1. Introduction
In several parts of developing countries including Ethiopia, livestock production is the major and achieving good living standards. From Africa, Ethiopia is ranked first and among the top 10 countries in the world in major farm animal populations [Reference Pocketbook1].
The livestock sector in our country has advantages for agricultural production, and domestic farm animals, in particular, are very significant, accounting for 30–40% of the food and agricultural sectors’ economic value. In Ethiopia, livestock contributes 45 percent of agricultural GDP, 18 percent of overall GDP, and 19 percent of export earnings [Reference Assefa and Hailu2]. There are diverse numbers of indigenous cattle breeds in Ethiopia. According to the Ethiopian Biodiversity Institute [Reference Ethiopian3] report, there are more than 28 native cattle breeds/ecotypes that have been recognized to exist in the country. Cattle diversity in our country is due to the prevalence of both Bos taurus and Bos indicus cattle in the country. Ethiopia is also well known for its varied climatic and topographic environments. This has contributed to several distinct cattle breeds that have evolved [Reference Mwai, Hanotte, Kwon and Cho4].
Despite the global economy’s commitment and diversity, there is a rapid depletion of farm animal genetic capital [Reference Abu5]. Among the factors, cross breeding, inbreeding, breed replacement, starvation, drought, and conflict are the factors which affects genetic diversity of indigenous cattle [Reference Edea, Dadi and Kim6].
As a result, understanding farm animal genetic diversity is needed to contribute to meeting current production needs in different settings, enable continued genetic development, or promote rapid adaptation to evolving environments (harsh environments), as well as to be used for breed selection based on their genetic contents for the dairy industry and beef industry [Reference Assefa and Hailu2].
Therefore, molecular characterization helps for effective conservation and long-term use strategies. Some phenotypic and genotypic characterization research has been done on Ethiopian indigenous cattle. However, so far identification and characterization of Ethiopian livestock resources are not exhaustive. There are, however, certain indigenous breeds that are known to exist at various levels of threat, interbreeding with other indigenous breeds and changes in production practices, for example, Sheko cattle. In addition, the cattle breeds Begait, Irob, Ogaden, Afar, and Borana are all threatened in some way [Reference Assefa and Hailu2, Reference Ethiopian3]. As a result, the identification of indigenous cattle breeds, as well as their distinct characteristics, should be prioritized. That should be a prerequisite for developing conservation and sustainable utilization programs to assist indigenous breeds to compete in the future with limited production resources such as land, feed, labor, and capital, knowing the genetic diversity of Ethiopian indigenous cattle is for improvement and conservation purposes. As an example, particularly in terms of disease-tolerant cattle population in the country (for example, Sheko), cattle have been reported as tolerant to trypanosomosis. Not all ancestral Ethiopian cattle have been analyzed at the molecular level. Some attempts have been made in the country to classify and recognize cattle genetic resources. However, those attempts are not enough to give the actual cattle genetic diversity of the country’s indigenous (local) cattle breeds [Reference Ethiopian3].
Characterization attempts were mainly focused on farms, phenotypic features of genetic properties, and their products such as meat and milk, among other things [Reference Ethiopian3]. Some molecular characterization of selected indigenous cattle breeds has been done, and it is not sufficient to say all Ethiopian indigenous cattle have been characterized at a molecular level. So far, the genetic diversity of Ethiopian indigenous cattle such as Horro, Guraghe, Arsi and Abigar, Zebu, Boran, Ambo, Adwa, Ogaden, Zebu Sanga, Fogera, Sanga, Raya-Azebo, Danakil, and African taurine Sheko have been analyzed using RAPD, microsatellite (SSR), and single nucleotide polymorphism (SNP) [Reference Edea, Dadi and Kim6–Reference Dadi, Tibbo, Takahashi, Nomura, Hanada and Amano8]. Even though cattle ecotypes like Kerayu are claimed for resistance to heat and tolerant to drought, they are not yet characterized. Similarly, the Bonga and Jimma ecotypes have variable coat colors, which help the ecotype to adapt to the very hostile environment and heat stress, and the Jimma ecotype is resistant to disease and high milk production when compared to both ecotypes, respectively. Morphological characterization has been done for Bonga, Jimma, and Kerayu cattle. However, molecular characterization has not been done for the three selected indigenous cattle. Therefore, the main aim of this study was to characterize the genetic variability of these three Ethiopian indigenous cattle ecotypes (Bonga, Kerayu, and Jimma) cattle using microsatellite markers.
2. Objective
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(i) To identify the extent of genetic diversity among three indigenous cattle breeds
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(ii) To determine the population structure of the selected indigenous cattle breeds
3. Materials and Methods
3.1. Study Area
Generally, the study was conducted in three areas that have different agroecological setups (Figure S1). The first study area was Jimma Zone, Southwestern Ethiopia. The area is situated approximately between 360 10′E longitude and 70 40′N latitude at an elevation ranging from 880 to 3360 meters above sea level. The zone has an agroecological setting of highlands (15%), midlands (67%), and lowlands (18%) [Reference Shiferaw9].
The second study area was in the Fentale district located in the East Shoa zone of Oromia, the southern part of the northern Rift Valley of Ethiopia. The area falls within an altitude range of 800–1100 masl. However, there are high peaks on the Fentale mountain from which the district derives its name, reaching up to 2007 masl [Reference Areb, Silase, Giorgis, Reti, Zeleke and Mamiru10]. It is found at a distance of about 200 km east of the capital city of Ethiopia, Addis Ababa, on the way to Harar. It is affected by recurrent droughts due to disrupted rainfall patterns. The total land area of the district is 1170 km2. The third study area was Southern Nations Nationalities and Peoples Regional State, Kaffa administrative Zone of Chena woreda. It is situated in the southwestern part of Ethiopia (7°34′N latitude and 37°6′E Longitude) and with an altitude range of 1851–1900 mean above sea level.
3.2. Description of the Breeds
3.2.1. Jimma-Type Cattle
Zenga type (Zebu), mainly distributed in the Jimma zone with medium horns and big body frames. Daily milk yield per day is expected to be about 1.92 liters. Among the Jimma zone, the Dedo district is the highest in milk production and lactation length (3–8) months [Reference Sheriff, Belay and Haile11]. This may be due to management and genetic makeup used for draft power, milk, and meat. Coat color is red, black, and mixed white red and resistant to heat and disease.
3.2.2. Kerayu Cattle
Sanga type breed and distributed in the Kerayu area of Eastern Shoa, mainly for milk, meat, saving, and dowry, with an average body weight of 300.4 kg and 249.9 kg male and female, respectively [Reference Shiferaw9]. They are well adapted to the hot environmental situation with a straight profile, long thin legs, and long horns plain, patchy, and spotty.
3.2.3. Bonga Cattle
Bonga cattle are a Zebu type found in the northern, western, and northwest parts of the Kaffa zone. It originated from around Horro Guduru of Wollega and is used for draft power, milk production, and meat production. The average lactation length of the Bonga cattle ecotype is about 8.5 months and the daily milk yield per cow is about 1.98 liters [Reference Areb, Silase, Giorgis, Reti, Zeleke and Mamiru10]. The coat color of the Bonga cattle ecotype is red, black, light red, grayish, patchy, and spotted. They have downward, upward, mix, and forward horn orientations.
3.3. Blood Samples
A total of 72 genotype were collected from (Bonga, Jimma, and Kerayu) and 24 from each.
Genotype from the same administrative zone was considered as one population with assumptions that they were more likely shared within zone than among zones through animal exchange for breeding.
3.4. Sampling Method
Purposive sampling method (judgmental based or researcher based) was applied during breed selection and simple random sampling (lottery method) was used to select an individual animal. The selection of administrative places (zones, districts, and kebeles) was conducted based on previous phenotypic characterization information of the cattle ecotypes. A list of animals that have the mentioned phenotypic characteristics was found from the selected kebeles. This list was used as a sampling frame for the study. Individual animals were selected using simple random sampling from the sampling frame in all the study areas [Reference Fao12, Reference Lenstra, Groeneveld and Eding13].
3.5. Sample Collection
Blood samples were collected from 24 unrelated animals of each cattle breed using 4 ml EDTA-coated vacutainer tubes. It is recommendable to study diversity within breed 20–30 range [Reference Fao12, Reference Lenstra, Groeneveld and Eding13] samples. From the three study areas, about 72 blood samples were collected. Then, after gently mixing, collected blood samples were placed in an Ice box and transported to National Agricultural Biotechnology Research Center, Holeta, Ethiopia, and stored at −200°C until DNA extraction.
3.6. DNA Extraction
DNA extraction from blood samples was conducted according to the standard salting-out protocol [Reference Nasiri, Forouzandeh, Rasaee and Rahbarizadeh14]. A 500 μl blood sample was transferred into a 2 ml Eppendorf tube; then 800 μl of lysis buffer was added to each tube (repeated until a white pellet formed).
3.7. Determination of DNA Concentration and Quality
The extracted genomic DNA concentration was checked by Nano drop (Nano Drop® ND-8000). DNA quality was checked using gel electrophoresis by loading 5 μl sample DNA on a 1% agarose gel at 100v for one hour. The gel was stained with gel red and visualized under UV light gel documentation system (Figure S2).
3.8. Polymerase Chain Reaction (PCR)
A total of 16 bovine-specific microsatellite markers were used for cattle genetic characterization (Table 1) [Reference Gororo, Makuza, Chatiza, Chidzwondo and Sanyika15]. Polymerase chain reaction (PCR) was performed by touch down method with two steps. The 1st step was initial denaturation at 950C for 3 minutes. Then, it was followed by 20 cycles of denaturation of 950C for 20 sec, annealing begins at 790C and ends at 52.40C for 45 sec, and extension at 720C for 1 minute. The annealing temperature was decreased by 10C until it reached 52.40 C. At the second cycle, denaturation of 940C for 20sec, with 10 cycles, 52.40C for 45 sec, and 720C for 1 minute was applied. The final extension 720C for 10 minutes was applied in all reactions. The final volume of the reactions was 10 μl. The polymerase chain reaction component was done in a total of 10 μl, which included 5 μl DreamTaq PCR master mix 2X, 10 μM forward primer (0.25 μl), 10 μM reverse primer (0.25 μl), 20 ng templates DNA (0.5 μl), and nuclease-free water (4 μl) and control reactions with no DNA template has been prepared to check for DNA contamination and primer dimers. At the end of the reaction, the PCR products were stored at +40C.
TABLE 1: Microsatellite markers, their sequence, and chromosomes number location and annealing AT°C using touchdown PCR.
3.9. Gel Electrophoresis
To assess amplification, 6 μl of the PCR product was loaded on 2% agarose gel prepared by dissolving 2 g of agarose in 100 ml 1XTAE buffer, staining with gel red. Electrophoresis was carried out at 80 V for 3 : 00 hrs. After completion of electrophoresis, the gel pictures were taken under UV Tran’s illuminator by Biodoc analysis with a digital cannon camera (Figures 1 and 2).
FIGURE 1: PCR product of selected Ethiopian indigenous cattle breeds using microsatellite markers (ILST006 primer using 100 bp).
FIGURE 2: PCR product of selected Ethiopian indigenous cattle breeds using microsatellite markers (ILST006 primer using 100 bp).
3.10. Data Scoring and Statistical Analysis
The clear and visible amplified bands of 72 genotype of selected Ethiopian indigenous cattle using microsatellite markers were scored using the PyElph 1.4 software [Reference Pavel and Vasile16].
Genetic variability was measured by estimating observed (Ho) and expected (He) heterozygosities [Reference Nei17]. The Polymorphic Information Content (PIC), the unbiased F-statistics [Reference Wright18], and the Analysis of Molecular Variance (AMOVA) were determined using the GenAlex software version 6.5 [Reference Peakall and Smouse19] and Power Marker. Pairwise FST (proportion of genetic variability due to population substructuring) values among pairs of populations were computed for all populations using GenAlex software version 6.5. Using the POPGEN version 1.31 software package [Reference Yeh, Yang, Boyle and Mao20] and the observed heterozygosity was done according to the algorithm of Levene [Reference Levene21].
Arlequin 3.5 was used to estimate basic frequency-based population genetic parameters such as gene diversity, the total number of alleles (No), Ne, breed private alleles (PAs), allele sizes, and allele ranges (in base pairs) [Reference Excoffier and Lischer22]. Principal Coordinate Analysis (PCoA) was used to infer genetic similarities using the covariance matrix of Nei’s genetic distance (DA) and unbiased genetic distances (DS) measurements. Dendrograms were created from pair-wise matrices of DA using the agglomerative hierarchical clustering unweighted pair group with arithmetic mean (UPGMA) method and DARwin vars to visualize evolutionary relationships among breeds. The Dendro UPGMA online application [Reference Garcia-Vallvé and Puigbo23] was used to design trees, which were then displayed in Tree View [Reference Page24]. Bootstraps of 1000 replicates were used to establish confidence statements about the breed groups and to test the clusters’ dependability.
HP-Rare 1.1software were used to calculate the rarified allelic richness (Ar) and private rarified allelic richness (Arp) [Reference Kalinowski25].
With independent allele frequencies and an admixture model (burn of 50000, followed by 100000 MCMC iterations with 10 replicate runs for each), the population structure analysis was carried out (1–10 K). Its most appropriate K value was identified based on the computed K value, and k = 3 was discovered to be the most likely number of clusters to partition the 72 genotypes into three (Figure 3) using the STRUCTURE harvester program [Reference Earl and VonHoldt26]. The CLUMPAK tool, developed by Kopelman et al. [Reference Kopelman, Mayzel, Jakobsson, Rosenberg and Mayrose27], was used to determine the best alignment from the STRUCTURE data, and the CLUMPAK result revealed genetic mixing and no clear geographic origin-based population structuring.
FIGURE 3: Principal coordinates analysis (PCOA) of 72 genotypes using 16 microsatellite markers.
4. Results
4.1. Microsatellite Locus Polymorphism
The 16 loci yielded a total of 131 alleles, with an average of 8.18 alleles per locus. The No ranged from 5 (RM067) to 12 (ETH3 and TGLA122) and MAF ranged from 0.23 (ETH10) to 0.61 (TGLA126) with a mean of 0.39 per locus. Observed heterozygosity (Ho) varied between 0.00 (ETH10, TGLA126, ILST006, CSSM66, BM2113, SPS115, RM067, TGLA227, TGLA53, and BM1818) and 0.297 (ETH3), while expected heterozygosity (He) ranged from 0.5 (RM67) to 0.75 (ETH10). Ho (0.023) was lower than He (0.66) and this showed that the populations are carrying out inbreeding (Table 1).
The average allelic richness (AR) was 4.57, ranging from 3.33 (TGLA126) to 6 (ETH3), and private alleles (PA) also ranged from 0.66 (BM1818) to 3.66 (INRA023) with an average of 2.1 per locus, respectively, out of 131 alleles, 33 (25%) of PA was unique to specific breeds. The gene diversity (GD) ranged from 0.55 (TGLA126) to 0.86 (ETH10), and the average gene diversity over all loci was 0.74. The 16 microsatellites showed a highly significant (P < 0.001) deviation from the Hardy‒Weinberg equilibrium (Table 2).
TABLE 2: Genetic diversity parameters for 16 microsatellite loci analyzed in three Ethiopian indigenous cattle breeds.
Allele size range (in bp), allelic richness (AR), private alleles (PA) (No, observed number of alleles; MAF, major allele frequency, Na, mean number of alleles; Ne, the effective number of alleles), marker in informativeness (PIC, polymorphism information content), heterozygosity (Ho, observed; He, expected; Ht, total). ∗HWE: ns stands for “not significant,” ∗∗P < 0.01, ∗∗∗P < 0.001, ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001.
4.2. Genetic Diversity of Population
Comparatively the mean number of alleles observed in this study ranged from Na = 4.94 (Bonga) to Na = 5.56 (Kerayu), and the medium mean number of alleles observed was in Jimma cattle populations. Secondly, Kerayu populations (Ne = 3.4) score the highest effective number of alleles than Jimma (Ne = 3.2), Bonga (Ne = 3.06) and with a total mean of Ne = 3.23. In Kerayu cattle populations, the highest number of private alleles were also observed (PA = 4.13), Jimma (PA = 1.13), and Bonga (PA = 1.0) with a total mean of PA = 2.1. Shannon’s information index ranged from I = 1.26 (Bonga and Jimma) to I = 1.34 (Kerayu) with an average mean of I = 1.29. The other things listed above the table were Ho and He. The Ho ranged from 0.044 (Kerayu), 0.016 (Jimma), and 0.011 (Bonga), respectively (Table 3).
TABLE 3: Sixteen microsatellite markers used to estimate genetic diversity parameters in three Ethiopian cattle breeds.
NA; mean number of alleles, and AR (allelic richness). NA; mean number of alleles, and AR (allelic richness). Na: stands for the average number of alleles. PA stands for private alleles and Ne stands for an effective number of alleles. The Shannon information index or I; He stands for expected heterozygosity, while % P stands for polymorphic loci proportion.
Allelic richness ranged from AR = 3.88 (Bonga) to (AR = 5.56) Kerayu. Expected heterozygosity (He) had the highest mean value of 0.658 in (Bonga) and the lowest in Jimma (0.63). Observed heterozygosity (Ho) had a mean value of 0.011 (Bonga) to 0.044 (Kerayu). In Kerayu cattle populations, the highest number of private alleles were also observed (PA = 4.13), Jimma (PA = 1.13), and Bonga (PA = 1.0). Shannon’s information index ranged from I = 1.26 (Bonga and Jimma) to I = 1.34 (Kerayu) with an average mean of I = 1.29. The Ho ranged from 0.044 (Kerayu), 0.016 (Jimma), and 0.011 (Bonga), respectively. The average mean number of alleles was from Na = 4.94 (Bonga) to Na = 5.56 (Kerayu), and allelic richness ranged from AR = 3.88 (Bonga) to (AR = 5.56) Kerayu. Expected heterozygosity (He) had the highest mean value of 0.658 in (Bonga) and the lowest in Jimma (0.63). Observed heterozygosity (Ho) had a mean value of 0.011 (Bonga) to 0.044 (Kerayu) (Table 3).
4.3. Analysis of Molecular Variance and Gene Flow
Population variance could be classified based on AMOVA among individuals within populations variability (84%) and (13%) variation among populations/among individuals and 3% within the individual. The analysis also confirmed the presence of considerable gene flow (1.69) among subpopulations (Table 4).
TABLE 4: Summary of AMOVA.
Df = Degree of freedom, SS, the sum of a square, Ms, means of a square, NM = gene flow.
4.4. Genetic Distance and Genetic Differentiation between Populations
The genetic differentiation between populations ranged from 0.100 between (Bonga and Jimma) to 0.120 (Kerayu and Bonga). The highest GD (0.120) was between Kerayu and Bonga populations, and the lowest genetic differentiation was observed between Jimma and Bonga populations (Table 5). This might be due to geographical locations and types of population/ecotype used. The highest (0.46) genetic distance was observed between Kerayu and Bonga cattle breeds. However, the lowest (0.407) genetic distance was determined between Jimma and Bonga cattle breeds.
TABLE 5: Pairwise population differentiation using Nei genetic distance (above diagonal) and FST (below diagonal) values.
4.5. Cluster Analysis of Genotype in Three Ethiopian Indigenous Cattle
The unweighted neighbor-joining cluster analysis (UPGMA) categorized the 72 genotypes into three major clusters (Cl-I, Cl-II, and Cl-III). From Figure 2, three types of clustering, dark-red, blue, and green color clusters showed Bonga cattle, Jimma, and Kerayu cattle ecotypes, respectively. Cl-I (36 percent), Cl-II (48.2 percent), and Cl-III (16 percent) of the overall population make up the three clusters, respectively. The first cluster, which contained 24 genotypes from all populations excluding Bonga and Jimma cattle genotypes, was the major cluster, whereas the second cluster contained 24 genotypes except the Bonga genotype. The third cluster contains 12 genotypes. The first cluster consists of 33% genotype from Kerayu, 1.4% genotype from Bonga, and 1.4% genotype from Jimma cattle, respectively.
Cluster-II consists of 33% and 15.2% from Jimma and Bonga, respectively. Cluster three consists of only genotype from Bonga (16%). Genotypes from Bonga are mainly found in all three clusters, especially found in cluster-II (Jimma) (Figure 4).
FIGURE 4: Hierarchical clustering of three Ethiopian indigenous cattle using NJ dendrogram.
Figure 5 indicates that there is high gene flow between the two breeds (Bonga and Jimma). Population grouping was also carried out based on the UPGMA method to determine the relationship among the three selected indigenous cattle groups. According to the analysis, the populations are divided into two major clusters. Kerayu (I) is categorized under cluster one and both Jimma and Bonga (II) are categorized under the second cluster. Clustering patterns indicated that populations from geographically adjoining regions like Bonga and Jimma are subclustered together (Figure 5).
FIGURE 5: Dendrogram showing the genetic diversity and dissimilarity among the three selected Ethiopian indigenous cattle generated by 16 microsatellite markers (with observations Bng = Bonga, Jmm = Jimma, Keryu = Kerayu).
4.6. Principal Coordinate Analysis (PCOA) and Population Structure
Principal coordinate analysis was also used to look at the genetic relatedness of 72 genotypes (PCOA). The pattern of genotype distributions on a two-dimensional plot showed separate clustering of populations based on the geographic locations and revealed a high pattern of grouping. The PCoA analysis displayed in Figure 3 below confirms and was complementary to the result of the NJ cluster analysis shown in Figure 4.
With independent allele frequencies and an admixture model (burn of 50000, followed by 100000 MCMC iterations with 10 replicate runs for each), the population structure analysis was carried out (1–10 K). In the method (K = m|L″(K)|/s[L(K)] published by Evano et al. [Reference Evanno, Regnaut and Goudet28], the suitable number of clusters was discovered using K values that reflected the proportion of change in the logarithmic probability Pr(X|K) of data between K values (28) (Figure 6). Its most appropriate K value was identified based on the computed K value and k = 3 were discovered to be the most likely number of clusters to partition the 72 genotypes into three (Figure 7).
FIGURE 6: Population structure of three cattle breeds obtained by STRUCTURE analysis (K = 3). Based on Evano et al. [Reference Evanno, Regnaut and Goudet28].
FIGURE 7: Population structure of three cattle breeds obtained by STRUCTURE analysis (K = 1, 2, 3,4,5,6, and 10), where each color represents a different cluster.
5. Discussion
5.1. Genetic Diversity
According to the classification of Botstein et al. [Reference Botstein, White, Skolnick and Davis29], the highly informative markers have PIC values >0.50, the reasonably informative markers have PIC values between 0.25 and 0.50, and the slightly informative markers have PIC values <0.25. In this study, all sixteen microsatellite loci used to profile genetic diversity of 72 genotype were found to be highly polymorphic with PIC >0.5. For evaluating genetic differences between animal breeds, all 16 microsatellite markers exceeded the FAO’s suggested minimum threshold of five alleles per locus [Reference Lenstra, Groeneveld and Eding13, Reference Gororo, Makuza, Chatiza, Chidzwondo and Sanyika15]. Öner et al. [Reference Öner, Yılmaz, Eriş, Ata, Ünal and Koncagül30] reported average mean PIC value was 0.87. The average means PIC value (0.70) of the present study indicated the markers used in this study were highly informative and the availability of high allelic variation in the marker loci and their distribution within the population’s genome. The result of the present study was also used for genetic diversity analysis. In general, the PIC of this study (0.70) was higher than that of the previous one reported by Gororo et al. [Reference Gororo, Makuza, Chatiza, Chidzwondo and Sanyika15] (0.664). Demir and Balcioglu [Reference Demir and Balcioğlu31] and also Hussain et al. [Reference Hussain, Babar and Peters32] reported a PIC value of 0.82 using microsatellite markers, which was greater than the PIC value of this study. The variation in PIC value could be due to a higher number of samples/breed types and an increased number of markers and implying the high discriminating ability of the markers.
Gororo et al. [Reference Gororo, Makuza, Chatiza, Chidzwondo and Sanyika15] found 119 alleles in total, with an average of 7.4 alleles per locus and 34 private alleles. Hussain et al. [Reference Hussain, Babar and Peters32] also reported a total of 476 alleles with a 22.33 average mean of alleles per locus using 21 microsatellite markers. Jakaria et al. [Reference Hussain, Babar and Peters32] reported 46 alleles in four microsatellite markers with an average mean of 11.5 per locus. Other studies by Özşensoy et al. [Reference Özşensoy, Kurar and Doğan33] reported 269 alleles in 20 markers with a 13.45 average mean of alleles per locus. Öner et al. [Reference Öner, Yılmaz, Eriş, Ata, Ünal and Koncagül30] reported a total of 545 alleles in 22 microsatellite markers with an average mean of 23.14 alleles per locus, and this observed number of alleles difference might be due to the number of genotypes, number of the marker, and breed number used.
5.2. Genetic Diversity Analysis along with Populations
Previously, both Dadi et al. [Reference Dadi, Tibbo, Takahashi, Nomura, Hanada and Amano8] and Gororo et al. [Reference Gororo, Makuza, Chatiza, Chidzwondo and Sanyika15] reported similar observed and expected heterozygosity across the populations. Agung et al. [Reference Agung, Saputra and Septian34] also detected 0.66, 0.68, Ho and He, respectively. But, in this study, the level of observed and expected heterozygosity across the study populations was different, the Ho and He of this study were lower than the study reported by previous workers [Reference Dadi, Tibbo, Takahashi, Nomura, Hanada and Amano8, Reference Zerabruk, Li and Kantanen35].
Demir and Balcioğlu [Reference Demir and Balcioğlu31] reported that the Ho and He mean values were 0.63 and 0.74, respectively, due to the increased number of microsatellite markers and individual samples. But, in this case, Ho and He values were 0.023 and 0.651, respectively.
The number of individuals sampled per population and the number of populations studied might be the main cause for the higher numbers of Ho and He in Dadi’s et al. [Reference Dadi, Tibbo, Takahashi, Nomura, Hanada and Amano8] study and others. This difference might be due to factors like Null alleles, assortative mating, the Wahlund effect, selection against heterozygotes, inbreeding, or a combination of all of these reasons that can all explain this state [Reference Gororo, Makuza, Chatiza, Chidzwondo and Sanyika15, Reference Zerabruk, Li and Kantanen35].
In this study, the mean number of alleles for each breed (Bonga, Jimma, and Kerayu) was 4.94, 5.063, and 5.56, respectively. Demir and Balcioğlu [Reference Demir and Balcioğlu31] reported that the mean number of alleles for four breed types such as Turkish Grey steppe (7.95), Eastern Anatolian Red (7.15), Anatolian Black (8.45), and HF (7.1). Agung et al. [Reference Agung, Saputra and Septian34] also reported the mean number of alleles for 11 different cattle breeds was 6.28. The variation of mean alleles in this study and the previous study might be due to the increased number of microsatellite markers and the number of breed types used for the study. Gororo et al. [Reference Gororo, Makuza, Chatiza, Chidzwondo and Sanyika15] reported a mean number of alleles (Na) of 5.16, which was similar to this study, microsatellite markers based on characterization of selected Ethiopian cattle like Bonga, Jimma, and Kerayu and Na detected were 5.18.
The other measure of gene diversity is the Shannon information index (I), if Shannon’s information index value is close to one or above, it indicates that there is variation in the tested populations and that the markers are suitable for studying diversity [Reference Nassiry, Javanmard and Tohidi36]. The value obtained in this study ranged from I = 0.86 to I = 1.63 with an average mean of 1.28. This implies that selected Ethiopian indigenous cattle have genetic diversity.
According to Zerabruk et al. [Reference Zerabruk, Li and Kantanen35], allelic richness reported in north Ethiopia of cattle ranged from 5.67 to 6.27 with a mean of 6.23 AR per breed. But, in this study, AR ranged from 3.88 to 5.56 with a mean of 4.5 AR per breed. This showed that there is genetic diversity among populations.
Agung et al. [Reference Agung, Saputra and Septian34] reported a 3.81 effective number of alleles across 11 different breeds using 12 microsatellite markers; however, in this study, 3.23 effective numbers of alleles were detected using 16 microsatellite markers from 3 different Ethiopian indigenous cattle breeds. Hussain et al. [Reference Hussain, Babar and Peters32] reported 6.7 effective numbers of alleles. This showed that the types of breeds and the number of breeds used might be the cause for the variation in an effective number of alleles.
To quantify the genetic variability, in Ethiopian indigenous cattle breeds and 16 microsatellite markers were used resulting in a Fis value (0.966), showing that the selected Ethiopian indigenous cattle have undergone inbreeding. Jakaria et al. [Reference Jakaria, Alwiyah, Saputra, Baihaqi and Noor37] reported a Fis value of 0.07 using five microsatellite markers, and it was lower than the Fis value reported in this study. FST values could indicate small (0–0.05), medium (0.05–0.15), high (0.15–0.25), and very high (FST >0.25) genetic differentiation between breeds [Reference Hartl and Clark38].
The estimated FST value for 3 different cattle breeds was higher than Zimbabwean cattle breeds (0.084) [Reference Gororo, Makuza, Chatiza, Chidzwondo and Sanyika15]; this finding was comparable with those reported by Sharma et al. [Reference Sharma, Maitra, Singh and Tantia39], 13.3% of FST value in Indian cattle using STR markers and high FST value of this study indicated that 16 microsatellite markers used for 3 breeds/ecotypes were significantly high and are useful indicators of markers that could be powerful tools for genetic differentiation of different breeds. But, lower than Pakistan cattle breeds (0.1456) Rahal et al. [Reference Rahal, Aissaoui and Ata40], and Indonesian cattle breeds (0.243) [Reference Sharma, Maitra, Singh and Tantia39]. Jakaria et al. [Reference Jakaria, Alwiyah, Saputra, Baihaqi and Noor37] also revealed a higher number of FST values (0.246) than this study. El-Sayed et al. [Reference El-Sayed, Al-Soudy and Teleb41, Reference Bora42] detected a higher FST (0.236) value from two Egyptian cattle breeds. This FST variation might be due to gene flow and exchange of breeding animals. The FST is higher when the populations are isolated between them.
5.3. Analysis of Molecular Variance
In addition, AMOVA shown in genetic variation among breeds was 13%, and 84% within the population, 3% within an individual was observed. Significant genetic differentiation was observed among all 3 different Ethiopian indigenous cattle (Bonga, Jimma, and Kerayu) estimated by FST = 0.129. Dadi et al. [Reference Dadi, Tibbo, Takahashi, Nomura, Hanada and Amano8] reported 1.3% genetic variation among populations and 98.7% genetic variation within populations. Jakaria et al. [Reference Jakaria, Alwiyah, Saputra, Baihaqi and Noor37] also reported genetic variation within a population (70.6%) was higher than among populations (29.4%). The genetic variation within the population was higher than among populations this implies that there might be due to interpopulation gene flow, sexual recombination, and mutations.
5.4. Cluster Analysis, PCOA, and Population Structure
In clustering, a dendrogram of cluster analysis based on NJ algorism using UPGMA categorized the three ecotypes into three clusters based on the geographical locations (I, II, and III) with different subgroups. Dadi et al. [Reference Dadi, Tibbo, Takahashi, Nomura, Hanada and Amano8] obtained two main clusters using 30 microsatellite markers with 10 indigenous, one HF, and different subgroups formed under two main subgroups. Cervini et al. [Reference Cervini, Henrique-Silva, Mortari and Matheucci43] showed two different clusters of dendrogram-based UPGMA/NJ using 12 microsatellite markers in 11 different cattle breeds. A study by Edea et al. [Reference Edea, Dadi and Kim6] also revealed two main clusters in six Ethiopian indigenous and one Korean cattle breed using SNP markers.
The difference might be due to the number of microsatellites/type of microsatellite used, some ecotypes/breeds, and sample numbers. The clustering model showed that there was a relationship between the patterns of genetic diversity and the geographical origins of the collection. Populations collected from Bonga and Jimma had a strong relationship. Bonga and Jimma cattle were close to each other and the existence of gene flow among the neighboring populations seems possible.
Moreover, this result is also clearly reflected in population structure showing weak admixture of a gene across populations. It revealed the existence of substructuring (K = 3) in three populations of Ethiopia. Previously, Jakaria et al. [Reference Jakaria, Alwiyah, Saputra, Baihaqi and Noor37] also reported population structure with k = 3 in four cattle populations and there were genetic admixtures. This could be likely due to the presence of gene flow between the ecotypes because of the movement of cattle and uncontrolled mating/exchange of breeding animals, migration from one region to another. Grouping of PCoA corresponds with the clustering dendrogram based, which showed conformity result obtained from UPGMA analysis Agung et al. [Reference Agung, Saputra and Zein44].
6. Conclusion
In this study, the genetic diversity and population structure of the selected Ethiopian indigenous cattle were covered using highly polymorphic microsatellite markers. The study confirmed the presence of genetic diversity in selected Ethiopian indigenous cattle breeds, indicating the possibility of improvement through breeding and the importance of maintaining diversity by applying appropriate conservation strategies. This study also indicated genetic variation (84%) accounted for within populations, likely due to gene flow, sexual recombination, and mutation. The populations showed moderate genetic differentiation, due to high gene flow. The highest genetic diversity indices were recorded for Kerayu cattle populations, suggesting that this area is a hotspot for genetic diversity studies, sources of important alleles for breeding purposes, and conservation strategies must be applied.
In general, cluster analysis, PCoA, and population structure analysis exhibited moderate grouping of samples. Studying the genetic basis of locally adapted indigenous cattle populations is critical for developing appropriate breeding strategies and programs aimed at improving and conserving their genetic diversity. From this study, the generated information is valuable for the national animal breeding program and conservation purposes.
Abbreviations
AR: Allelic richness
PA: Private alleles
Masl: Mean above sea level
PCOA: Principal coordinate analysis
UPGM: Unweighted pairwise group method
I: Shannon information index
PIC: Polymorphic information content
HWE: Hardy‒Weinberg equilibrium
AMOVA: Analysis of molecular variance
NJ: Neighbor joining tree
MAF: Major allele frequency
GDP: Gross domestic product.
Data Availability
The corresponding author will provide the datasets produced or analyzed during the current investigation upon reasonable request.
Ethical Approval
In this study, the authors did not use laboratory animals/experimental animal; instead, the authors only collected blood samples from jugular vein of the selected cattle. Additionally, the manuscript does not have any clinical studies and no patient data.
Consent
Not applicable.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Authors’ Contributions
The research concept and proposal was developed by SKB and GG. Primers were bought by TST. All Authors contributed starting from research design, sample collections, data analysis, and full write-up of the research. Especially, TST and GG provided their valuable comments during research full write-up.
Acknowledgments
The authors thank Addis Ababa University and Ethiopian Institute of Agricultural Research, National Agricultural Biotechnology Research Center for their assistance in laboratory facilities, primers, and sample collection materials.
Supplementary Materials
Figure S1: Study Area. Figure S2: Image of genomic DNA. Table S1: List of sample collection site used for the study and their geographical origins. (Supplementary Materials)
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