Table 1.
Genetic variability estimated at 39 autosomal microsatellite loci (STR) and at the KB melanistic deletion on the β-defensin CBD103 gene in the wolf, dog and putative hybrid sampled groups used in this study.
Table 2.
Distribution of the Y-linked microsatellite haplotypes in the wolf, dog and putative hybrid sampled groups.
Table 3.
Distribution of the mtDNA CR1 haplotypes in the wolf, dog and putative hybrid sampled groups.
Figure 1.
Discriminant analysis of principal components (DAPC) of wolf, dog and wolf x dog hybrids genotyped with 39 (A), 24 (B) and 12 (C) autosomal microsatellites.
Sampling groups: 1) village dogs sampled in Italy (DIT; n = 31); 2) “Lupino del Gigante” dogs from Italy (DAP; n = 26); 3) German Shepherd dogs from Czech Republic (DCZ; n = 12;); 4) wolves in Italy (WIT; n = 63); 5) wolves in Czech and Slovak republics (WCZ; n = 10); 6)wolves in Croatia (WHR; n = 26); 7) certified Czechoslovakian wolfdogs (WDCZ; n = 73); and 8) putative wolf x dog hybrids (HYIT; n = 30) collected in Italy and identified by their anomalous phenotypic traits (dog-like body shape, coat colour variations, presence of hind-leg spurs or white nails), or by previous microsatellite analyses. Black numbers indicate the most probable F1 (sample n. 1) and F2 (sample n. 3) individuals as determined by Structure and NewHybrids analyses. The first principal component PC I (abscissa) explains 51.48%, 49.96% and 63.65% of the total genetic variance shown by genotypes determined at 39, 24 and 12 microsatellites, respectively. The corresponding second principal component PC II (ordinate) explains 21.25%, 21.93% and 18.19% of the total genetic variance. The inserts (low right corners) indicate the proportion of genetic variability explained by the first 6 eigenvalues.
Table 4.
Identifications of the 30 putative wolf x dog hybrid samples used in this study.
Figure 2.
Structure analyses performed to infer the optimal partition of 8 sampled groups (A): DIT = village dogs in Italy; DAP = Apennine dogs; DCZ = German Shepherd; WIT = wolves in Italy; WCZ = wolves in Czech and Slovak republics; WHR = wolves in Croatia; WDCZ = Czechoslovakian wolfdogs; HYIT = putative wolf x dog hybrids collected in Italy; (genotyped at 39 autosomal microsatellites).
The posterior probability Ln(K) of the data and the statistics ΔK were used to identify the optimal K = 4 (averages of 2 independent runs). Plots of individual assignment probability to each inferred cluster are shown (B) for optimal K = 4, 5 and 6. Structure was run assuming K from 1 to 12, with 400 000 MCMC and discarding the first 40 000 burn-ins, using the “admixture” and independent allele frequency “I” models, and no prior information (option “usepopinfo” not activated).
Figure 3.
Structure analyses performed on the putative Italian wolf x dog hybrids (HYIT), assuming 4 reference groups (DIT, DAP, DCZ and WIT), at 39 (A), 24 (B) and 12 (C) microsatellites.
Structure was run with K from 1 to 8 (left side: values of ΔK; Evanno et al. [82]), with 400 000 MCMC and 40 000 burn-ins, with option “usepopinfo” not activated.
Figure 4.
Structure analyses of empirical (DIT, WIT and HYIT) and HybridLab-simulated genotypes identified using 39 microsatellites.
F1 and F2 between wolf and dogs; BC1 = first, and BC2 = second backcross with dogs (D) or wolves (W); BC3D and BC3W = F2 backcrossed with dogs or wolves, respectively. Structure was run with K = 2; admixture and I models, popflag = 0. Details of the individual proportion of admixture in the Italian wolves (WIT) and putative hybrid (HYIT), genotyped with 39 (top), 24 (mid) or 12 (bottom) microsatellites are showed.
Figure 5.
Average proportion of membership (qi, upper boxplots) of wolves from Italy (WIT) to the wolf cluster and lower boundary of their 90% credibility intervals (CI; lower boxplots), computed on genotypes at 39, 24 or 12 microsatellites.