A Validated Molecular Protocol to Differentiate Pure Wolves, Dogs and Wolf x Dog Hybrids through a Panel of Multiplexed Canine STR Markers

Author:

Lorenzini Rita,Attili Lorenzo,Tancredi Chantal,Fanelli Rita,Garofalo LuisaORCID

Abstract

The conservation of pure wolf populations in Europe is currently threatened by anthropogenic hybridization with dogs, through potential extinction of local gene pools due to replacement with domestic gene variants. Distinction of wolf–dog hybrids from wild ancestors is essential for development and implementation of management and conservation plans. Morphological traits are rarely helpful, and a genetic approach is the most effective to distinguish admixed individuals back to old backcrosses. To provide a molecular tool to address the issue, we optimized and validated a cost-effective protocol in multiplexed PCR format based on 22 STR canine loci, which allows us to distinguish genetically pure wolves from dogs, and, when used in association with a Bayesian assignment approach, is capable of statistically assigning admixed individuals to classes of hybrids with different levels of dog ancestry. Our method demonstrated high reliability, showing full repeatability and reproducibility of data with as little as 0.125 ng of genomic DNA, and was therefore suitable for the analysis of non-invasively collected samples and degraded DNA. The application of our STR panel to the appropriate assignment procedure unambiguously defined two genetically separated clusters for wolves and dogs, and successfully identified known hybrids as admixed individuals, which eventually were classified as recent hybrids and older backcrosses. The protocol, which is described here in detail, can be adopted by various laboratories that need this kind of diagnosis; furthermore, it would be capable of producing concordant results through inter-laboratory comparisons with wolf and dog control DNAs.

Publisher

MDPI AG

Subject

Nature and Landscape Conservation,Agricultural and Biological Sciences (miscellaneous),Ecological Modeling,Ecology

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