University of Oulu

Harmoinen, J., von Thaden, A., Aspi, J. et al. Reliable wolf-dog hybrid detection in Europe using a reduced SNP panel developed for non-invasively collected samples. BMC Genomics 22, 473 (2021).

Reliable wolf-dog hybrid detection in Europe using a reduced SNP panel developed for non-invasively collected samples

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Author: Harmoinen, Jenni1; von Thaden, Alina2,3; Aspi, Jouni1;
Organizations: 1Ecology and Genetics Research Unit, University of Oulu, Oulu, Finland
2Conservation Genetics Group, Senckenberg Research Institute and Natural History Museum Frankfurt, Gelnhausen, Germany
3Institute for Ecology, Evolution and Diversity, Johann Wolfgang Goethe-University, Biologicum, Frankfurt am Main, Germany
4LOEWE Centre for Translational Biodiversity Genomics (LOEWE-TBG), Frankfurt am Main, Germany
5Association for the Conservation of Biological Diversity, Focşani, Romania
6Department of Systems Ecology and Sustainability, Faculty of Biology, University of Bucharest, Bucharest, Romania
7Department of Veterinary Biosciences, University of Helsinki, Helsinki, Finland
8Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland
9Folkhälsan Research Center, Helsinki, Finland
10Natural Resources Institute Finland (Luke), Eteläranta 55, FI-96300, Rovaniemi, Finland
11Department of Biology, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
12Department of Biotechnology and Life Sciences, Insubria University, Varese, Italy
13Unit for Conservation Genetics (BIO-CGE), Department for the Monitoring and Protection of the Environment and for Biodiversity Conservation, Italian Institute for Environmental Protection and Research, Bologna, Italy
14Scientific Area, WWF Italy, Rome, Italy
15CIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus de Vairão, 4485-661, Vairão, Portugal
16Department of Biology, Faculty of Science, University of Porto, Porto, Portugal
17Department of Zoology and Animal Cell Biology, Zoology Laboratory, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain
18Department of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, Italy
19Department of Chemistry and Bioscience, Faculty of Engineering and Science, University of Aalborg, Aalborg, Denmark
20European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.7 MB)
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Language: English
Published: Springer Nature, 2021
Publish Date: 2021-10-15


Background: Understanding the processes that lead to hybridization of wolves and dogs is of scientific and management importance, particularly over large geographical scales, as wolves can disperse great distances. However, a method to efficiently detect hybrids in routine wolf monitoring is lacking. Microsatellites offer only limited resolution due to the low number of markers showing distinctive allele frequencies between wolves and dogs. Moreover, calibration across laboratories is time-consuming and costly. In this study, we selected a panel of 96 ancestry informative markers for wolves and dogs, derived from the Illumina CanineHD Whole-Genome BeadChip (174 K). We designed very short amplicons for genotyping on a microfluidic array, thus making the method suitable also for non-invasively collected samples.

Results: Genotypes based on 93 SNPs from wolves sampled throughout Europe, purebred and non-pedigree dogs, and suspected hybrids showed that the new panel accurately identifies parental individuals, first-generation hybrids and first-generation backcrosses to wolves, while second- and third-generation backcrosses to wolves were identified as advanced hybrids in almost all cases. Our results support the hybrid identity of suspect individuals and the non-hybrid status of individuals regarded as wolves. We also show the adequacy of these markers to assess hybridization at a European-wide scale and the importance of including samples from reference populations.

Conclusions: We showed that the proposed SNP panel is an efficient tool for detecting hybrids up to the third-generation backcrosses to wolves across Europe. Notably, the proposed genotyping method is suitable for a variety of samples, including non-invasive and museum samples, making this panel useful for wolf-dog hybrid assessments and wolf monitoring at both continental and different temporal scales.

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Series: BMC genomics
ISSN: 1471-2164
ISSN-E: 1471-2164
ISSN-L: 1471-2164
Volume: 22
Article number: 473
DOI: 10.1186/s12864-021-07761-5
Type of Publication: A1 Journal article – refereed
Field of Science: 1184 Genetics, developmental biology, physiology
1181 Ecology, evolutionary biology
Funding: JH received funding from the Maj and Tor Nessling Foundation, the Finnish Cultural Foundation and Emil Aaltonen Foundation. Laboratory analyses were cofounded by the (LOEWE) program (Landes-Offensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz) of the German Federal State of Hessen. AvT received funding from the Karl und Marie Schack-Stiftung. RG was supported by the Portuguese Foundation for Science and Technology (FCT, DL57/2016).
Copyright information: © The Author(s). 2021. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.