Genetic Diversity and Population Structure of Four

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from blood using the QIA amp ® DNA blood mini kit to calculate observed number .... [18] and 0.47 to 0.55 reported by Tadelle [19]. populationat LEI0094 locus ...
International Journal of Genetics 5(1): 18-24, 2015 ISSN 2222-1301 © IDOSI Publications, 2015 DOI: 10.5829/idosi.ijg.2015.5.1.94107

Genetic Diversity and Population Structure of Four Indigenous Chicken Ecotypes Representing South and South Western Ethiopia 1,2

Emebet M. Bekerie, 2Zemelak S. Goraga, 3Anna Maria Johansson and 1Harpal Singh Addis Ababa University, P.O. Box: 34, Deber Zeit, Ethiopia Ethiopian Institute of Agricultural Research, P.O. Box: 33, Deber Zeit, Ethiopia 3 Swedish University of Agricultural Sciences, P.O. Box: 7023, Uppsala, Sweden 1

2

Abstract: The level of genetic diversity and population structure of four indigenous chicken ecotypes representing South and Southwest Ethiopia were studied using ten microsatellite markers. Fayomi and Swedish breeds were included for comparison. The number of alleles in Ethiopian ecotypes ranged from 3 to 11 per locus. The highest number of alleles per locus (11) was observed for the Mehal Amba chicken ecotype at LEI0094 locus, while the lowest number of alleles per locus (3) was recorded for Seden ecotype at MCW0014, MCW0222 and MCW0078. The average polymorphism information content value of the markers across population varied from 0.25 for MCW0014 to 0.73 for LEI0094. The mean observed heterozygosity (Ho) values were range from 19 to 55% in overall population and from 50 to 55% in Ethiopian ecotypes. In the phylogenetic tree the Swedish, Fayomi and the Ethiopian local chicken formed three different branches. This result showed that the Ethiopian local chickens are different from exotic breeds. On the other hand there is a tendency or sub-clustering within Ethiopian ecotypes which needs further investigation. Key words: Chicken Ecotype

Ethiopia

Genetic Diversity

INTRODUCTION

genetic markers for assessing genetic diversity, mapping of QTLs, population structure and relationship within and among various populations of indigenous and commercial chickens including Jungle Fowl [8-10]. Population differentiation in African chicken ecotypes is expected because of geographical isolation, drift and the wide range of phenotypes displayed by the animals. Genetic characterization contribute to breed definition especially for populations, which are not well defined and provide an indication of the genetic diversity of these lines. It has potential also to identify unique alleles in the breeds or lines studied. Up to date no information is available on the genetic diversity of South and South western Ethiopian chicken ecotypes. Therefore in the present study the genetic diversity and population structure of four local ecotypes (two from the South and two from South western Ethiopia) were characterized using microsatellite markers. Fayomi and Swedish breeds were included as a control group.

Despite the important role-played by tropical fowl as a supplier of meat and eggs in developing countries, there is very little information on its genetic makeup. The most important genes proved for their special utility in the tropics are naked neck, frizzle, dwarf, silky, slow feathering, noninhibitor, fibro-melanosis, peacomb and blue shell [1,2]. Even though the information collected in the FAO Domestic Animal Diversity Information System (DAD-IS) and other sources show that these genes are prevalent in the local populations across the African countries, little information exists on the genetic make-up of the indigenous chicken of Africa. Microsatellites are tandem repeats of one to six bases [3,4]. Compared to other types of molecular markers, microsatellites have many advantages. They are numerous and ubiquitous throughout the genome, showing a high degree of polymorphism and codominant inheritance [5-7]. Microsatellite are the most widely used Corresponding Author:

Microsatellite Marker

Emebet Moreda, Addis Ababa University, P.O. Box: 34 , Deber Zeit, Ethiopia, Ethiopian Institute of Agricultural Research, P.O. Box: 33, Deber Zeit, Ethiopia.

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Intl. J. Genet., 5(1): 18-24, 2015

MATERIALS AND METHODS

Thereafter, a mixture of 7.5 µl LIZ600 internal size standard and 300 µl Formamide was made and from this mix 12 µl was taken and mix with 1 µl of PCR products and this mix was allowed for heat denatured at 95°C for about 3 minutes. Each sample was prepared and performed as single runs and analyzed on POP-7xl_ 900s polymer using a 36 cm capillary with 55 injection at 15 KV and run for 28 minutes at 15 KV + 9 µA on ABI 310 genetic analyzer following the Applied Biosystem user manual version 2.1. The fragment sizes were calculated based on the internal size standards of LIZ 600 using the Gene Mapper ID version 4.1 and exported to microsoft excel for preparation of input files for statistical analyses.

Chicken Population and Blood Sample Collection: A total of 205 chickens belonging to 4 indigenous chicken ecotype: Dawo (n = 57), Seden Sodo (n = 33), Mehale Amba (n = 75) and Mehurena Aklile (n = 40) were used for blood sample collection. The Fayomi (n= 25) and Swedish local chicken (n= 17) breed were included as control. Blood samples from Ethiopian indigenous chicken and Fayomi were collected in 2 ml tubes containing EDTA in the form of K3E, as anticoagulant and stored at – 70°C until DNA extraction. DNA Extraction: Genomic DNA from Ethiopian indigenous chicken and the Fayomi breed was extracted from blood using the QIA amp ® DNA blood mini kit according to Sambrook et al. [11]. DNA concentrations were quantified spectrophotometrically. For the Swedish local chicken; extracted DNA which was already stored at animal breeding and genetics laboratory of Swedish Agricultural University was used.

Data Analysis: GENEPOP software version 4.1 was used to calculate observed number of alleles, observed heterozygosity (Ho) and expected heterozygosity (He) per microsatellite marker. Genetic distances between populations were calculated by Nei [13] unbiased distance and similarity measures. Genetic population relationships were estimated by constructing both Neighbour-Joining (NJ) method and Unweighted Pair-Group method with 70 Arithmetic mean (UPGMA) tree based on Nei's standard genetic distance which was modified from NEIGHBOUR procedure of PHYLIP version 3.5. The Polymorphism Information Content (PIC) values were calculated based on the number and frequency of alleles per marker at a specific locus [14] using Power Stat version 12 software to assess how polymorphic the microsatellite markers are in the studied population.

Marker Selection: A total of 10 microsatellite markers were used for this study and all of them had already been used in the AVIANDIV project and the markers were selected based on FAO [12] recommendations. Determination of genetic distances using neutral, highly polymorphic microsatellite markers are currently the method of choice for investigating genetic relationships and breed differentiation. Polymerase Chain Reaction (PCR): PCR was used to amplify the specific DNA fragments containing microsatellites. Three to five pairs of primers were run in one tube. A PCR reaction mixture with a total volume of 10 µl containing 0.5 µl of genomic DNA, 1µl of 10 X Buffer including MgCl2, 0.1 µl of 25 mM dNTP, 0.1 µl of each (10 pmol/µl) forward and reverse primers, 0.2 µl of AT Gold (Taq DNA polymerase) and the remaining volume free nucleic water were prepared. The ingredients were thoroughly mixed by vortexing in order to produce a homogenous mix. The amplification protocol involved initial denaturation of DNA and enzyme activation, at 95°C for 15 min followed by 35 cycles of denaturation at 95°C for 1min, primer annealing at temperature 55°C for 1min, extension at 72°C for 1min and final extension at 72°C for10 min using an automated thermal cycler (Mastercycler, Eppendorf, Hamburg, Germany).

RESULTS AND DISCUTION Microsatellite Markers Allele Distribution: Out of the 10 microsatelite markers that were sorted in the studied population nine of them were used for diversity analysis. A total of 74 alleles were observed across all populations (Table 1). The number of alleles per loci ranged from 4 (MCW0078 and MCW0222) to 14 (LEI0094). Within a marker, allele size range varied from a difference of 6 base pairs (215-221bp) for locus MCW0222 to 36bp (244-280bp) for LEI0094. Microsatellite markers tested in this study showed variation in number of alleles within population which ranged from 3 to 11 in Ethiopian ecotypes (Table 2). According to reports by Crooijmans et al. [15], Ponsuksili et al. [16], Vanhala et al. [3], Wimmers et al. [17] and Köster [18], the different number of alleles per marker were between 1 and 9 (commercial lines), 4 to 13 19

Intl. J. Genet., 5(1): 18-24, 2015 Table 1: Number and size range of alleles at each locus in whole populations Markers/ locus No of alleles observed Observed alleles ADL0268 LEI0094 MCW0034 MCW0069 MCW0081 MCW0222 MCW0295 ADL0112 MCW0014 MCW0078

6 14 10 9 6 4 8 5 8 4

Total

74

Observed size range (bp) Expected size range (bp)

100 106 108 110 112 111 244 245 246 252 258 260 262 264 266 268 274 276 278 280 221,222, 224 226 228 230 232 234 242 244 153 155 157 160 162 164 166 170 172 109 111 117 125 127 131 215 217 219 221 83 85 87 89 93 95 97 100 120 122 124 126 128 162 168 170 172 174 176 178 180 134 138 140 142

100-112 244-280 221-244 153-172 109-131 215-221 83-100 120-128 162-180 134-142

102-116 247-287 212-246 158-176 112-135 220-226 88-106 120-134 164-182 114-126

Table 2: Observed numbers of alleles for different microsatellite markers within and across populations Marker ADL0268 LEI0094 MCW0034 MCW0069 MCW0081 MCW0222 MCW0295 ADL0112 MCW0014 MCW0078 Mean

Dawo (N=37) Seden sodo (N=30) Mehal amba (N=29) Mhurena aklile (N=26) Fayomi (N=10) Swedish (N=17) MeanAllele across population 5 8 6 6 5 4 6 4 5 4 5.3

5 10 7 6 6 3 6 4 3 3 5.3

5 11 6 6 4 4 6 5 4 4 5.5

6 6 8 5 4 4 6 4 4 4 5.1

(inbred lines and hybrid), 2 to 11 (native lines), 3 to 14 (native South African) and 2 to 11 (for different breeds of Africa and Asia), respectively. Studies by Tadelle [19] and Halima [20] that were characterized other local chicken ecotypes reported 2 to 9 and 2 to 11 number of alleles per loci respectively. As indicated in Table 2, the highest number of alleles per locus (11) was observed for the Mehal Amba) chicken population at LEI0094 locus, while the lowest number of alleles per locus (1) was recorded for Fayomi (MCW0014). No allele was detected at MCW0078in Swedish chicken. The mean number of alleles within population varied from 3.6 (Fayomi) to 5.5 (Mehal Amba)). From the Ethiopian chicken population, Mehal Amba chickens showed the highest mean number of alleles within population (5.5), followed by Dawo and Seden Sodo (5.3) and Mehurena Aklile (5.1). The average number of alleles across all populations in all loci was 4.80.

4 4 5 4 2 3 5 4 1 4 3.6

3 4 5 4 3 3 6 5 4 0 3.7

4.67 7.17 6.17 5.17 4.00 3.50 5.83 4.33 3.50 3.17 4.80

0.25 to 0.83 [16], 0.33 to 0.66 [18] and 0.27 to 0.73 [19]. Halima [20] reported PIC value of 0.58 to 0.78 in North western chicken ecotypes of Ethiopia. PIC values for all tested markers within population showed a much smaller variation of 0.45 to 0.54, which are in a similar range as PIC values of 0.46 to 0.57 reported for native South African chicken populations tested at 23 microsatellite markers [18] and 0.47 to 0.55 reported by Tadelle [19]. Those markers such as LEI0094, MCW0034, MCW00295, MCW0069 and ADL0268 which had the highest number of alleles (closer to five or more) showed the highest PIC value. Even though MCW0078 had the smallest number of alleles, its PIC value is greater than the PIC value of MCW0081 which had more number of alleles as compared to MCW0078. The reason for low PIC value for MCW0081 is one of the alleles (111) was main (dominant). For example, in Amba and Dawo population, the dominant allele represented 90 and 91%. The mean observed hetrozygosity (Ho) were ranged from 19 to 55% in overall population and from 50 to 55% in Ethiopian ecotypes (Table 4). The observed heterozygosity values of 50 to 55% obtained in Ethiopian ecotypes is much higher than the previous results reported by Vanhala et al. [3]; Zhou and Lamont [21] and Wimmers et al. [17], who reported mean heterozygosity values ranging from 0.29 to 0.67, 0.0 to 0.1 and 0.45 to 0.71

Polymorphic Information Content and Hetrozygosity: Table 3 describes the polymorpic information content (PIC) of the markers in the studied population. The average PIC value of the markers across population varied from 0.25 for MCW0014 (smaller number of alleles) to 0.73 for LEI0094 (larger number of alleles). This is in agreement with PIC values reported for other chicken populations 20

Intl. J. Genet., 5(1): 18-24, 2015 Table 3: Polymorphic information content (PIC) of microsatellite markers in tested populations

Markers/ locus

Population ------------------------------------------------------------------------------------------------------------------------------Aklile Amba Dawo Seden Fayomi Swedish

ADL0112 ADL0268 LEI0094 MCW0014 MCW0034 MCW0069 MCW0078 MCW0081 MCW0222 MCW0295 Mean**

0.54 0.75 0.77 0.28 0.74 0.51 0.59 0.21 0.52 0.51 0.54

0.50 0.70 0.82 0.17 0.61 0.53 0.49 0.35 0.52 0.70 0.54

0.52 0.67 0.76 0.16 0.68 0.59 0.54 0.21 0.56 0.61 0.53

0.48 0.67 0.71 0.29 0.71 0.54 0.47 0.23 0.49 0.57 0.52

0.22 0.55 0.78 0 0.62 0.60 0.35 0.63 0.16 0.58 0.45

Mean*

0.59 0.59 0.54 0.57 0.69 0.66 0 0.39 0.12 0.73 0.54

0.48 0.67 0.73 0.25 0.68 0.57 0.49 0.34 0.40 0.62

Table 4: Observed and expected Heterozygosity values of microsatellite markers in tested populations

Markers/ locus

Chicken population ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------Aklile Amba Dawo Seden Fayomi Swedish --------------------------------------------------------------------------------------------------Ho He Ho He Ho He Ho He Ho He Ho He

ADL0112 ADL0268 LEI0094 MCW0014 MCW0034 MCW0069 MCW0078 MCW0081 MCW0222 MCW0295 Mean SD

0.75 0.95 0.85 0.22 0.76 0.43 0.57 0.14 0.38 0.33 0.54 0.09

0.60 0.79 0.80 0.30 0.77 0.58 0.63 0.22 0.60 0.55 0.58 0.06

0.46 0.84 0.89 0.10 0.73 0.69 0.50 0.25 0.56 0.5 0.55 0.08

0.54 0.74 0.84 0.18 0.65 0.60 0.55 0.37 0.59 0.74 0.58 0.06

0.55 0.83 0.86 0.10 0.78 0.70 0.44 0.17 0.61 0.48 0.55 0.08

0.60 0.74 0.80 0.26 0.69 0.69 0.48 0.20 0.61 0.55 0.56 0.06

0.43 0.79 0.81 0.70 0.76 0.52 0.38 0.48 0.48 0.30 0.50 0.07

0.49 0.69 0.81 0.19 0.81 0.63 0.64 0.66 0.51 0.67 0.61 0.05

0.60 1.00 1.00 0.00 0.63 0.50 0.67 0.13 0.50 0.50 0.55 0.10

0.58 0.70 0.72 0.00 0.71 0.56 0.71 0.12 0.59 0.68 0.54 0.08

0.12 0.13 0.21 0.12 0.38 0.31 0.00 0.19 0.13 0.31 0.19 0.04

0.63 0.66 0.58 0.64 0.73 0.71 0.00 0.47 0.12 0.76 0.53 0.08

Table 5: Analysis of Molecular Variance (Amova) of the six chicken populations tested at seven marker loci Source of variation

df

Sum of Squares

Mean Square

Est.Var

% of variation

Among population Within population

5 143

106.459 996.333

21.292 6.967

0.592 6.967

8% 92%

Total

148

1102.792

7.559

100%

Genetic Distance and Relatedness: Results of the analysis of molecular variance (AMOVA) revealed that although there was considerable genetic variation among the different chicken populations, the variation within population was greater than among populations. About 92% of the total variation in the studied chicken population was due to within population; and the rest 8% among populations (Table 5). These results are consistent with population variation analyses in other works [19,22]. The lowest genetic distance observed between local ecotypes was 0.034 (between Amba and Dawo) and the largest was 0.074 (between Aklile and Seden). Swedish chicken had the greatest genetic distance to Ethiopian chicken ecotypes. Seden was the most distant

Table 6: Genetic distance of the six chicken populations Population

Aklile

Amba

Dawo

Seden

Fayomi

Swedish

Aklile Amba Dawo Seden Fayomi Swidish

0.000 0.042 0.041 0.074 0.083 0.440

0.000 0.034 0.061 0.087 0.471

0.000 0.072 0.087 0.398

0.000 0.106 0.405

0.000 0.441

0.000

Unbiased genetic distance (Nei, 1978)

using microsatelite markers, respectively. However it is in agreement with the range (53 to 64%) and (55 to 63%) by Köster [18] and Tadelle [19] respectively. Halima [20] also reported hetrozygosity values above 50% in other local chicken ecotypes. 21

Intl. J. Genet., 5(1): 18-24, 2015

Fig. 1: Phylogenetic tree with Nei’s standared genetic distance using UPMGA Method for four local chicken and the reference breed Fayomi and Swedish (0.11) to Fayomi but *Average PIC/ microsatellite marker; ** Average PIC/population Aklile was most close (0.083). Amba ecotype was the most distant (0.47) to Swedish breed but Dawo was the closest (0. 40). Although, the genetic distance among the local ecotypes (0.034 to 0.074) obtained in this study is lower than values reported by Tadelle [19], there is a considerable genetic distance of the local ecotypes from Fayomi breed and Swedish chicken population. The observed patterns in genetic diversity where there is high variation within populations than among populations may be due to a high rate of recombination within populations and a relatively high degree of gene flow between them, preventing genetic differentiation. The Fst distances generated support that explanation, since they did not exceed 0.12 (P < 0.01). Using Nei's standard genetic distance and the UPMGA method [13], a phylogenetic tree was reconstructed (Figure 1). This tree reflected three patterns of relatedness between the chicken populations studied. The Swedish chicken formed a separate branch, Fayomi breed was grouped in a second branch, while the third group embraced the Ethiopian local chicken. Although Ethiopian ecotypes formed one large cluster, within this cluster it seems that there are two sub- clusters. One ecotype Amba was separated as a single cluster and the other three ecotypes, Seden, Dawo and Aklile were grouped in another cluster. The grouping of Dawo and Seden was not surprising since they are from the same zones (South west Showa) but the grouping of

Aklile with Dawo and Seden was not expected as its geographical location is not the same with Dawo and Seden. The reason for clustering of Aklile with Dawo and Seden might be due to the fact that this district is a newly established district and the two study zones (South west Showa and Gurage) are adjacent to it. So, the source of initial stock for Aklile district could be Dawo and Seden ecotypes. This is in agreement with the information obtained from the result of the first part of this study that is characterization of village chicken production system in South and South western part of Ethiopia which reports 83.8% of the initial stock in Aklile district is obtained by purchasing. Still Seden formed a distinct group from Dawo and Aklile and this makes sense because Dawo is situated in the western most area of Ethiopia relative to the other three districts. In general, indigenous chickens are considered as a national asset and a key factor in creating sustainable agriculture in developing countries. Precise assessment of such native genetic resources is of great importance and could be utilized for the purpose of their conservation, management, reproduction and further exploitations [22]. Although, the genetic distance among the local ecotypes (0.034 to 0.074) presented in this study is low, there is a considerable genetic distance of the local ecotypes from Fayomi breed and Swedish chicken population and this is reflected by a phylogenetic tree which showed three patterns of relatedness between the chicken populations studied. 22

Intl. J. Genet., 5(1): 18-24, 2015

CONCLUSION

9.

Ethiopian ecotypes are different from the reference populations and there is a tendency of sub-clustering in Ethiopian ecotypes. At least two sub-clustering might exist in Ethiopian ecotypes but this needs further investigation using more number of informative markers and advanced clustering tools such as structure programme. Furthermore, studies need to be made to characterize the two sub-clusters of Ethiopian ecotypes for performance traits such as growth and egg laying.

10.

11. REFERENCES 1.

2.

3.

4.

5.

6.

7.

8.

Hartl, L.D., 1988. A primer of population genetics. 2 Ed. Sinauer Associates, Inc. Publishers. Sunderland, Massachusetts. Horst, P., 1991. Native fowl as a reservoir for genomes and major genes with direct and indirect effects on the adaptability and their potential for tropically orientated breeding plans. A Review, 1991. Animal Research and Dev. Vanhala, T., M. Tuiskula-Haavisto, K. Elo, J. Vilkki and A. Maki-Tanila, 1998. Evaluation of genetic variability and genetic distances between eight chicken lines using microsatellite markers. Poultry Science, 77: 783-790. Muhammet, K. and A.Y. Mehmet, 2008. Genetic diversity among Turkish native chickens, Denizli and Gerze, estimated by microsatellite markers. Biochem. Genet., 46: 480-491. Cheng, H.H. and L.B. Crittenden, 1994. Microsatellite markers for genetic mapping in the chicken. Poult. Sci., 73: 539-546. Laval, G., A. Lannuccelli, C.H. Legault, D. Milan, A.M. Groenen, E. Giuffra, L. Andersson, P.H. Nissen, C.B. Jorgensen, P. Beeckmann, H. Geldermann, J.L. Foulley, C. Chevalet and L. Ollivier, 2000. Genetic diversity of eleven European pig breeds. Genet. Sel. Evol., 32: 187-203. Martinez, A.M., J.V. Delgado, A. Rodero and J.L. Vega-pla, 2000. Genetic structure of the Iberian pig breeds using microsatellites. Anim. Gene., 31: 295-301. Mwacharo, J.M., K. Nomura, H. Hanada, H. Jianlin, O. Hanotte and T. Amano, 2007. Genetic relationships among Kenyan and other East African indigenous chickens. Anim. Genet., 38: 485-490.

12.

13.

14.

15.

16.

17.

18.

19.

23

Berthouly, C., B. Bed'Hom, M. Tixier-Boichard, C.F. Chen, Y.P. Lee, D. Laloë, H. Legros, E. Verrier and X. Rognon, 2008. Using molecular markers and multivariate methods to study the genetic diversity of local European and Asian chicken breeds. Anim. Genet., 39: 121-129. Heifetz, E.M., J.E. Fulton, N.P. O'Sullivan, J.A. Arthur, H. Cheng, J. Wang, M. Soller and J.C. Dekkers, 2009. Mapping QTL affecting resistance to Marek's disease in an F6 advanced intercross population of commercial layer chickens. BMC. Genomics, 10: 20. Sambrook, J., E.F. Fritsch and T. Maniatis, 1989. Molecular Cloning: A Laboratory Manual Cold Spring Harbor Laboratory Press NY. FAO, 2004. Secondary guidelines for development of national farm animal genetic resources management plans. Measurement of domestic animal diversity (MoDAD): recommended microsatellite markers. w ww. d ad. f ao. o rg /en / re fer /l i bra ry /g u de l in e /marker.pdf. Nei, M., 1978. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genet., 89: 583-590. Botstein, D., R.L. White, M. Skolnick and R.W. Davis, 1980. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am. Hum. Genet., 32: 314-331. Crooijmans, R.P.M.A., A.B.F. Groen, A.J.A. Van Kampen, S. Van Der Beek, J.J. Van Der Poel and M.A.M. Groenen, 1996. Microsatllite polymorphism in commercial broiler and layer lines estimated using pooled blood samples. Poultry Sci., 75: 904-909. Ponsuksili, S., K. Wimmers and P. Horst, 1996. Genetic variability in chickens using polymorphic microsatellite markers. Thai Journal of Agricultural Science, 29: 571-580. Wimmers, K., S. Ponsuksili, T. Hardge, A. ValleZarate, P.K. Mathur and P. Horst, 2000. Genetic distinctness of African, Asian and South American local chickens. Anim Genet, 31: 159-165. Köster, E.M., 2001. Genetic and phenotypic characterization of native fowl population in South Africa. PhD thesis. University of Pretoria. South Africa. Tadelle, D., 2003. Phenotypic and genetic characterization of chicken ecotypes in Ethiopia. Ph.D. Thesis, Humboldt University, Germany.

Intl. J. Genet., 5(1): 18-24, 2015

20. Halima, H., F.W.C. Neser, A. De Kock and E.M. Koster, 2009. Study on the genetic diversity of native chickens in North West Ethiopia using microsatellite markers. African Journal of Biotechnology, 8(7): 1347-1353. 21. Zhou, H. and S.J. Lamont, 1999. Genetic characterization of biodiversity in highly inbred chicken lines by microsatellite markers.

22. Shahbazi, S., S.Z. Mirhosseini and M.N. Romanov, 2007. Genetic diversity in five Iranian native chicken populations estimated by microsatellite markers. Biochem. Genet., 45: 63-75.

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