A total of 84 goat blood samples were collected and analysed in this study, which included Damascus goats, also known as Shami goats (DMS, 20); Kilis goats (KLS, 21), Hatay goats, also known as Yayladağ goats (HTY, 22); and Kil goats, also known as Hair goats (KIL, 21). The animal materials for this study were sourced from flocks located in the Hatay Province (including Damascus, Hatay, and Kil goats) and Kilis Province (Kilis goats) as part of the “National Breeding Project under Farm Conditions” coordinated by the Republic of Türkiye's Ministry of Agriculture and Forestry, General Directorate of Agricultural Research and Policies (TAGEM). For each breed, animals exhibiting typical phenotypic characteristics representative of that breed were selected, with careful consideration given to ensuring that the animals were unrelated. Blood samples were collected from the jugular vein of each goat using vacuum tubes containing the anticoagulant ethylenediaminetetraacetic acid (EDTA) (BD Vacutainer Systems, Plymouth, UK), with approximately 9 mL of blood obtained from each animal. The samples were then transported to the laboratory under cold chain conditions to preserve their integrity and were stored at -20 °C until further analysis. This study was conducted in accordance with the approval of the Aydin Adnan Menderes University Animal Experiments Local Ethics Committee, dated 18 May 2023, and numbered 64583101/2023/79.

In this study, genomic DNA was extracted from white blood cells using the high-salt method described by Montgomery and Sise (1990) and Miller et al. (1988), followed by purification. The hypervariable region I (HV-1) of the mitochondrial DNA (mtDNA) D-loop was amplified and sequenced in 84 goats sampled from Türkiye, which is part of the Fertile Crescent. A 598-base pair (bp) fragment, corresponding to positions 15.653 to 16.250 of the goat mitochondrial genome, was successfully amplified using specific primers: forward (CGTGTATGCAAGTACATTAC) and reverse (CTGATTAGTCATTAGTCCATC) (Naderi et al., 2007; Parma et al., 2003). The primer sequences were obtained from the National Center for Biotechnology Information (NCBI) GenBank database (Accession number AF533441.1) to ensure precise targeting of the desired genomic region (Naderi et al., 2007). The PCR reaction mixture comprised 1X PCR buffer, 1.5 mM MgCl₂, 200 µM of each deoxynucleotide triphosphate (dNTP), 1 µM of both forward and reverse primers, 1 unit of Taq DNA polymerase (Thermo Scientific, USA), and approximately 100 ng of genomic DNA, resulting in a total volume of 30 µL. The amplification process commenced with an initial denaturation at 95 °C for 5 min, followed by a specific number of cycles tailored to the gene of interest. Each cycle consisted of denaturation at 94 °C for 40 s, annealing at 55 °C for 40 s, and extension at 72 °C for 1 min. A final extension at 72 °C for 5 min ensured complete amplification of the PCR products. The amplification was performed using a Bio-Rad C1000 Touch™ Thermal Cycler. To verify the PCR products, the amplified samples were subjected to electrophoresis on a 1.5 % agarose gel, using a 100 bp DNA ladder (Thermo Scientific, 50 bp O'Gene Ruler, USA) as a molecular size marker. DNA sequencing was conducted under a service contract with a reputable provider (Macrogen Inc., Seoul, Republic of Korea), ensuring reliable and high-quality results. The sequences of distinct haplotypes for each goat breed and population have been deposited in the National Center for Biotechnology Information (NCBI) GenBank, with accession numbers ranging from PV068375 to PV068458.

Nucleotide sequence editing was performed manually using BioEdit v7.2.6 (Hall, 1999), focusing on the removal of ambiguous base calls and low-quality chromatogram regions. Sequences obtained in this study were aligned alongside reference sequences from six known goat mitochondrial haplogroups and outgroup species using the ClustalW algorithm implemented in MEGA 11 (Tamura et al., 2021). Following alignment, trimming was performed in MEGA 11 to remove poorly aligned and non-overlapping end regions, resulting in a final consensus sequence of 453 bp within a 598 bp fragment. This consensus region was defined based on the overlap among all sequences, including the reference and outgroup sequences, to ensure comparability in subsequent phylogenetic and haplotype network analyses. Mitochondrial DNA diversity parameters, including the number of haplotypes (H), haplotype diversity (Hd), nucleotide diversity (π), and the number of polymorphic and parsimony-informative sites were estimated for each population and across all populations using DnaSP v6 (Rozas et al., 2017). Reference sequences from the six known goats mitochondrial haplogroups (GenBank accession numbers: AY155721, EF618134 for Haplogroup A; EF617706 for Haplogroup B1; DQ121578 for Haplogroup B2; EF618413, DQ188892 for Haplogroup C; EF617701, DQ188893 for Haplogroup D; DQ241349 for Haplogroup F; EF618535, EF617727, EF618084 for Haplogroup G) were incorporated alongside the sequences obtained in this study. Additionally, sequences from Capra aegagrus (GenBank accession numbers: AJ317864, EF989163, EF989426, EF989577), Capra caucasica (AJ317875), Capra sibirica (AJ317874), Capra cylindricornis (AJ317870), Capra ibex nubiana (AJ317871), and Capra falconeri (AJ317872) were included as outgroups. These sequences were utilized to construct a neighbour-joining (NJ) tree in MEGA 11 (Tamura et al., 2021) using the HKY85+G model (as determined by SMS in PhyML), with 1000 bootstrap replicates, and pairwise deletion for gaps/missing data treatment. A median-joining (MJ) network of mtDNA haplotypes was constructed using NETWORK 10.2 (Bandelt et al., 1999), with a default epsilon value (ε=0), transitions and transversions equally weighted, and median vectors inferred automatically to illustrate the genealogical relationships among haplotypes. The best-fitting nucleotide substitution model was identified using the Smart Model Selection (SMS) algorithm (Lefort et al., 2017) implemented in PhyML 3.0 (Guindon et al., 2010), based on the small-sample-corrected Akaike information criterion (AICc) (Akaike, 1979). This model estimation was performed exclusively to inform the neighbour-joining (NJ) tree construction in MEGA 11. The resulting phylogenetic tree was visualized using FigTree 1.4.4 (Rambaut, 2018). Mitochondrial DNA neutrality tests – including Tajima's D, Fu's Fs, and Fu and Li's D and F statistics – were performed using DnaSP v6 (Rozas et al., 2017). All tests were conducted on the full 453 bp consensus region, using the complete dataset that combined all sampled populations. Statistical significance was evaluated through 10 000 coalescent simulations under the standard neutral model. The genetic differentiation among populations was quantified using Nei's (1972) distance matrix, which was computed in MEGA 11 (Tamura et al., 2021), and subsequently visualized through phylogenetic network analysis in SplitsTree (Hudson and Bryant, 2006).

We sequenced and analysed hypervariable region 1 of the mitochondrial DNA D-loop in a total of 84 goats representing four breed populations. The nucleotide frequencies were calculated as follows: 30.93 % adenine (A), 30.28 % thymine (T), 22.26 % cytosine (C), and 16.52 % guanine (G). The overall transition / transversion bias (R) was estimated to be approximately 76.48, which is higher than the value reported for goat breeds raised in Türkiye (R=63.74) by Kul (2010). Comparative studies have documented transition / transversion ratios R=40 (Wu et al., 2012) and R=36.47 (Chen et al., 2005) for indigenous Chinese goats, R=25 for Vietnam goats, R=28 for Tanzanian goats (Nguluma et al., 2021), and R=17 for indigenous Indian goats (Joshi et al., 2004). The value obtained in this study is higher than values reported for goat breeds raised in Asia. The elevated transition / transversion ratio observed can be attributed to the breeding region of the analysed goat breeds being part of the Fertile Crescent. This finding is particularly important as it highlights the accumulation of mutations within these populations. The bias (R) value obtained in this study reflects the mitochondrial DNA D-loop region's tendency to maintain a high transition rate, independent of natural selection, thereby underscoring its evolutionary significance.

As indicated in Table 1, haplotype diversity (Hd) was observed across all breeds, ranging from 0.944±0.028 (HTY) to 0.989±0.019 (DMS). The overall mean haplotype diversity was 0.993±0.003, indicating a significant level of genetic variation within the populations. In terms of haplotype diversity, the average values obtained in this study were comparable to those reported by Kul (2010) for domestic goat breeds in Türkiye (Hd = 0.998±0.0006) and by Akis et al. (2014). However, our values are higher than those reported by Kiraz (2009) (Hd = 0.706±0.019) but lower than the data reported by Çakmak (2019) and Kiraz et al. (2024), who found Hd = 1.000. The results obtained in this study are comparable to those reported by Sardina et al. (2006) for the native Sicilian goat breed (Hd = 0.969±0.007). Furthermore, our findings align with those of several other studies, including Zhao et al. (2014) for Chinese native goat breeds (Hd = 0.980±0.018), Hoda et al. (2014) for Albanian goats (Hd = 0.996), Deng et al. (2017) for Tibetan goats (Hd = 0.990±0.003), Diwedi et al. (2020) for Indian native goats (Hd = 0.998±0.001), and Nguluma et al. (2021) for Tanzanian goats (Hd = 0.9945±0.001). These similarities indicate a relatively high level of haplotype diversity across different goat populations, suggesting substantial genetic variation within these breeds.

Nucleotide diversity (π) values range from 0.01734±0.00109 in KIL to 0.02635±0.00336 in DMS, with an average of 0.02091±0.00142, as shown in Table 1. These moderate nucleotide diversity levels indicate that the populations are genetically diverse; however, there may be some degree of shared ancestry or recent divergence. The relatively higher nucleotide diversity observed in DMS aligns with its greater haplotype diversity, further reinforcing the unique genetic structure of this population. When evaluating nucleotide diversity, the results obtained in this study (π=0.02091±0.00142) align closely with those reported by Kul (2010) for native goat breeds in Türkiye (π=0.02100±0.00073). However, our findings are higher than those of Kiraz (2009) (π=0.00226±0.00011) and Çakmak (2019) (π=0.01601±0.00006). The nucleotide diversity observed in this study is lower compared to the values reported by Kiraz et al. (2024) for Mahalli goats (π=0.0375±0.00209), Nguyen et al. (2022) for Chinese native goats (π=0.03005±0.00000008), Diwedi et al. (2020) for Indian native goats (π=0.028±0.001), Hoda et al. (2014) for Albanian goats (π=0.036), and Zhao et al. (2014) for China local goat breeds (π=0.0336±0.0008). Conversely, our results demonstrate higher nucleotide diversity than those reported for various populations, including Baenyi-Simon et al. (2022) for Congolese goats (π=0.015±0.003), Nguluma et al. (2021) for Tanzanian goats (π=0.0139±0.046), Deniskova et al. (2020) for Russian goats (π=0.01478±0.00297), Deng et al. (2017) for Tibetan goats (π=0.0145±0.0013), and Kamalakkannan et al. (2018) for Indian native goats (π=0.0088±0.0048 to π=0.0170±0.0088). These comparisons indicate varying levels of nucleotide diversity across different goat populations, reflecting differences in genetic variability and evolutionary history.

The analysis of the aligned combined dataset revealed polymorphism, with 95 polymorphic sites, including 64 parsimony-informative positions. Among the breeds, the DMS goat exhibited the highest number of polymorphic regions (54), while the HTY breed showed the lowest (38). In terms of parsimony-informative sites, the DMS breed also had the highest count (35), whereas the KIL breed had the lowest (20). These findings align with those of Diwedi et al. (2020) but are lower than those reported by Kul (2010) and higher than those observed by Çakmak (2019). The higher number of both polymorphic and parsimony-informative sites in the DMS breed suggests a greater level of genetic variation, which may indicate unique evolutionary characteristics within this population compared to the others.

A total of 65 haplotypes were identified across four goat breeds, consisting of 84 individuals, with 51 of these haplotypes being unique. In haplogroup A, 47 animals exhibited a unique haplotype composition, while haplogroup G contained only 4 animals with unique haplotypes. The proportion of unique haplotypes was highest in the KLS breed (88.9 %), followed by DMS (83.3 %), KIL (76.5 %), and HTY (53.8 %). Upon examining the analysis results, it is evident that two or more animals share the same haplotype, as shown in Table 2. The sharing of haplotypes among multiple individuals within the same breed of HTY goats explains the relatively lower haplotype diversity observed in this breed compared to others. Additionally, it was observed that Haplotype 13 (Hap_13) is shared between individuals from different breeds, specifically between DMS_16, KLS_7, KLS_15, and KLS_16.

In this study, the major mitochondrial DNA haplogroups of goats (A, B, C, D, F, and G) and outgroups were identified and used to construct a neighbour-joining (NJ) tree, which is presented in Fig. 1. The analysis of genetic relationships between the studied goats and reference haplogroups revealed that the breeds are classified into two distinct haplogroups: A and G. The distribution of haplogroups indicated that Haplogroup A comprised the majority, encompassing 80 individuals and 47 haplotypes (95.2 %), while Haplogroup G was represented by only 4 individuals with 4 haplotypes (4.8 %). Figure 2 presents the distribution of haplogroups among breeds and their geographic locations.

The predominance of Haplogroup A observed in this study is consistent with findings reported in the literature (Masila et al., 2024; Bherey et al., 2023; Guo et al., 2022; Baenyi-Simon et al., 2022; Deniskova et al., 2020; Ganbold et al., 2020; Diwedi et al., 2020; Tabata et al., 2019; Ahmed et al., 2016; Zhao et al., 2011; Kul, 2010; Kiraz, 2009; Naderi et al., 2007; Sardina et al., 2006). Similarly, the low frequency of Haplogroup G within the studied populations aligns with previous reports (Deniskova et al., 2020; Kibegwa et al., 2016; Kiraz, 2009; Naderi et al., 2007). Naderi et al. (2007) reported that among 66 goats sampled in Türkiye, 61 belonged to Haplogroup A, while 5 were classified as Haplogroup G. Furthermore, this study was the first to document the presence of Haplogroup G in goat populations from Iran, Saudi Arabia, and Türkiye (specifically among Georgian goats), with an overall frequency of approximately 1 %.

This study represents the first report of Haplogroup G in the DMS goat population in Türkiye, with two individuals classified under this haplogroup, as shown in Table 3. In total, 18 goats from the DMS population were assigned to Haplogroup A. Previous research on DMS goats from Egypt (Othman and Mahfouz, 2016) and Iraq (Al-Abbasi, 2019) has consistently shown that these populations predominantly belong to Haplogroup A. A recent study by Bherey et al. (2023) also identified Haplogroup A as the dominant haplogroup in DMS goats; however, they additionally reported one individual carrying Haplogroup F. Notably, the discovery of Haplogroup G in DMS goats from Türkiye provides new insights into the genetic diversity of this population, highlighting a previously unrecognized aspect of their mitochondrial DNA. In the KLS goat population, 20 individuals were classified under Haplogroup A, while 1 individual was assigned to Haplogroup G. These findings are consistent with previous studies (Kul, 2010; Kiraz, 2009). However, other studies have also identified the presence of Haplogroup D in addition to Haplogroups A and G in Kilis goats (Akis et al., 2014; Kul, 2010). For the first time, this study includes the HV1 mtDNA D-loop region in HTY goats, with 21 individuals classified under Haplogroup A and one individual assigned to Haplogroup G. This marks a significant contribution to the genetic understanding of the HTY goat population. All KIL goats in the present study were assigned to Haplogroup A, which aligns with the findings of previous studies (Çakmak, 2019; Kul, 2010). Also, Kiraz (2009) reported that 15 KIL goats belonged to Haplogroup A, while 1 individual was classified under Haplogroup G.

The median-joining network in constructed by integrating the sequence results of this study with other reference sequences, demonstrating consistency with the previously generated neighbour-joining tree. Figure 3 shows that haplogroups A, B, D, and G are clearly distinguished from each other. The cluster of outgroups (Capra aegagrus, Capra caucasica, Capra sibirica, Capra cylindricornis, Capra ibex nubiana, Capra falconeri) and Haplogroup C and Haplogroup F together formed an isolated group on the left side of the median-joining network tree. Large, clustered centres generally represent more common haplogroups, while more distant and isolated clusters represent more rare or specific haplogroups.

The comparison of DNA sequences within and between populations is a valuable tool for determining the evolutionary factors influencing specific gene regions. It also provides important insights into the species' evolutionary history (Ramos-Onsins and Rozas, 2002). The neutral theory, initially proposed by Motoo Kimura (Kimura, 1968), offers a foundational framework for interpreting DNA variation through well-defined, testable hypotheses. It also provides a suite of statistical tools designed to differentiate the effects of natural selection from those of random genetic drift (Hahn, 2007; Nielsen, 2001; Kreitman, 2000). Tajima's D test (Vitti et al., 2013; Tajima, 1989) is the most widely used general-purpose neutrality test in non-recombinant sequences. Tajima's D is widely regarded as the most effective test in detecting selective sweeps, population bottlenecks, and population subdivision (Simonsen et al., 1995). Tajima's D test is most optimal for detecting an excess of intermediate-frequency alleles and a deficit of low-frequency alleles, whereas Fu and Li's D test is highly sensitive to the presence of very rare alleles (Ferretti et al., 2010). The results of various neutrality tests, including Tajima's D, Fu's Fs, Fu and Li's D, and Fu and Li's F, conducted for the analysed populations are summarized in Table 4. Due to the small number of animals within each haplogroup, neutrality tests were not conducted at the haplogroup level but were instead analysed at the breed level and for the overall population.

Tajima's D values and Fu's Fs estimates for all goat populations were negative and statistically non-significant. The negative values observed in Tajima's D test for all breeds indicate that these populations have not deviated from neutral population values. In general, the higher-than-expected number of rare alleles at low frequencies could suggest a potential population expansion or the influence of negative selection pressure. Similarly, the negative values observed in Fu's Fs test suggest the presence of population expansion or selective pressure. Particularly, the very high negative value (-59.074) for the general population indicates that these processes are highly pronounced across the populations. When examining Fu and Li's D and F statistics in the DMS, KLS, and KIL populations, the negative values indicate the presence of rare alleles and suggest the influence of population expansion or negative selection. However, the positive values of Fu and Li's D and F in the HTY population point to a scarcity of rare alleles and suggest the potential mild effects of a population bottleneck.

The genetic distance between populations was computed using Nei's (1972) distance matrix (Table 5), and the resulting dendrogram is shown in Fig. 4.

The DMS goat population is positioned at the greatest genetic distance from the KLS, HTY, and KIL goat populations. The populations that are closest to each other are HTY and KIL, followed by KLS. Previous studies have reported that the KLS population emerged as a result of uncontrolled crossbreeding between DMS and KIL goats by local breeders aiming to increase milk and meat production (Gündüz and Biçer, 2023; Gül et al., 2020; Keskin, 2002). According to the dendrogram, the KIL and HTY goat populations are closely related. The proximity of the HTY goat genotype, resulting from the crossbreeding of KLS and KIL goats (Gül, 2008; Biçer et al., 2005; Kaya, 1999; Keskin and Biçer, 1997), to the local KIL population is expected.