate hybrid class assignment. Therefore, we assessed the discrimination power of the 72 nuclear markers from Nussberger et al. (2013) and an additional set of 134 markers from Mattucci et al. (2014, 2016, 2019), which were pre-selected for their ability to discriminate between domestic and wildcats. Wet lab testing of marker performance followed the scheme outlined in von Thaden et al. (2020). At first, SNPtype™ assays were designed for the pre-selected 134 markers using the Standard Biotools D3™ assay design tool ( https://d3.standardbio.com ) and run on the Biomark HD (Standard BioTools Inc, formerly Fluidigm) using the high-DNA reference sample set provided in von Thaden et al. (2020) to test for technical performance and maximum F ST . The best performing 132 markers were then retested using a dilution of 0.2 ng/µl template DNA to simulate low-quality DNA. Subsequently, the best-performing markers with highest F ST were combined with the best performing markers from Nussberger et al. (2013) in a single 96 panel. After retesting this first version, invalid markers were replaced iteratively until a final panel of 96 SNP markers demonstrated optimal performance (Supplementary Material Table S1 ). Finally, the panel was tested for its performance with low-quality DNA using 28 non-invasively collected hair samples obtained from regular German wildcat monitoring (Supplementary Material Table S2 ). The optimised panel comprises 36 markers from the former panel (Nussberger et al. 2014) and 60 new markers from (Mattucci et al. 2014, 2016, 2019), with an average F ST = 0.715 compared to F ST = 0.614 for the former panel. To compare the statistical discrimination power of both SNP panels, we genotyped a set of 50 wildcats from different European regions and 50 domestic cats. From those genotypes we generated 30 in silico hybrid genotypes covering four hybrid generations respectively (F1, F2, 1st and 2nd backcrosses). For this, allelic frequencies were derived from each pure population, and gametes were drawn randomly based on a multinomial distribution. The in silico hybrid and pure genotypes were then analysed based on genetic distance clustering using Discriminant Analysis of Principal Components (DAPC, Jombart et al. 2010, Fig. 1 a), and based on assumed allele frequency distributions, using the NewHybrids software (Anderson and Thompson 2002, Supplementary Material 1). In addition, the DAPC framework was used to generate 100 bootstrap datasets for each SNP panel to compare the proportions of correct (re-)assignments for hybrid and pure genotypes to their respective origin clusters (Fig. 1 b). All analyses were performed in the R software environment, using the adegenet package (R Core Team 2024, Jombart 2008). The DAPC scatter plots based on the optimised panel show the higher discriminative power compared to the former panel as clusters are more distinct and show considerably less overlap (Fig. 1 a). In addition, the optimised panel exhibits consistently higher reassignment probabilities, with higher mean values and generally narrower interquartile ranges, particularly for assigning to second backcross generations (Fig. 1 b). Those results are confirmed by increased discriminatory power in the NewHybrids analysis (Supplementary Material Fig S3 ). The optimised panel outperforms the former panel in terms of discrimination power and assignment accuracy, providing consistently higher resolution in hybrid class detection and reliably assigning individuals to the second backcross generation. We successfully validated the panel for both tissue and non-invasively collected hair samples, confirming its suitability for implementation in applied species monitoring programs. The enhanced resolution enables reliable assessment of hybridisation dynamics, facilitating accurate genetic reconstruction of hybrid swarm formation and the application of effective conservation management strategies. Declarations Author contributions: Lina S. Martin, G. Rolshausen, and B. Cocchiararo contributed equally. Acknowledgements: This work was supported by the LOEWE Centre for Translational Biodiversity Genomics funded by the Federal State of Hessen. We are grateful to the lab team of the Senckenberg Centre for Wildlife Genetics. Conflict of interest: The authors declare no competing interests. References Allendorf, F. W., Leary, R. F., Spruell, P., & Wenburg, J. K. (2001). The problems with hybrids: Setting conservation guidelines. Trends in Ecology and Evolution , 16(11), 613–622. https://doi.org/10.1016/S0169-5347(01)02290-X Anderson, E. C., & Thompson, E. (2002). A model-based method for identifying species hybrids using multilocus genetic data. Genetics , 160(3), 1217-1229. https://doi.org/10.1093/genetics/160.3.1217 Jombart, T. (2008). adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics , 24(11), 1403-1405. Jombart, T., Devillard, S., & Balloux, F. (2010). Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genetics , 11, 1-15. https://doi.org/10.1186/1471-2156-11-94. https://doi.org/10.1093/bioinformatics/btn129 Mattucci, F. (2014). Conservation genetics of European wildcat ( Felis silvestris silvestris ): A wide and integrating analysis protocol for admixture inferences and population structure (Unpublished doctoral dissertation). University of Bologna. Retrieved from http://amsdo ttora to.unibo.it/6459/1/Phd_thesis_Mattu cciF_170314.pdf Mattucci, F., Oliveira, R., Lyons, L. A., Alves, P. C., & Randi, E. (2016). European wildcat populations are subdivided into five main biogeographic groups: consequences of Pleistocene climate changes or recent anthropogenic fragmentation? Ecology and Evolution , 6(1), 3-22. https://doi.org/10.1002/ece3.1815 Mattucci, F., Galaverni, M., Lyons, L. A., Alves, P. C., Randi, E., Velli, E., ... & Caniglia, R. (2019). Genomic approaches to identify hybrids and estimate admixture times in European wildcat populations. Scientific Reports 9, 11612. https://doi.org/10.1038/s41598-019-48002-w Nussberger, B., Greminger, M. P., Grossen, C., Keller, L. F., & Wandeler, P. (2013). Development of SNP markers identifying European wildcats, domestic cats, and their admixed progeny. Molecular Ecology Resources , 13(3), 447–460. https://doi.org/10.1111/1755-0998.12075 Nussberger, B., Wandeler, P. & Camenisch, G. (2014). A SNP chip to detect introgression in wildcats allows accurate genotyping of single hairs. European Journal of Wildlife Research , 60, 405–410. https://doi.org/10.1007/s10344-014-0806-3 Nussberger, B., Hertwig, S. T., & Roth, T. (2023). Monitoring distribution, density and introgression in European wildcats in Switzerland. Biological Conservation , 281, 110029. https://doi.org/10.1016/j.biocon.2023.110029 R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ Senn, H. V., Ghazali, M., Kaden, J., Barclay, D., Harrower, B., Campbell, R. D., ... & Kitchener, A. C. (2019). Distinguishing the victim from the threat: SNP-based methods reveal the extent of introgressive hybridization between wildcats and domestic cats in Scotland and inform future in situ and ex situ management options for species restoration. Evolutionary Applications , 12(3), 399-414. https://doi.org/10.1111/eva.12720 Steyer, K., Simon, O., Kraus, R. H., Haase, P., & Nowak, C. (2013). Hair trapping with valerian-treated lure sticks as a tool for genetic wildcat monitoring in low-density habitats. European Journal of Wildlife Research , 59(1), 39-46. http://dx.doi.org/10.1007/s10344-012-0644-0 Tiesmeyer, A., Ramos, L., Manuel Lucas, J., Steyer, K., Alves, P. C., Astaras, C., ... & Nowak, C. (2020). Range-wide patterns of human-mediated hybridisation in European wildcats. Conservation Genetics , 21, 247-260. https://doi.org/10.1007/s10592-019-01247-4 von Thaden, A., Nowak, C., Tiesmeyer, A., Reiners, T. E., Alves, P. C., Lyons, L. A., ... & Cocchiararo, B. (2020). Applying genomic data in wildlife monitoring: Development guidelines for genotyping degraded samples with reduced single nucleotide polymorphism panels. Molecular Ecology Resources , 20(3), 662-680. https://doi.org/10.1111/1755-0998.13136 Supplementary Figures 1 and 2 Supplementary Figures 1 and 2 are not available with this version Additional Declarations No competing interests reported. Supplementary Files LSMoptimisedSNPpanelSupplementaryMaterialFigS3NewHybrids.docx LSMoptimisedSNPpanelSupplementaryMaterialTableS1SNPsFst.xlsx LSMoptimisedSNPpanelSupplementaryMaterialTableS2noninvasivesamples.xlsx Cite Share Download PDF Status: Published Journal Publication published 05 May, 2025 Read the published version in Conservation Genetics Resources → Version 1 posted Editorial decision: Revision requested 16 Jan, 2025 Reviews received at journal 29 Dec, 2024 Reviewers agreed at journal 02 Dec, 2024 Reviewers invited by journal 21 Aug, 2024 Editor assigned by journal 13 Aug, 2024 Submission checks completed at journal 13 Aug, 2024 First submitted to journal 12 Aug, 2024 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-4902208","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":345084462,"identity":"deb2e8e3-8e4f-4a92-a3fb-bd444d652eb2","order_by":0,"name":"Lina S. Martin","email":"data:image/png;base64,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","orcid":"","institution":"Senckenberg Research Institute and Natural History Museum Frankfurt","correspondingAuthor":true,"prefix":"","firstName":"Lina","middleName":"S.","lastName":"Martin","suffix":""},{"id":345084463,"identity":"b3c6f98a-b40d-436d-8383-98cf66dca85c","order_by":1,"name":"Gregor Rolshausen","email":"","orcid":"","institution":"Senckenberg Research Institute and Natural History Museum Frankfurt","correspondingAuthor":false,"prefix":"","firstName":"Gregor","middleName":"","lastName":"Rolshausen","suffix":""},{"id":345084464,"identity":"4f7ea2bb-17f5-4855-b941-71132283cef7","order_by":2,"name":"Paulo C. 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Hulls comprise subsets of either pure lineages (black dots) or in silico hybrid generations (transparent dots). \u003cstrong\u003eb)\u003c/strong\u003e Comparison of discriminatory effect size between former and new panel. Boxplots span data from 100 bootstrap samples of re-assignment probabilities from discriminant analyses of principal components (DAPC, Jombart et al. 2010)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4902208/v1/79b7148b1f5264264b85353a.png"},{"id":82537561,"identity":"8c48a3d4-90ed-4792-bf0a-6fec113bb7ad","added_by":"auto","created_at":"2025-05-12 16:08:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1025618,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4902208/v1/d1998a4d-87b3-4022-bbb8-dd2391247f13.pdf"},{"id":64246717,"identity":"5e171778-7d64-41ab-bd65-747e4ea4bef3","added_by":"auto","created_at":"2024-09-10 20:04:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":66360,"visible":true,"origin":"","legend":"","description":"","filename":"LSMoptimisedSNPpanelSupplementaryMaterialFigS3NewHybrids.docx","url":"https://assets-eu.researchsquare.com/files/rs-4902208/v1/f131814e7bb7ff55e04dc431.docx"},{"id":64246718,"identity":"152cc942-f86c-4a52-9eaf-757c0d91f633","added_by":"auto","created_at":"2024-09-10 20:04:06","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11660,"visible":true,"origin":"","legend":"","description":"","filename":"LSMoptimisedSNPpanelSupplementaryMaterialTableS1SNPsFst.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4902208/v1/b4596385ba748555cab2e735.xlsx"},{"id":64246716,"identity":"76129604-8054-4dfd-8a03-a62f78826e2b","added_by":"auto","created_at":"2024-09-10 20:04:06","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":12070,"visible":true,"origin":"","legend":"","description":"","filename":"LSMoptimisedSNPpanelSupplementaryMaterialTableS2noninvasivesamples.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4902208/v1/795f91006362a38f1ba57852.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimisation of a microfluidic SNP assay for accurate hybrid class detection in the European Wildcat (Felis silvestris)","fulltext":[{"header":"Main Text","content":"\u003cp\u003eAnthropogenic hybridisation poses a serious conservation challenge for wild taxa (Allendorf et al. 2011). Hybridisation with domestic cats (\u003cem\u003eFelis catus\u003c/em\u003e), for instance, imperils the genetic integrity of the endangered European wildcat (\u003cem\u003eFelis silvestris\u003c/em\u003e) (Nussberger et al. 2023). In some areas of its distribution, such as Scotland, ongoing mating of both species and their hybrid progeny has led to extensive introgression, ultimately leading to the formation of a hybrid swarm (Senn et al. 2019). As genetic assessments of this elusive species often rely on non-invasively collected hair samples with low-quality DNA (Steyer et al. 2013), a microfluidic single nucleotide polymorphism (SNP) genotyping panel has been utilised to monitor hybridisation in wildcat populations based on single hairs (Nussberger et al. 2014). Among 96 SNP markers, the panel comprises 72 nuclear markers for hybrid detection and has been used to identify wildcats, domestic cats and their most recent hybrid generations (F1, F2, and backcrosses; Nussberger et al. 2013, 2014; Tiesmeyer et al. 2020).\u003c/p\u003e \u003cp\u003eHere we aimed to optimise the aforementioned panel to improve the statistical power for accurate hybrid class assignment. Therefore, we assessed the discrimination power of the 72 nuclear markers from Nussberger et al. (2013) and an additional set of 134 markers from Mattucci et al. (2014, 2016, 2019), which were pre-selected for their ability to discriminate between domestic and wildcats. Wet lab testing of marker performance followed the scheme outlined in von Thaden et al. (2020). At first, SNPtype\u0026trade; assays were designed for the pre-selected 134 markers using the Standard Biotools D3\u0026trade; assay design tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://d3.standardbio.com\u003c/span\u003e\u003cspan address=\"https://d3.standardbio.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and run on the Biomark HD (Standard BioTools Inc, formerly Fluidigm) using the high-DNA reference sample set provided in von Thaden et al. (2020) to test for technical performance and maximum F\u003csub\u003eST\u003c/sub\u003e. The best performing 132 markers were then retested using a dilution of 0.2 ng/\u0026micro;l template DNA to simulate low-quality DNA. Subsequently, the best-performing markers with highest F\u003csub\u003eST\u003c/sub\u003e were combined with the best performing markers from Nussberger et al. (2013) in a single 96 panel. After retesting this first version, invalid markers were replaced iteratively until a final panel of 96 SNP markers demonstrated optimal performance (Supplementary Material Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Finally, the panel was tested for its performance with low-quality DNA using 28 non-invasively collected hair samples obtained from regular German wildcat monitoring (Supplementary Material Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The optimised panel comprises 36 markers from the former panel (Nussberger et al. 2014) and 60 new markers from (Mattucci et al. 2014, 2016, 2019), with an average F\u003csub\u003eST\u003c/sub\u003e = 0.715 compared to F\u003csub\u003eST\u003c/sub\u003e = 0.614 for the former panel.\u003c/p\u003e \u003cp\u003eTo compare the statistical discrimination power of both SNP panels, we genotyped a set of 50 wildcats from different European regions and 50 domestic cats. From those genotypes we generated 30 \u003cem\u003ein silico\u003c/em\u003e hybrid genotypes covering four hybrid generations respectively (F1, F2, 1st and 2nd backcrosses). For this, allelic frequencies were derived from each pure population, and gametes were drawn randomly based on a multinomial distribution. The \u003cem\u003ein silico\u003c/em\u003e hybrid and pure genotypes were then analysed based on genetic distance clustering using Discriminant Analysis of Principal Components (DAPC, Jombart et al. 2010, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), and based on assumed allele frequency distributions, using the NewHybrids software (Anderson and Thompson 2002, Supplementary Material 1). In addition, the DAPC framework was used to generate 100 bootstrap datasets for each SNP panel to compare the proportions of correct (re-)assignments for hybrid and pure genotypes to their respective origin clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). All analyses were performed in the R software environment, using the adegenet package (R Core Team 2024, Jombart 2008).\u003c/p\u003e \u003cp\u003eThe DAPC scatter plots based on the optimised panel show the higher discriminative power compared to the former panel as clusters are more distinct and show considerably less overlap (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). In addition, the optimised panel exhibits consistently higher reassignment probabilities, with higher mean values and generally narrower interquartile ranges, particularly for assigning to second backcross generations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Those results are confirmed by increased discriminatory power in the NewHybrids analysis (Supplementary Material Fig \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe optimised panel outperforms the former panel in terms of discrimination power and assignment accuracy, providing consistently higher resolution in hybrid class detection and reliably assigning individuals to the second backcross generation.\u003c/p\u003e \u003cp\u003eWe successfully validated the panel for both tissue and non-invasively collected hair samples, confirming its suitability for implementation in applied species monitoring programs. The enhanced resolution enables reliable assessment of hybridisation dynamics, facilitating accurate genetic reconstruction of hybrid swarm formation and the application of effective conservation management strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor contributions:\u003c/p\u003e\n\u003cp\u003eLina S. Martin, G. Rolshausen, and B. Cocchiararo contributed equally.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Acknowledgements:\u003c/p\u003e\n\u003cp\u003eThis work was supported by the LOEWE Centre for Translational Biodiversity Genomics funded by the Federal State of Hessen. We are grateful to the lab team of the Senckenberg Centre for Wildlife Genetics.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Conflict of interest: The authors declare no competing interests.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllendorf, F. W., Leary, R. F., Spruell, P., \u0026amp; Wenburg, J. K. (2001). The problems with hybrids: Setting conservation guidelines. \u003cem\u003eTrends in Ecology and Evolution\u003c/em\u003e, 16(11), 613\u0026ndash;622. https://doi.org/10.1016/S0169-5347(01)02290-X\u003c/li\u003e\n\u003cli\u003eAnderson, E. C., \u0026amp; Thompson, E. (2002). A model-based method for identifying species hybrids using multilocus genetic data. \u003cem\u003eGenetics\u003c/em\u003e, 160(3), 1217-1229. https://doi.org/10.1093/genetics/160.3.1217\u003c/li\u003e\n\u003cli\u003eJombart, T. (2008). adegenet: a R package for the multivariate analysis of genetic markers. \u003cem\u003eBioinformatics\u003c/em\u003e, 24(11), 1403-1405.\u003c/li\u003e\n\u003cli\u003eJombart, T., Devillard, S., \u0026amp; Balloux, F. (2010). Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. \u003cem\u003eBMC Genetics\u003c/em\u003e, 11, 1-15. https://doi.org/10.1186/1471-2156-11-94. https://doi.org/10.1093/bioinformatics/btn129\u003c/li\u003e\n\u003cli\u003eMattucci, F. (2014). Conservation genetics of European wildcat (\u003cem\u003eFelis silvestris \u003cem\u003esilvestris\u003c/em\u003e): A wide and integrating analysis protocol for admixture inferences and population structure (Unpublished doctoral dissertation). University of Bologna. Retrieved from http://amsdo ttora to.unibo.it/6459/1/Phd_thesis_Mattu cciF_170314.pdf\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eMattucci, F., Oliveira, R., Lyons, L. A., Alves, P. C., \u0026amp; Randi, E. (2016). European wildcat populations are subdivided into five main biogeographic groups: consequences of Pleistocene climate changes or recent anthropogenic fragmentation? \u003cem\u003eEcology and Evolution\u003c/em\u003e, 6(1), 3-22. https://doi.org/10.1002/ece3.1815\u003c/li\u003e\n\u003cli\u003eMattucci, F., Galaverni, M., Lyons, L. A., Alves, P. C., Randi, E., Velli, E., ... \u0026amp; Caniglia, R. (2019). Genomic approaches to identify hybrids and estimate admixture times in European wildcat populations. \u003cem\u003eScientific Reports\u003c/em\u003e 9, 11612. https://doi.org/10.1038/s41598-019-48002-w\u003c/li\u003e\n\u003cli\u003eNussberger, B., Greminger, M. P., Grossen, C., Keller, L. F., \u0026amp; Wandeler, P. (2013). Development of SNP markers identifying European wildcats, domestic cats, and their admixed progeny. \u003cem\u003eMolecular Ecology Resources\u003c/em\u003e, 13(3), 447\u0026ndash;460. https://doi.org/10.1111/1755-0998.12075\u003c/li\u003e\n\u003cli\u003eNussberger, B., Wandeler, P. \u0026amp; Camenisch, G. (2014). A SNP chip to detect introgression in wildcats allows accurate genotyping of single hairs. \u003cem\u003eEuropean Journal of Wildlife Research\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e60, 405\u0026ndash;410. https://doi.org/10.1007/s10344-014-0806-3\u003c/li\u003e\n\u003cli\u003eNussberger, B., Hertwig, S. T., \u0026amp; Roth, T. (2023). Monitoring distribution, density and introgression in European wildcats in Switzerland. \u003cem\u003eBiological Conservation\u003c/em\u003e, 281, 110029. https://doi.org/10.1016/j.biocon.2023.110029\u003c/li\u003e\n\u003cli\u003eR Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/\u003c/li\u003e\n\u003cli\u003eSenn, H. V., Ghazali, M., Kaden, J., Barclay, D., Harrower, B., Campbell, R. D., ... \u0026amp; Kitchener, A. C. (2019). Distinguishing the victim from the threat: SNP-based methods reveal the extent of introgressive hybridization between wildcats and domestic cats in Scotland and inform future in situ and ex situ management options for species restoration. \u003cem\u003eEvolutionary Applications\u003c/em\u003e, 12(3), 399-414. https://doi.org/10.1111/eva.12720\u003c/li\u003e\n\u003cli\u003eSteyer, K., Simon, O., Kraus, R. H., Haase, P., \u0026amp; Nowak, C. (2013). Hair trapping with valerian-treated lure sticks as a tool for genetic wildcat monitoring in low-density habitats. \u003cem\u003eEuropean Journal of Wildlife Research\u003c/em\u003e, 59(1), 39-46. http://dx.doi.org/10.1007/s10344-012-0644-0\u003c/li\u003e\n\u003cli\u003eTiesmeyer, A., Ramos, L., Manuel Lucas, J., Steyer, K., Alves, P. C., Astaras, C., ... \u0026amp; Nowak, C. (2020). Range-wide patterns of human-mediated hybridisation in European wildcats. \u003cem\u003eConservation Genetics\u003c/em\u003e, 21, 247-260. https://doi.org/10.1007/s10592-019-01247-4\u003c/li\u003e\n\u003cli\u003evon Thaden, A., Nowak, C., Tiesmeyer, A., Reiners, T. E., Alves, P. C., Lyons, L. A., ... \u0026amp; Cocchiararo, B. (2020). Applying genomic data in wildlife monitoring: Development guidelines for genotyping degraded samples with reduced single nucleotide polymorphism panels. \u003cem\u003eMolecular Ecology Resources\u003c/em\u003e, 20(3), 662-680. https://doi.org/10.1111/1755-0998.13136\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Figures 1 and 2","content":"\u003cp\u003eSupplementary Figures 1 and 2 are not available with this version\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"conservation-genetics-resources","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cogr","sideBox":"Learn more about [Conservation Genetics Resources](https://www.springer.com/journal/12686)","snPcode":"12686","submissionUrl":"https://submission.nature.com/new-submission/12686/3","title":"Conservation Genetics Resources","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"reduced SNP panel, microfluidic, hybridisation, Felidae","lastPublishedDoi":"10.21203/rs.3.rs-4902208/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4902208/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Anthropogenic hybridisation between wild and domestic taxa poses a significant threat to species integrity, including the endangered European wildcat. To enable reliable molecular assessment of admixture with domestic cats and to increase the accuracy of hybrid class assignment we optimised an existing reduced microfluidic 96 Single Nucleotide Polymorphism (SNP) panel. We selected SNPs from a genome-wide dataset for maximum FST between both taxa and replaced 60 SNPs from the previous 96 SNP panel. Comparison of both panels based on simulated hybrid genotypes and real-world genotypes proof the higher discriminatory power of the optimised panel, which allows for reliable assignment of F1 and F2 hybrids, as well as 1st and 2nd generation backcrosses. Additionally, we successfully tested the panel for both tissue and non-invasively collected hair samples, demonstrating the suitability of the new panel for implementation in wildcat monitoring programmes.","manuscriptTitle":"Optimisation of a microfluidic SNP assay for accurate hybrid class detection in the European Wildcat (Felis silvestris)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-10 20:04:01","doi":"10.21203/rs.3.rs-4902208/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-16T12:56:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-29T19:02:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111430535182738524927315815372307077087","date":"2024-12-02T11:17:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-21T08:02:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-13T14:30:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-13T14:28:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Conservation Genetics Resources","date":"2024-08-12T17:17:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"conservation-genetics-resources","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cogr","sideBox":"Learn more about [Conservation Genetics Resources](https://www.springer.com/journal/12686)","snPcode":"12686","submissionUrl":"https://submission.nature.com/new-submission/12686/3","title":"Conservation Genetics Resources","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5c0335d6-8804-4330-be5c-fdfbf21a45bb","owner":[],"postedDate":"September 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-12T16:03:09+00:00","versionOfRecord":{"articleIdentity":"rs-4902208","link":"https://doi.org/10.1007/s12686-025-01386-6","journal":{"identity":"conservation-genetics-resources","isVorOnly":false,"title":"Conservation Genetics Resources"},"publishedOn":"2025-05-05 15:57:45","publishedOnDateReadable":"May 5th, 2025"},"versionCreatedAt":"2024-09-10 20:04:01","video":"","vorDoi":"10.1007/s12686-025-01386-6","vorDoiUrl":"https://doi.org/10.1007/s12686-025-01386-6","workflowStages":[]},"version":"v1","identity":"rs-4902208","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4902208","identity":"rs-4902208","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}