We studied the distribution of the mitochondrial DNA haplotypes and microsatellite genotypes at 8 loci in 102 gray wolves, 57 livestock guarding dogs, and 9 mongrel dogs from Georgia (Caucasus). Most of the studied dogs had mitochondrial haplotypes clustered with presumably East Asian dog lineages, and most of the studied wolves had the haplotypes clustered with European wolves, but 20% of wolves and 37% of dogs shared the same mitochondrial haplotypes. Bayesian inference with STRUCTURE software suggested that more than 13% of the studied wolves had detectable dog ancestry and more than 10% of the dogs had detectable wolf ancestry. About 2–3% of the sampled wolves and dogs were identified, with a high probability, as first-generation hybrids. These results were supported by the relatedness analysis, which showed that 10% of wolves and 20% of dogs had closest relatives from an opposite group. The results of the study suggest that wolf–dog hybridization is a common event in the areas where large livestock guarding dogs are held in a traditional way, and that gene flow between dogs and gray wolves was an important force influencing gene pool of dogs for millennia since early domestication events. This process may have been terminated 1) in areas outside the natural range of gray wolves and 2) since very recent time, when humans started to more tightly control contacts of purebred dogs.

All existent dog breeds descend from domesticated wolves (Olsen 1985; Tsuda et al. 1997; Vilà et al. 1997; Leonard et al. 2002; Savolainen et al. 2002; Parker et al. 2004; Hindrikson et al. 2012; Larson et al. 2012). Wolves are thought to have first been domesticated in East Asia (Olsen 1985; Ding et al. 2012; Sacks et al. 2013), although Southwest (SW) Asian (Clutton-Brock 1995) and European (Thalmann et al. 2013) origin is also considered. Neither archaeological nor genetic data rule out multiple domestication events (Vonholdt et al. 2010; Larson et al. 2012; Thalmann et al. 2013). According to most authors, wolves were domesticated in East Asia 14000–40000 YA (Vilà et al. 1997; Savolainen et al. 2002; Östrander and Wayne 2005; Verginelli et al. 2005; Sacks et al. 2013), and the domestication was followed by the expansion of dogs throughout Eurasia. Deguilloux et al. (2009) and Sacks et al. (2013) hypothesized that the expansion led to the replacement of the multiple dog lineages throughout Eurasia, the process that can be traced by the presence of mitochondrial and Y-chromosome DNA haplogroups more typical for the Western than the Eastern Eurasian wolves (Savolainen et al. 2002; Sacks et al. 2013). On the other hand, “western” haplogroups could descend from long-lasting gene introgression between dogs and Western Eurasian wolves (Ardalan et al. 2011; Thalmann et al. 2013). Genetic studies of dogs and wolves from the same geographic area are critical for better understanding of wolf domestication pattern: They help to infer the extent and direction of gene flow between the wild and domestic forms and to generate ideas how potential gene flow could change gene pools of both dog breeds and wolf populations from different geographic areas since the early Holocene.

Multiple researchers explored natural hybridization between wolves and dogs using molecular genetic methods (Vilà and Wayne 1999; Randi and Lucchini 2002; Ciucci et al. 2003; Verardi et al. 2006; Randi 2008; Munoz-Fuentes et al. 2010; Godinho et al. 2011; Hindrikson et al. 2012; Khosravi et al. 2013). General findings of these studies and related assumptions are the following: 1) hybridization, at least in Europe, is currently a rare event (Randi and Lucchini 2002; Hindrikson et al. 2012), although up to 5% of wolf population may descend from the admixture events that happened in the last 2 centuries (Verardi et al. 2006; Godinho et al. 2011); 2) field observations suggest occasional interbreeding between male wolves and female dogs (Vilà and Wayne 1999; Hindrikson et al. 2012), whereas genetic analyses show that the hybrids may descend matrilineally both from wolves and from dogs (Vilà et al. 2003; Godinho et al. 2011); 3) mitochondrial DNA (mt-DNA) studies suggest gene introgression from wolf to dog population long after domestication (Hedrick 2009; Ardalan et al. 2011).

Majority of the studies of wolf–dog interactions are geographically limited to Western Europe, where dogs are mostly under tight human control (although some feral populations do exist: see Randi 2008), and wolf populations are small and fragmented (Boitani 1983; Vilà et al. 2003). Wolf is still a common species throughout most of Russia, Turkey, the Caucasus, Iran, Afghanistan, Mongolia, and Tibet, particularly in continental less human-populated parts of Eurasia (Mech and Boitani 2010). It is possible that wolf–dog hybridization is currently more common in the areas where 1) feral dogs are common or domestic dogs are only partly under control—such as large livestock guarding dogs in the mountains of the Middle East and Central Asia or (2) feral or less controlled dogs are sufficiently large to hybridize with wolves. The situation is the most appropriate for hybridization throughout Anatolia, the Caucasus, and mountainous parts of Iran, Iraq, and Turkmenistan, where large livestock guarding dogs are widespread (Rigg 2001) (Figure 1) and gray wolves are common. Recent study of Khosravi et al. (2013) suggests that gene flow between wolves and free-ranging dogs does exist in Iran, although the authors refrain from estimating the hybridization rates.

Figure 1.

Upper panel: livestock guarding dog; middle panel: livestock guarding dog with the inferred wolf ancestry (first-generation hybrid); lower panel: wolf (all from Kazbegi, Georgia).

Figure 1.

Upper panel: livestock guarding dog; middle panel: livestock guarding dog with the inferred wolf ancestry (first-generation hybrid); lower panel: wolf (all from Kazbegi, Georgia).

During 2008–2012, we collected tissues and feces of wolves and shepherd dogs from different parts of Georgia (the Caucasus; Figure 2). We studied the distribution of mt-DNA haplotypes and alleles at 8 microsatellite loci in the studied samples. We applied a combination of analytical approaches for identifying specimens of hybrid origin both in dogs and wolves and to infer current gene flow between them. The inferred rates of hybridization appeared high enough to suggest that it has strong impact on both wolf and dog gene pools.

Figure 2.

Sampling locations. Open circles: wolves from Western Georgia; closed circles: wolves from Eastern Georgia; stars: dogs.

Figure 2.

Sampling locations. Open circles: wolves from Western Georgia; closed circles: wolves from Eastern Georgia; stars: dogs.

Materials and methods

Sampling

We collected pieces of wolf pelts available from local hunters, blood of captured wolves, and scat samples from all over Georgia (the Caucasus) (altogether 102 wolf samples; Figure 2 and Table 1). Scats were collected by following wolf snow tracks in winter, as described in Lucchini et al. (2002), to avoid misidentification with dog scats. We collected 57 hair samples from shepherd dogs in Eastern Georgia and 9 samples of mongrel dogs.

Table 1

Studied samples of wolves and dogs from Georgia

Sample Scats Skin pieces/ hair/blood Short tandem repeat mt-DNA 
Gray wolves (Eastern Georgia) 53 20 73 39 
Gray wolves (Western Georgia) 27 29 26 
Livestock guarding dogs 57 57 53 
Mongrel dogs 
Sample Scats Skin pieces/ hair/blood Short tandem repeat mt-DNA 
Gray wolves (Eastern Georgia) 53 20 73 39 
Gray wolves (Western Georgia) 27 29 26 
Livestock guarding dogs 57 57 53 
Mongrel dogs 

DNA Extraction and Sequencing

DNA was extracted from hair samples of domestic dogs (Canis lupus familiaris) and from skin, blood, hair, and scat samples of gray wolves (C. lupus) using the Qiagen DNeasy kit (Blood and Tissue Kit and Stool Kit) according to manufacturer’s instructions. About 228bp of the mt-DNA control region (D-loop) was amplified using the following primers: L 15562 5′- CCATGC ATA TAAGCATGTACA T-3′ and H 15790 5′-AGA TGC CAG GTA TAG TTCCA-3′ (designed for this study; the primers match positions 15566–15793 of the full mitochondrial DNA sequence of C. lupus, GenBank accession # NC008092). PCR amplifications were carried out in 20 µL volumes (2–4 µL of template DNA, 0.25 units of Promega Taq polymerase, 10× Promega Buffer, 1mM of MgCl2, 0.1mM of each dNTP, and primer concentrations at 0.1mM). The thermal profile was 3min at 95 °C, followed by 46 cycles of 30 s at 94 °C, 1min at 56 °C, 1min at 72 °C, and a final extension at 72 °C for 10min and 10 °C at 10min. PCR products were checked using 1% by agarose gel with SyberSafe staining. Negative controls were used to check for contamination in each PCR reactions. Single-stranded sequencing was performed with the primers of PCR, using the Big-Dye Terminator v.3.1. PCR fragments were sequenced in both directions to assure sequence accuracy. ABI 3130 Genetic Analyzer was used for electrophoresis (Applied Biosystems, Inc.). The sequences were aligned with BioEdit software (Hall 1999). High-quality sequence fragments containing 204bp were deposited to GenBank (accession #KF048874-KF048906).

A median-joining (MJ) algorithm (Donnelly and Tavaré 1986; Bandelt et al. 1999) was applied to reconstruct all possible evolutionary pathways among the inferred haplotypes of Georgian dogs and wolves and dogs and wolves sequences downloaded from GenBank (Supplementary Table S1 online). In total, 317 sequences were used in the analyses, including 66 sequences of Georgian wolves and 57 of Georgian dogs. Software NETWORK 4.6.1.1 (Fluxus Technology Ltd.) was used for the network construction. The MJ algorithm was run repeatedly, first for estimating weights for individual substituting positions, and then for using the weighting option, in order to reduce the number of loops in the network.

Microsatellite Genotyping

Eight dinucleotide microsatellites were selected for their polymorphism and reliable scorability of wolves and dogs. The used primers were CXX.103, CXX.109, CXX.121, CXX.172, CXX.173, CXX.20, CXX.200, and CXX.377 (Östrander et al. 1993, 1995; Kitchen et al. 2005; Stronen et al. 2012). PCR amplifications were carried out in 7 µL volumes using the Qiagen master mix (3 µL of Master Mix [2×], 0.6 µL of Q solution (5×0), 1.28 µL of dH2O and 1.4 µL of DNA, and primer concentrations of 0.11–0.14 mMs). The thermal profile for PCR reaction was 15min at 95 °C, followed by 15 cycles of 10 s at 94 °C, 1min and 30 s at 61 °C and 1min at 95 °C, then followed by 29 cycles of 30 s at 94 °C, 1min and 30 s at 53 °C, 1min at 72 °C, and a final extension at 60 °C for 30min and 10 °C for 10min. Amplified DNA was run on ABI 3130, using deionized Formamade and Genescan size standard Liz 500 (Applied Biosystems Inc., Foster City, CA). Genotypes were screened using Genemapper v4.0 software package (Perkin Elmer, Waltham, MA). We amplified and screened all studied loci 3 or more times and calculated genotyping errors (allelic dropouts, false alleles, and genotype reliability), following the guidelines of Waits et al. (2001) and Miller et al. (2002) (software used: RelioType, GIMLET). We set the reliable change index threshold value as 95% and verified every locus of each individual twice for heterozygosity and 3 times for homozygosity.

Population Genetic Analysis: Microsatellites

We used microsatellite genotypes at the 8 studied loci for inferring population genetic parameters of wolves and the dogs and estimating gene flow between the 2 populations. We used 4 algorithms for evaluating gene flow between wolves and dogs and inferring origin of individual animals: 1) traditional Fst estimates among the populations separated on a priori basis (Dobzhansky and Wright 1941), 2) Bayesian clustering procedure using STRUCTURE algorithm (Pritchard et al. 2000; Randi 2008), 3) evaluating relatedness among all individuals using the relatedness measure (Lynch and Ritland 1999; Belkhir et al. 2002), and 4) Bayesian inference of migrants, using software BayesAss 3.0 (Wilson and Rannala 2003; Rannala 2007).

We applied ARLEQUIN v3.5 (Excoffier et al. 1992, 2005) for exploring genetic structure of the studied samples of wolves and dogs (genetic diversity, linkage disequilibria with 10000 permutations per locus pair, and Hardy–Weinberg equilibrium). We inferred gene flow rates (Nm) among the wolf and dog populations using expression Nm = 1/(4 × Fst) − 0.25 (Dobzhansky and Wright 1941), where Fst is replaced by Rst for microsatellite data.

While using Bayesian structure procedure, we applied software STRUCTURE v2.3 (Pritchard et al. 2000) to separate the entire dataset into 2 groups with least within-locus and between-locus disequilibria. Marcov Chain Monte Carlo (MCMC) parameters were set on burn-in period of 10000 and 100000 post-burnin samples. We repeated the procedure 10 times for the number of clusters (K) equal to 2 and selected the output of the run with the lowest log-likelihood ratio, following the outline of the software manual. Considering the outlines of Randi (2008), we assumed that wolves and dogs with assignment probabilities to the respective clusters below threshold values 0.9 and 0.8 might have hybrid ancestry. To test the hypothesis on the “true” hybrid origin of individuals with the intermediate assignment probabilities, we simulated 20 samples of “purebred” wolves and dogs by shuffling gametes among the conspecific animals with high (>0.9) empirical assignment probabilities. The simulated sample sizes were equal to those of the original dataset (102 wolves vs. 66 dogs). For simulation procedure, functions “Resample” and “Monte Carlo Analysis” of Microsoft Excel application PopTools (Hood 2010) were applied. For each simulated sample, the number of individuals with assignment probabilities below the threshold values was identified and compared with that of the original sample. The average difference between the inferred numbers of suspected hybrids in the empirical and generated samples was used as an index of “true” number of individuals with hybrid ancestry, and the 95% confidence limits were estimated by excluding the lowest and the highest values from the result set.

Bayesian algorithm developed by Wilson and Rannala (2003) was used for identifying first-generation hybrids among the studied wolves and dogs (second-generation migrants, according to the authors’ terminology). This algorithm allows estimating the probability of hybrid origin for each genotyped individual. Applying this method helped to rule out the potential presence of mistakenly identified scat samples (those should demonstrate high probability of being “first-generation migrants”). The software used was BayesAss 3.0 (Rannala 2007). We ran the program 5 times, with different random number seed, with the number of iterations to discard as burn-in equal to 100000 and the number of iterations for MCMC equal to 10000000.

We inferred coefficient of genetic relatedness of Lynch and Ritland (1999), rxy, among all studied individuals using software IDENTIX (Belkhir et al. 2002). From this analysis, we excluded 5 wolves and 3 dogs, which had nonreadable genotypes at least at one out of 8 studied microsatellite loci. The coefficient varies between 0 (no relatedness) to 1 (genetic identity). Then, for each individual (wolf or dog), we calculated the average relatedness separately with all wolves and with all dogs. We treated this individual as a “wolf related” if the average relatedness with wolves exceeded the average relatedness with the dogs and as a “dog related” in case of the inverse.

Results

Mitochondrial DNA Haplogroups

Forty-two variable positions were found among all 317 studied individuals. Eighty-nine haplotypes were identified, individual haplotypes separated by 1–4 substitutions. Both Georgian wolf and Georgian dog haplotypes were deeply nested in the haplotype network consisting European, Asian, Middle Eastern, and North American wolves and dogs studied by Savolainen et al. (2002) (Figure 3). Forty-five out of 66 Georgian wolves (68%) had haplotypes associated with some European (and to less extent, Asian and American) wolf haplotypes but not with Georgian or other dog haplotypes. Simultaneously, 21 Georgian wolves had haplotypes close or identical to those of clades B and A of Savolainen et al. (2002). Forty shepherd dogs (70% of the studied sample) had haplotypes associated with the clade A, 10 with the clade B, 7 with the clade C, and 1 with D (Figure 3). Thirteen Georgian wolves and 22 dogs (20% and 37% of the studied samples) had shared haplotypes (present in both Georgian wolves and Georgian dogs). This especially applied to the animals that belonged to the clade B of Savolainen et al. (2002).

Figure 3.

MJ network showing genetic links among the wolf and dog haplotypes inferred during this study and ones downloaded from the GenBank. Light green: Georgian dogs; dark green: Georgian wolves; yellow: European wolves; red: East Asian wolves; pink: West Asian wolves; blue: North American wolves; red forward diagonal lines: dogs, clade A (after Savolainen et al. 2002); black forward diagonal lines: dogs, clade B; vertical lines: clade C; horizontal lines: clade D. Individual haplotypes separated by 1–4 substitutions (only shown if the number of substitutions >1).

Figure 3.

MJ network showing genetic links among the wolf and dog haplotypes inferred during this study and ones downloaded from the GenBank. Light green: Georgian dogs; dark green: Georgian wolves; yellow: European wolves; red: East Asian wolves; pink: West Asian wolves; blue: North American wolves; red forward diagonal lines: dogs, clade A (after Savolainen et al. 2002); black forward diagonal lines: dogs, clade B; vertical lines: clade C; horizontal lines: clade D. Individual haplotypes separated by 1–4 substitutions (only shown if the number of substitutions >1).

In summary, there is a high haplotype group sharing between the studied wolves and dogs. Fourteen percentage of wolves and 17% of dogs belong to a single haplotype of the clade B of Savolainen et al. (2002), almost in equal proportion shared among the 2 forms (Figure 4).

Figure 4.

The distribution of the assignment probabilities to the clusters (K = 2) identified using Bayesian inference, in Georgian wolves and in livestock guarding dogs.

Figure 4.

The distribution of the assignment probabilities to the clusters (K = 2) identified using Bayesian inference, in Georgian wolves and in livestock guarding dogs.

Genetic Diversity of the Studied Samples – Microsatellites

Analysis of the microsatellite data revealed the presence of 4–16 alleles (10.8 on average) at each studied locus in the studied wolves and 4–14 alleles (8.8 on average) in dogs. Wolves had higher mean number of alleles and higher Garza-Williamson indices than dogs. No significant differences in observed or expected heterozygozities among the studied groups were recorded, although the observed heterozygozities exceeded the expected values in both groups. The number of pairs of loci significantly (Chi-square test, P < 0.05) deviated from linkage disequilibria was 29% for the entire sample, 11% for wolves, and 7% for dogs (Table 2).

Table 2

Genetic diversity of wolves and that of dogs from Georgia

Variable Wolves Dogs All samples 
Ho 
 Mean ± SD 0.780±0.112 0.826±0.219 0.760±0.124 
He 
 Mean ± SD 0.738±0.094 0.729±0.188 0.760±0.124 
NA 
 Mean 10.75–3.81 8.75–3.62 12.00–4.38 
GW 
 Mean ± SD 0.936±0.077 0.789±0.148 0. 0.833±0.117 
GWM 
Mean ± SD 0.840±0.125 0.668±0.119 0.715±0.116 
PLD 
 Proportion 0.107 0.071 0.285 
Variable Wolves Dogs All samples 
Ho 
 Mean ± SD 0.780±0.112 0.826±0.219 0.760±0.124 
He 
 Mean ± SD 0.738±0.094 0.729±0.188 0.760±0.124 
NA 
 Mean 10.75–3.81 8.75–3.62 12.00–4.38 
GW 
 Mean ± SD 0.936±0.077 0.789±0.148 0. 0.833±0.117 
GWM 
Mean ± SD 0.840±0.125 0.668±0.119 0.715±0.116 
PLD 
 Proportion 0.107 0.071 0.285 

He, expected heterozygozity; Ho, observed heterozygozity, GW, Garza-Williamson Index; GWM, modified Garza-Williamson Index; NA, allele number; PLD, proportion of alleles in linkage disequilibria (from 28 total allele pairs); SD, standard deviation.

The analysis showed significant differentiation between the dogs and wolves (exact test, P < 0.01). Pairwise Fst value was 0.0637 when comparing wolf and dog populations. The calculated migration rates [Nm = 1/(4 × Fst) − 0.25] between the dogs and wolves was 3.67.

Bayesian Inference: Wolves and Dogs With Hybrid Ancestry

STRUCTURE simulations showed reasonably good separation of the identified genotypes into 2 clusters, from here onwards W and D. Most of wolf samples had high assignment probabilities to the cluster W, and most of the dog samples had high assignment probabilities to the cluster D. However, the animals with intermediate assignment probabilities were common: 17.6% of the wolves had assignment probabilities to the cluster W falling below 0.8, and 15.2% of the dogs had assignment probabilities to the cluster D falling below 0.8. About 24.5% of the wolves and 24.2% of the dogs had assignment probabilities to the respective cluster below 0.9 (Table 2). Simulation procedure described in the Materials and methods section produced some genotypes with intermediate assignment probabilities. Average (for 20 replicates) difference between the empirically obtained and simulated genotypes slightly decreased the number of the individuals with suspected hybrid ancestry to 15.7–20.5% for wolves and 12.2–19.0% for dogs (threshold levels: 0.8 and 0.9). The respective confidence limits are shown in Table 2. The 95% confidence limits for wolves with suspected hybrid ancestry (considering “true” threshold level either 0.8 or 0.9) are 13.7–23.5% for wolves and 10.6–24.2% for the dogs. Only 3 wolves with suspected hybrid ancestry were sampled from feces, hence the misidentification cannot be a potential reason for the intermediate probability assignments for at least 14 individual wolves (Table 3).

Table 3

Assignment probabilities of the eastern and western wolves and dogs to the 2 clusters (W and D) identified using Bayesian inference

 Cluster Wolves (w) Dogs (d) 
Empirical 0.95 0.598 0.606 
 0.9 0.755 0.758 
 0.8 0.824 0.848 
 0.7 0.873 0.909 
 0.6 0.882 0.955 
 0.5 0.902 0.970 
 Means 0.859 0.901 
Corrected average 0.95 0.678±0.005 0.695±0.008 
 0.9 0.795±0.004 0.810±0.006 
 0.8 0.843±0.003 0.878±0.005 
 0.7 0.833±0.002 0.928±0.004 
 0.6 0.891±0.002 0.970±0.003 
 0.5 0.908±0.002 0.981±0.003 
 Cluster Wolves (w) Dogs (d) 
Empirical 0.95 0.598 0.606 
 0.9 0.755 0.758 
 0.8 0.824 0.848 
 0.7 0.873 0.909 
 0.6 0.882 0.955 
 0.5 0.902 0.970 
 Means 0.859 0.901 
Corrected average 0.95 0.678±0.005 0.695±0.008 
 0.9 0.795±0.004 0.810±0.006 
 0.8 0.843±0.003 0.878±0.005 
 0.7 0.833±0.002 0.928±0.004 
 0.6 0.891±0.002 0.970±0.003 
 0.5 0.908±0.002 0.981±0.003 

The proportion of the individuals is shown which assignment probability to a cluster n equal or exceeding: 0.9, 0.8, 0.5, 0.2, and means of posterior distribution. Lower panel (corrected average) shows respective figures corrected using simulated purebred genotypes (see text for details), arithmetic mean ± standard error for 20 simulations.

Bayesian inference of first-generation hybrids with software BayesAss 3.0 showed high convergence among the different run outputs and consistent migration estimates and individual probabilities. At least 3 out of 102 wolves were shown to be first-generation hybrids (=second-generation migrants) with a probability of 0.641, 0.875, and 0.908, respectively. Conversely, 2 out of 66 dogs were likely first-generation hybrids, with probabilities of 0.619 and 0.811. These individuals were the same as showing intermediate assignment probabilities with the STRUCTURE algorithm.

Relatedness Among the Individuals

The average Lynch and Ritland’s rxy was 0.29 for the entire dataset, 0.26 when comparing individual Georgian wolves with individual dogs, and 0.33 when comparing the wolves with wolves and the dogs with dogs. About 9.2% of the wolves had average rxy higher when compared with dogs than when compared with the other wolves. About 20.6% of the dogs had average rxy higher when compared with the wolves than when compared with other dogs, suggesting that the dogs with detectable close relations with wolves are more than twice as many as the wolves with the detectable close relations with the dogs.

Discussion

Gene flow among wolf and dog populations in the Caucasus is substantial and exceeds that recorded for the studied European populations. This includes both gene flow from dog to wolf population and inverse gene flow from wolves to shepherd dogs. The distribution of maternal lineages suggests that the gene introgression both from wolf to dog and vice versa had a remarkable impact on the gene pools of both populations.

Introgression of mt-DNA Between Wolf and Dog in the Caucasus

Domestic dogs have occurred throughout the entire mainland Eurasia since at least 12000 YA (Larson et al. 2012). Most of the previous molecular genetic studies (Savolainen et al. 2002; Brown et al. 2011) supported an earlier hypothesis on the East Asian origin of dogs (Olsen 1985) by showing that maternal haplogroups, which prevail in domestic dogs, are most diverse in East Asian wolves. A recent mt-DNA study of dogs from SW Asia suggests that only 2.7% of the studied individuals had mt-DNA, presumably associated with Western rather than Eastern Eurasian wolves (Ardalan et al. 2011). The authors hypothesize that low sharing of haplotypes is due to mt-DNA introgression rather than to independent wolf domestication in SW Asia. However, the “SE Asian domestication hypothesis” (SE: Southeast) is challenged by Larson et al. (2012), who suggest that, while testing alternative hypotheses, complexity of the post-domestication evolutionary pattern is underestimated. Prehistorically and in early historical time, geographic distribution of dog lineages appears to have undergone substantial changes, as is evident, for instance, from ancient dog mt-DNA studies (Deguilloux et al. 2009); dog populations went recently through tight bottlenecks, which supposedly impoverished the gene pool of domestic dog breeds 100–150 years ago (Larson et al. 2012). Sacks et al. (2013) revised the existent views on wolf domestication, based on the analysis of Y-chromosome DNA haplogroups of dogs from throughout most of the current range. They showed that multiple domestication centers of wolves did exist throughout Eurasia, but the morphologies best adapted to human needs prevailed in SE Asia, which triggered rapid expansion of their maternal lineages. The most inclusive recent analysis of ancient Canid mitochondrial genomes (Thalmann et al. 2013) suggests that all clades, which present either in modern dogs or in fossilized dog remains from Europe, are shared with modern or ancient European wolves; this study obviously challenges the “SE Asian hypothesis”.

Our data suggest that at least 17% of Georgian shepherd dogs had mt-DNA from clade B (Savolainen et al. 2002). This clade dominated in fossil European dog remains (Deguilloux et al. 2009). Savolainen et al. (2002) hypothesized an East Asian origin of this clade, based on the analysis of its internal diversity. However, they simultaneously mentioned that the clade was found in Western and not Eastern Eurasian wolves. Thalmann et al. (2013) suggest that the clade B represents a mitochondrial genome introgressed into dog gene pool from European wolves rather than one established by domestication. Our analysis of multiple published haplotypes suggests that this clade is one of the most common among European wolves, and the haplotype of this clade especially common in Europe is also common in Georgian wolves and shepherd dogs. This finding can be taken as an argument for the local maternal ancestry of a substantial part of the studied shepherd dogs.

Most of the Georgian dogs were associated with the presumably East Asian (Savolainen et al. 2002; but see Thalmann et al. 2013) clade A. However, 3 haplotypes of this clade, found in dogs, were also present in 4 wolves (all identified as wolves by viewing the pelts), which may reflect gene flow between the populations. About 68% of the studied wolves had haplotypes clustered in a group with no dog representatives (neither those from our sample, nor from the bibliography) but shared with Eurasian and North American wolves.

Therefore, Georgian shepherd dogs combine mt-DNA lineages typical for ancient and East Asian dogs with the lineages that are shared with Caucasian wolves. This can indicate very important contribution of local wolves into the gene pool of Caucasian shepherd dogs.

Gene Flow Rates and Consequences for the Wolf and Dog Gene Pools

If effective migration rates Nm >1, 2 populations cannot maintain differences solely by gene flow (Wright 1951). In the case of domestic versus wild population of the same species, domestic breeds are likely to be maintained by artificial selection against hybrid individuals with potentially undesirable morphology or behavior. At the same time, morpho-ecological characters of the wild forms are maintained by natural selection against those that may decline reproductive success in the wild. Therefore, the situation can be conceptually similar to a semipermeable hybrid zone (sensuArnold 2006), with neutral genes flowing across the boundary between the hybridizing populations but nonneutral genes remaining separated. The gene flow is substantial and can lead to rethinking both dog and wolf phylogeography in the Caucasus and, possibly, West Asia, since Traditional F-statistic estimation suggests Nm >3 between the dogs and wolves. STRUCTURE simulations predict the presence of recent wolf ancestry in more than 10% of shepherd dogs and recent dog ancestry in more than 13% of wolves. Moreover, 2% of the studied wolves and 3% of dogs were, with a high probability, first-generation hybrids. According to the Lynch and Ritland’s relatedness index, 21% of dogs had closer average relatedness with wolves than with other dogs, and 9% of wolves had closer average relatedness with dogs than with other wolves. In summary, all analyses applied suggest that the gene flow between dog and wolf populations in Georgia is sufficiently high to cause gene introgression. Gene flow from dogs to wolves remarkably exceeds that identified for some regions of southern Europe (Randi and Lucchini 2002; Verardi et al. 2006).

The majority of publications describing dog–wolf hybridization suggest that mating between male dogs and female wolves is more common than the other way around (Godinho et al. 2011; Hindrikson et al. 2012). However, genetic analyses suggest that hybridization between male wolves and female dogs also occurs in nature (Hindrikson et al. 2012). The presence of dog maternal lineages in wolf populations (Ardalan et al. 2011; this study) or sharing haplotypes between wolves and dogs can only be the result of such hybridization pattern: Female dogs can produce offspring both in the wild and in domestic conditions, whereas female wolves can breed mostly in the wild. Our study supports this point of view. Hybridization between male wolves and female dogs might happen both occasionally and deliberately: In mountain parts of Georgia, dogs are occasionally paired with captured wolves, which allegedly “improves the breed.” In such deliberate hybrid occasions, both male and female wolves can participate. The latter case is the most likely explanation of shared haplotypes between dog and wolf.

Throughout Western Europe, where previous wolf–dog hybridization studies were located, the dogs are largely controlled by humans, which prevent their hybridization with wolves (in areas where wild wolf populations still exists), although the proportion of wolves with hybrid ancestry exceeds 4% when feral dogs are relatively common (Randi 2008; Godinho et al. 2011). However, these studies were not designed to evaluate mixed ancestry in feral dog population. Large livestock guarding dogs, such as Great Pyrenees, are not commonly used any more in a way that they can easily interbreed with wolves, but nobody can say to what extent they interbred with wolves in the past. Larson et al. (2012) suggested that the current gene pool of most dog breeds has been formed very recently, not more than 100–150 years ago. In that time, the breeds were subjected to extensive selection and most likely passed through narrow bottlenecks (Larson et al. 2012). The current distribution of clonally inherited genes in dogs, showing strong dominance of East Asian mt-DNA haplogroups, should be viewed in this context. We hypothesize that the situation was much more flexible in earlier times, when most of the dogs used by common people were not subjected to intensive selection, similar to what is now the situation with livestock guarding dogs in the Caucasus and most likely the rest of West Asia. This means that interbreeding with gray wolves was an important part of the dog maintenance, and the situation was much more complicated than the simple pattern including Neolithic domestication with the further expansion of dogs descending from these early domesticated wolves.

Concluding Remarks

Gene flow between dog and wolf populations might have substantially influenced the gene pool of both since a very early period of domestication. In Europe, gene flow between dogs and wolves could be remarkable until large livestock guarding dogs were used for sheep protection and wolves had large continuous range. High rates of gene flow suggest that the gene pool of Western Eurasian wolves could integrate numerous dog maternal lineages. Phylogeographic analysis of both Eurasian wolf and dog should consider this introgression, which could result in biased interpretations.

Supplementary Material

Supplementary material can be found at http://www.jhered.oxfordjournals.org/.

Funding

Georgian National Science Foundation (award no. GNSF/ST07/6–230); Ilia State University budget.

Acknowledgements

The authors thank Gigi Tevzadze for his moral and administrative support and help during the field work. Lisette Waits and Cort Anderson supervised T.Q. during her training at the Molecular Genetic Laboratory of the University of Idaho. Mari Murtskhvaladze provided assistance to M.S. and T.Q. at the molecular genetic laboratory at Ilia State University. Two anonymous referees and R. Wayne provided useful suggestions on the first draft of the manuscript. Alexander Gavashelishvili and one of the 2 anonymous referees helped to correct English of the MS.

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Author notes

Corresponding Editor: Robert Wayne