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<article class="scholarly-article">
<h2>Introduction</h2>
<p>Indigenous livestock breeds in Africa are reservoirs of genetic diversity adapted to harsh environments, including drought, poor nutrition, and disease pressure (Monau et al., 2020). Goats (Capra hircus) are particularly important for smallholder farmers in Southern Africa, providing meat, milk, fiber, and socio-economic benefits (Mwai et al., 2015). However, these populations face threats from crossbreeding with exotic breeds, climate change, and shifting production systems (Muluneh, 2021). Understanding their genetic diversity is essential for conservation and sustainable utilization.</p><p>Microsatellite markers are widely used for genetic characterization due to their high polymorphism, codominant inheritance, and reproducibility (Chenyambuga et al., 2004). They have been applied to assess diversity in goats from various regions, including Asia (Wang et al., 2011; Zein et al., 2012), Africa (Chenyambuga et al., 2004; Okpeku et al., 2011), and Europe (Moutchou et al., 2018). In Southern Africa, studies on goats are limited, although indigenous breeds such as Tswana, Landim, and Savannah are known to possess adaptive traits (Monau et al., 2020).</p><p>This study aimed to characterize the genetic diversity and population structure of five indigenous goat populations from Southern Africa using 20 microsatellite markers. The specific objectives were to: (1) estimate allele frequencies and genetic diversity parameters, (2) assess population differentiation, and (3) infer population structure and admixture patterns.</p>
<h2>Literature Review</h2>
<p>Domestication of goats occurred approximately 10,000 years ago in the Fertile Crescent, followed by dispersal into Africa via multiple migration routes (Gifford–Gonzalez & Hanotte, 2011; Colli et al., 2018). African indigenous goats exhibit high phenotypic diversity, reflecting adaptation to diverse agro-ecological zones (Chenyambuga et al., 2004). Microsatellite-based studies have revealed significant genetic diversity in African goat populations. For instance, Chenyambuga et al. (2004) analyzed goats from sub-Saharan Africa and found high heterozygosity (He > 0.60) and moderate differentiation among populations. Similarly, Okpeku et al. (2011) reported substantial diversity in Nigerian goats.</p><p>In Southern Africa, Monau et al. (2020) reviewed the sustainable utilization of indigenous goats, emphasizing the need for genetic characterization. The Boer goat, originally from South Africa, has been globally recognized for meat production, but its genetic relationship with other indigenous breeds is not fully understood (Colli et al., 2018). The Kalahari Red and Savannah are also important South African breeds, while Tswana (Botswana) and Landim (Mozambique) are adapted to local conditions (Monau et al., 2020).</p><p>Microsatellite markers have been successfully used in other livestock species to assess genetic diversity, such as cattle (Bora et al., 2023; Makina et al., 2014), sheep (Mihailova, 2021), pigs (Šalamon et al., 2019), and chickens (Eltanany et al., 2011). These studies underscore the utility of microsatellites for conservation genetics.</p>
<h2>Methodology</h2>
<h4>Sample collection and DNA extraction</h4><p>Blood samples were collected from 250 unrelated indigenous goats representing five populations: Boer (n=50), Savannah (n=50), Kalahari Red (n=50), Tswana (n=50), and Landim (n=50). Sampling was conducted in Botswana, South Africa, and Mozambique between January and June 2023. Animals were selected from different herds to minimize relatedness. Genomic DNA was extracted using a standard phenol-chloroform protocol. DNA quality and concentration were assessed by agarose gel electrophoresis and spectrophotometry.</p><h4>Microsatellite genotyping</h4><p>Twenty microsatellite markers recommended by the Food and Agriculture Organization (FAO) for goat diversity studies were selected: INRA006, OarFCB20, ILSTS005, ILSTS011, ILSTS022, ILSTS033, ILSTS034, ILSTS049, ILSTS054, ILSTS058, ILSTS059, ILSTS065, ILSTS068, ILSTS082, ILSTS087, ILSTS093, ILSTS099, ILSTS102, MAF209, and McM527. Forward primers were labeled with fluorescent dyes (FAM, HEX, NED). PCR amplifications were performed in 15 μL reactions containing 50 ng DNA, 1× PCR buffer, 1.5 mM MgCl2, 0.2 mM dNTPs, 0.2 μM each primer, and 0.5 U Taq polymerase. Thermocycling conditions: initial denaturation at 94°C for 5 min; 35 cycles of 94°C for 30 s, 55°C for 45 s, 72°C for 45 s; final extension at 72°C for 10 min. Fragment analysis was performed on an ABI 3730xl DNA analyzer, and allele sizes were determined using GeneMapper v5.0.</p><h4>Data analysis</h4><p>Genetic diversity parameters including number of alleles (Na), effective number of alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He), and inbreeding coefficient (Fis) were calculated using GenAlEx v6.5. Hardy-Weinberg equilibrium (HWE) was tested using exact tests in GENEPOP v4.7. Genetic differentiation was assessed using pairwise Fst (Weir & Cockerham) and analysis of molecular variance (AMOVA) in GenAlEx. Population structure was inferred using STRUCTURE v2.3.4 with admixture model and correlated allele frequencies. Twenty independent runs were performed for K=1 to K=8 with 100,000 burn-in and 500,000 MCMC iterations. The optimal K was determined using the Evanno method. Principal coordinate analysis (PCoA) was conducted in GenAlEx.</p>
<h2>Results</h2>
<h4>Genetic diversity</h4><p>All 20 microsatellite markers were polymorphic across the five populations. A total of 198 alleles were detected, with a mean of 9.9 alleles per locus. The mean number of alleles per population ranged from 6.2 (Tswana) to 8.4 (Boer). Table 1 summarizes genetic diversity parameters for each population.</p><figure class="table-figure"><table><thead><tr><th>Population</th><th>N</th><th>Na</th><th>Ne</th><th>Ho</th><th>He</th><th>Fis</th></tr></thead><tbody><tr><td>Boer</td><td>50</td><td>8.4</td><td>4.12</td><td>0.69</td><td>0.74</td><td>0.07</td></tr><tr><td>Savannah</td><td>50</td><td>7.6</td><td>3.85</td><td>0.65</td><td>0.71</td><td>0.09</td></tr><tr><td>Kalahari Red</td><td>50</td><td>7.2</td><td>3.68</td><td>0.63</td><td>0.69</td><td>0.09</td></tr><tr><td>Tswana</td><td>50</td><td>6.2</td><td>3.21</td><td>0.58</td><td>0.65</td><td>0.11</td></tr><tr><td>Landim</td><td>50</td><td>6.8</td><td>3.45</td><td>0.61</td><td>0.68</td><td>0.10</td></tr><tr><td>Overall</td><td>250</td><td>7.24</td><td>3.66</td><td>0.63</td><td>0.69</td><td>0.09</td></tr></tbody></table><figcaption>Table 1. Genetic diversity parameters for five indigenous goat populations. N: sample size; Na: mean number of alleles; Ne: effective number of alleles; Ho: observed heterozygosity; He: expected heterozygosity; Fis: inbreeding coefficient.</figcaption></figure><p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/genetic-diversity-of-indigenous-goats-in-southern-africa-using-microsatellite-markers-0ac5x/figure-1-1779954187980.octet-stream" alt="bar chart of observed and expected heterozygosity across five goat populations" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 1. bar chart of observed and expected heterozygosity across five goat populations</figcaption></figure></p><p>Observed heterozygosity was lower than expected heterozygosity in all populations, resulting in positive Fis values, indicating a slight deficit of heterozygotes. However, Fis values were low, suggesting minimal inbreeding. Tests for Hardy-Weinberg equilibrium revealed that 12 out of 100 population-locus combinations deviated significantly (P<0.05) after Bonferroni correction.</p><h4>Population differentiation</h4><p>AMOVA revealed that 10% of the total genetic variation was among populations, while 90% was within populations. Pairwise Fst values are presented in Table 2.</p><figure class="table-figure"><table><thead><tr><th></th><th>Boer</th><th>Savannah</th><th>Kalahari Red</th><th>Tswana</th><th>Landim</th></tr></thead><tbody><tr><td>Boer</td><td>0.00</td><td>0.09</td><td>0.10</td><td>0.13</td><td>0.16</td></tr><tr><td>Savannah</td><td></td><td>0.00</td><td>0.05</td><td>0.11</td><td>0.14</td></tr><tr><td>Kalahari Red</td><td></td><td></td><td>0.00</td><td>0.10</td><td>0.13</td></tr><tr><td>Tswana</td><td></td><td></td><td></td><td>0.00</td><td>0.07</td></tr><tr><td>Landim</td><td></td><td></td><td></td><td></td><td>0.00</td></tr></tbody></table><figcaption>Table 2. Pairwise Fst values among five goat populations. All values were significant (P<0.001).</figcaption></figure><p>The highest differentiation was between Boer and Landim (Fst=0.16), while the lowest was between Savannah and Kalahari Red (Fst=0.05). PCoA (Figure 1) showed that the first two coordinates explained 58.3% and 21.7% of the variation, respectively, separating the populations into two clusters: Boer, Savannah, and Kalahari Red grouped together, while Tswana and Landim formed another cluster.</p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/genetic-diversity-of-indigenous-goats-in-southern-africa-using-microsatellite-markers-0ac5x/figure-2-1779954192586.octet-stream" alt="PCoA scatter plot of goat populations based on microsatellite data" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 2. PCoA scatter plot of goat populations based on microsatellite data</figcaption></figure><h4>Population structure</h4><p>STRUCTURE analysis indicated that the optimal K was 2, based on the Evanno method (ΔK=124.5). At K=2, the first cluster comprised Boer, Savannah, and Kalahari Red, while the second cluster comprised Tswana and Landim. Some admixture was observed, particularly in Savannah and Kalahari Red. At K=3, further substructure emerged, with Boer separating from Savannah and Kalahari Red, but the primary division remained between the two major groups. Figure 2 shows the STRUCTURE bar plot for K=2.</p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/genetic-diversity-of-indigenous-goats-in-southern-africa-using-microsatellite-markers-0ac5x/figure-3-1779954197922.octet-stream" alt="STRUCTURE bar plot for K=2 showing population assignment" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 3. STRUCTURE bar plot for K=2 showing population assignment</figcaption></figure>
<h2>Discussion</h2>
<p>The microsatellite markers revealed substantial genetic diversity in Southern African indigenous goats, with overall expected heterozygosity (He=0.69) comparable to or higher than values reported for other African goat populations. Chenyambuga et al. (2004) reported He ranging from 0.58 to 0.71 in sub-Saharan African goats, while Okpeku et al. (2011) found He=0.67 in Nigerian goats. The high diversity likely reflects the large effective population sizes and historical admixture.</p><p>The Boer goat exhibited the highest diversity (He=0.74), consistent with its widespread use and breeding history. In contrast, Tswana had the lowest diversity (He=0.65), possibly due to smaller population size or isolation. The positive Fis values (0.07–0.11) suggest slight heterozygote deficiency, which may be due to population substructure or sampling of related individuals, though values were low and similar to those in other studies (Zein et al., 2012; Wang et al., 2011).</p><p>Genetic differentiation among populations was moderate (Fst=0.10), indicating gene flow or shared ancestry. The close relationship between Savannah and Kalahari Red (Fst=0.05) reflects their common origin from South African indigenous goats. Similarly, Tswana and Landim grouped together, likely due to geographic proximity and historical trade routes. The divergence of Boer from other populations (Fst up to 0.16) may be attributed to selective breeding for meat production.</p><p>Population structure analysis supported two main genetic clusters, aligning with geographic and phenotypic differences. The admixture observed in some individuals suggests ongoing gene flow. These findings are consistent with other studies on African livestock (Bora et al., 2023; Makina et al., 2014).</p><p>The results have implications for conservation. Indigenous goats are adapted to local conditions and possess unique alleles that may be valuable for future breeding programs (Monau et al., 2020). However, crossbreeding with exotic breeds could erode genetic diversity. Conservation strategies should prioritize maintaining purebred populations and managing gene flow.</p>
<h2>Conclusion</h2>
<p>This study provides the first comprehensive assessment of genetic diversity in five indigenous goat populations from Southern Africa using microsatellite markers. The populations harbor high genetic diversity, with moderate differentiation and clear population structure. The findings support the need for conservation programs to preserve these genetic resources. Future studies should incorporate more populations and genome-wide markers to refine our understanding. Additionally, phenotypic characterization should be integrated with genetic data to identify adaptive traits. Sustainable utilization of indigenous goats can contribute to food security and rural livelihoods in Southern Africa.</p>
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