ArticleBMC genomics2021
Reliable wolf-dog hybrid detection in Europe using a reduced SNP panel developed for non-invasively collected samples.
Article in BMC genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 35 citations in OpenAlex.
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- Whole-Genome Data to Investigate Recent and Historical Dog Introgression Patterns in Italian Wolves.Ecology and evolution · 2025Article
- Development of a cost-effective, multifunctional SNP panel and analysis workflow for Wolf monitoring in Finland.Scientific reports · 2025Article
- Intronomics-MIP: a snakemake pipeline for analyzing multilocus intron polymorphisms in species identification and population genomics.BMC research notes · 2025Article
- Development of a Noninvasive Genotyping-In-Thousands (GTseq) Panel for Long-Term Conservation of Western Great Lakes Gray Wolves (Ecology and evolution · 2025Article
- Beyond population size: Whole-genome data reveal bottleneck legacies in the peninsular Italian wolf.The Journal of heredity · 2025Article
- Assessment of genetic diversity, population structure and wolf-dog hybridisation in the Eastern Romanian Carpathian wolf population.Scientific reports · 2023Article
- Genome-wide variant analyses reveal new patterns of admixture and population structure in Australian dingoes.Molecular ecology · 2023Article
- Citizen science facilitates first ever genetic detection of wolf-dog hybridization in Indian savannahs.Ecology and evolution · 2023Article
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- Application potential of chicken DNA chip in domestic pigeon species - Preliminary results.Saudi journal of biological sciences · 2023Article
- "Guess Who's Coming to Dinner": Molecular Tools to ReconstructAnimals : an open access journal from MDPI · 2022Article
- A reduced SNP panel to trace gene flow across southern European wolf populations and detect hybridization with other Canis taxa.Scientific reports · 2022Article
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Authors and funding
20 authors at 13 institutions in 9 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUnderstanding 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.
resultsGenotypes 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.
conclusionsWe 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.