NAAS Score – 4.31

Free counters!


Previous Next

Studies on Genetic Variability in Nellore Brown Sheep Using Microsatellite Markers

J. Sheeela Manjari B. Punya Kumari B. Ekambaram Y. Ravindra Reddy Talokar Amol J. G. Bharathi
Vol 8(11), 149-157

Genetic variation in Nellore Brown sheep was investigated using 24 microsatellite loci. A total of fifty blood samples collected at random from unrelated Nellore Brown sheep maintained by farmers its breeding tract of Andhra Pradesh. A total of 216 alleles were identified in the study. The number of alleles at each locus varied from four (HUJ616) to thirteen (OarCP49) with a mean of 9.0. Allele size ranged from 75(OarCP49) to a maximum of 297 bp (HSC), while allele frequencies ranged from 0.0102 to 0.3980. The number of effective alleles, observed heterozygosity and expected heterozygosity range found to be 3.8757(HUJ616) to 9.6232 (OarCP49), 0.0408(BM1314) to 0.1429 (OarHH35, OarHH41) and 0.7420(HUJ616) to 0.896(OarCP49) with an overall mean of 6.78 ± 1.79, 0.0985 ± 0.025 and 0.8409 ± 0.046 alleles, respectively. All the loci found to be highly polymorphic and the PIC values ranged from 0.6945(HUJ616) to 0.8961(OarCP49) with a mean of 0.8240 ± 0.061. The inbreeding estimates obtained were all positive and varied from 0.8318(OarHH35) to 0.9534 (BM1314).The mean FIS value of 0.8825 ± 0.03 indicated the deficiency of heterozygotes. The Chi-square test revealed that all the 24 loci were showing significant deviation from Hardy-Weinberg Equilibrium.

Keywords : Genetic Variability Microsatellite markers Nellore Brown Sheep

Sheep biodiversity in India is characterized by high degree of endemism and variations in agro climatic conditions of the different regions has led to the development of various breeds/strains that are well adapted to specific set of environmental conditions. These breeds have generally been named after their place of origin and some based on their prominent characteristics. Sheep contribute greatly to the agrarian economy, especially in areas where crop and dairy farming are not economical and play an important role in the livelihood of a large proportion of small and marginal farmers and landless labourers. Sheep diversity in India is reflected by 42 descript breeds that are well adapted to varied agro ecological conditions of the country. The total sheep population of the country is about 65.06 millions and occupies third position in the world (19th Livestock Census, 2012). The Southern peninsular region has highest percentage of sheep population that are exclusively used for meat production. Nellore sheep is the tallest mutton breed of India and its home tract is Nellore and Prakasam districts of Andhra Pradesh, but widely distributed throughout the state. Three varieties are distinguished phenotypically into Palla (completely white), Jodipi (white withblack patches at specified locations) and Dora/Brown (completely brown). Rams are horned and ewes are almost always polled. Nellore Brown sheep are predominantly present in Kadapa, Kurnool and parts of Anantapur districts of Andhra Pradesh. The ears are long and drooping and most of the animals carry wattles. The overall mean bodyweights of Nellore sheep were 2.95, 13.91, 17.38, 22.39 and 26.61 kg at birth, 3, 6, 9 and12 months of age, respectively (Rani et al., 2014).

Genetic diversity analysis using markers and statistical analysis of the data are the most notable tools for breed characterization and improvement. The measurement of genetic relationships between breeds and genetic heterozygosity within breeds is the task of molecular characterization by using molecular markers (FAO, 2004). Molecular markers can be used for scrutinizing genetic variations at the DNA level between poles apart from populations and individuals and easily assessed by PCR (Radko and Rychlik, 2010). Molecular level studies were carried out in Nellore Jodipi sheep (Vani, 2017 and Surekha, 2015) using ISAG (FAO) recommended microsatellites. But molecular information regarding Nellore Brown sheep is lacking. Therefore, the present investigation was planned with a view to study the genetic variability in Nellore brown sheep with the aid of microsatellite markers.

Materials and Methods

A total of 50 blood samples were collected at random from unrelated Nellore brown Sheep from farmer’s flocks predominantly present in Rayalseema region (Kadapa, Kurnool, Anantapur and parts of Chittoor districts) of Andhra Pradesh, India. Two ml of whole blood was collected under aseptic conditions from jugular vein of each animal into a 6ml EDTA coated vaccutainer. The blood samples were processed for DNA extraction by using phenol-chloroform method (Sambrook et al., 1989).The purity and concentration of DNA sample was checked by UV spectrophotometer and 1% agarose gel electrophoresis. A total of twenty four ovine microsatellites were selected based on degree of polymorphism and genomic coverage (FAO, 2004). The selected microsatellite markers complied with the recommendations of the International Society for Animal Genetics (ISAG) and FAO. PCR amplification was carried out in a final reaction volume of 25µl. Reaction mixture contained 50 to 100 ng of genomic DNA, 200 mM of each dNTP, 10 picomoles of each forward and reverse primers, 10X PCR buffer, 15 mM magnesium chloride and 1 unit of Taq DNA polymerase. The PCR reaction cycle was carried out with initial denaturation of 5 mins at 94 °C followed by 35 cycles of amplification with annealing temperature ranging from 47°C to 70 °C for different primers and final extension for 10 mins at 72 °C. The amplified PCR products are verified on 2% agarose gel.

Number of alleles, microsatellite allele frequencies, effective number of alleles, polymorphism information content (PIC), test of Hardy-Weinberg equilibrium, observed and expected heterozygosity and F-statistics were calculatedby using POPGENE version 1.32 (Yeh et al., 1999). The expected heterozygosity (He) of each microsatellite locus was measured (Nei, 1973).

= 1- Σ Pi2


Pi = frequency of the ith allele

The observed heterozygosity (H0) was calculated as the actual percentage of heterozygotes occurring in the sample population.


Estimation of Polymorphism Information Content

The polymorphism information content was calculated using the individual frequencies in which the allele occurred at each locus (Nei, 1978).


Pi      =   allele frequency at the homozygous loci

PiPj =   allele frequency at heterozygous loci

The heterozygosity and PIC values estimated using above formulas and calculated online at

Testing for Hardy-Weinberg Equilibrium

The Chi-Square test of goodness of fit was carried out from observed and the expected numbers to check whether the population was in Hardy-Weinberg equilibrium (Falconer and Mackay, 1996) at the loci studied. The bottleneck hypothesis was investigated using BOTTLENECK- 1.2.02 (Cornuet and Luikart, 1996).


Results and Discussion

A total of 216 alleles were observed across twenty four microsatellite loci studied in Nellore Brown sheep and the number of alleles at each locus varied from a minimum of four (HUJ616) to a maximum of thirteen (OarCP49) with a mean of 9.0 ± 1.83 alleles with range of allele size from 75 bp (OarCP49) to 297 bp (HSC) (Table 1).

Table1: Genetic variability measures in Nellore Brown sheep across 24 microsatellite markers

Locus No. of alleles (na) No. of effective alleles (ne) Allele sizes (bp) Allele  frequencies Observed Heterozygosity (Ho) Expected Heterozygosity (He) Inbreeding estimate (FIS) Polymorphism information content(PIC) Hardy-Weinberg equilibrium


χ2 value d.f.
BM757 7 4.2496 174 – 205 0.0102 – 0.3980 0.0816 0.7647 0.8932 0.736 233.059 21
BM827 13 8.2085 145 – 260 0.0102 – 0.1837 0.0612 0.8782 0.9303 0.8781 545.63 78
BM1314 11 8.0706 152 – 203 0.0102 – 0.1837 0.0408 0.8761 0.9534 0.876 528.801 55
BM6506 11 7.577 114 – 214 0.0102 – 0.1939 0.102 0.8711 0.8829 0.871 423.937 55
BM6526 11 9.1293 152 – 201 0.0306 – 0.1429 0.1224 0.8905 0.8625 0.8941 434.769 55
BM8125 6 4.8652 127 – 158 0.0714 – 0.3061 0.102 0.7945 0.8716 0.7647 196.873 15
CSSM31 7 5.3296 155 – 189 0.0102 – 0.2653 0.102 0.8124 0.8744 0.7859 213.097 21
CSSM47 9 6.8797 144 – 181 0.0204 – 0.2245 0.0816 0.8635 0.9045 0.8379 363.474 36
HSC 10 8.0167 196 – 297 0.0408 – 0.1939 0.102 0.8753 0.8834 0.8625 411.603 45
HUJ616 4 3.875 128 – 163 0.2143 – 0.3265 0.102 0.742 0.8625 0.6945 115.064 6
INRA63 10 5.3237 171 – 229 0.0102 – 0.2857 0.1224 0.8122 0.8492 0.7869 416.881 45
MAF214 12 7.445 187 – 289 0.0102 – 0.2041 0.1224 0.8657 0.8586 0.8656 438.665 66
OarCP20 8 7.5266 84 – 96 0.0612 – 0.1633 0.0816 0.8671 0.9059 0.8519 305.302 28
OarCP34 5 4.2885 113 – 128 0.1176 – 0.3431 0.0784 0.7668 0.8977 0.7307 177.354 10
OarCP49 13 9.6232 75 – 128 0.0204 – 0.1735 0.0816 0.8961 0.9089 0.8961 662.41 78
OarFCB48 9 5.7716 135 – 195 0.0204 – 0.2959 0.0816 0.8267 0.9013 0.8061 392.109 36
OarFCB128 12 9.4528 98 – 129 0.0306 – 0.1735 0.0816 0.8942 0.9087 0.8977 521.54 66
OarHH35 9 6.6418 114 – 162 0.0408 – 0.2653 0.1429 0.8494 0.8318 0.8328 341.15 36
OarHH41 9 7.8592 124 -161 0.0510 – 0.1633 0.1429 0.8728 0.8363 0.8591 325.094 36
OarHH47 10 7.732 119 – 158 0.0306 – 0.1939 0.1224 0.8707 0.8594 0.8568 347.854 45
OarHH64 9 7.7452 142 – 186 0.0102 – 0.1531 0.0816 0.8709 0.9063 0.8568 333.651 36
OarJMP8 10 7.622 145 – 198 0.0102 – 0.1837 0.1224 0.8688 0.8591 0.8546 340.882 45
OarJMP29 6 4.831 156 – 188 0.0408 – 0.2959 0.1224 0.793 0.8456 0.7623 175.498 15
OarVH72 5 4.1468 127 – 147 0.0612 – 0.3061 0.0816 0.7589 0.8924 0.7183 154.356 10
Mean 9.0±1.83 6.78±1.79 75-297 0.0102-0.3980 0.0985±0.25 0.8409±0.046 0.8825±0.03 0.8240±0.061    

** Highly significant (p<0.01)                     

The total number of alleles (216) observed in the present study in Nellore Brown sheep were higher than those reported in indigenous sheep breeds by Hepsibha et al., 2014 in Coimbatore sheep (134), Surekha., 2015 in Nellore sheep (129) and Vani., 2017 in Nellore sheep (75). While Tolone et al., 2012 in Sicilian sheep (259), Ghazy et al., 2013 in Egyptian sheep (401), Kunene et al., 2014 in Nguni sheep (253), Sassidy-Zaidy et al., 2014 in Barbarine sheep (270) and Hoda et al., 2014 in Albanian sheep (348) breeds reported higher values than the present findings. The allele size range (75 to 297 bp) measured in the Nellore brown sheep were comparable to allele sizes reported by Musthafa et al. (2012) in Najdi sheep, Al-Atiyat et al. (2014) in Jordan sheep and Hepsibha et al. (2014) in Coimbatore sheep, Hoda et al. (2014) in Albanian sheep, Sassidy-Zaidy et al. (2014) in Barbarine sheep and Surekha (2015) in Nellore Jodipi sheep. The mean number of observed alleles (9.0 ± 1.83) estimated were similar to the earlier reports of Ligda et al. (2009) in Greek sheep breeds, Jyotsana et al. (2010) in Patanwadi and Marwari breeds, but higher than the findings of Surekha (2015) and Vani (2017) in Nellore Jodipi sheep and lower than the findings of Crispim et al. (2014) in Pantaneiro sheep, Sassidy-Zaidy et al. (2014) in Barbarine sheep and Hoda et al. (2014) in Albanian sheep. The effective number of alleles at each locus provides information on occurrence of predominant alleles and the number of effective alleles across twenty four microsatellite loci studied ranged from 3.88 (HUJ616) to 9.62 (OarCP49). The mean number of effective alleles (6.7821) recorded in the present research were higher than those reported for Kail (3.9471; Ahmed et al., 2014) and Albanian (4.47; Hoda et al., 2014) sheep breeds, whereas, Ghazy et al., 2013 in Rahmani (11.38) and Ossimi (11.81) breeds of Egypt, Crispim et al. (2014) in Pantaneiro sheep (10.68), Sassiy – Zaidy et al. (2014) in Barbarine sheep (8.06) reported higher mean effective allelic number than the present findings.

The observed heterozygosity ranged from 0.0408 (BM1314) to 0.1429 (OarHH35, OarHH41) with a mean value of 0.0985 ± 0.025, whereas, the expected heterozygosity ranged from 0.7420 (HUJ616) to 0.8961 (OarCP49) with a mean value of 0.8409 ± 0.046 (Table 1).The heterozygosity values observed in the Nellore Brown sheep were within the range of values noticed in Nali, Chokla and Garole breeds (Mukesh et al., 2006). The observed heterozygosity values were less than the expected heterozygosity for all the loci in the present study coincided with the findings of Musthafa et al. (2012), Kunene et al. (2014), Ahmed et al. (2014) and Surekha (2015) in Najdi, Nguni, Kail and Nellore sheep breeds, respectively. The expected heterozygosity values recorded (Table 1) were in accordance with studies of Nanekarani et al., 2011 in Karakul (0.831) sheep. Musthafa et al., 2012 in Najdi sheep (0.67) and Hepsibha et al., 2014 in Coimbatore sheep (0.6255) while, Pandey et al., 2009 recorded lower value for mean expected heterozygosity (0.554) in Bonpala sheep. A substantial difference was seen between observed and expected heterozygosity for all microsatellite markers. Expected heterozygosity is considered to be a better estimate of the genetic variability and a high level of expected heterozygosity obtained in the present study indicated that the population had retained the several alleles which in turn indicated the existence of genetic variability in Nellore Brown sheep.

The inbreeding estimates obtained in this study were all positive and varied from 0.8318 (OarHH35) to 0.9534 (BM1314). The mean FIS value was 0.8825 ± 0.03. A positive values for inbreeding estimates were also found in Nellore sheep (0.1889; Vani, 2017), Pantaneiro sheep (0.12; Crispim et al., 2014) and Nellore (Jodipi) sheep (0.919; Surekha, 2015), whereas, Hepsibha et al., 2014 observed a negative mean FIS (-0.0024 ± 0.07) in Coimbatore sheep. Musthafa et al. (2012) for MAF214 (0.5488) in Najdi sheep, Ghazy et al. (2013) in Egyptian sheep breeds for HSC (0.244) and OarCP34 (0.313), Hepsibha et al. (2014) in Coimbatore sheep breed for loci OarCP49 (0.3084), OarHH64 (0.4733), BM827 (0.1802), OarJMP8 (0.2485) and INRA63 (0.1959) and Surekha 2015 in Nellore sheep for loci BM827, BM6506, HUJ616 and OarHH41 (1.00) reported fairly high FIS values similar to the present study. The inbreeding coefficient measures the reduction of heterozygosity in an individual because of non- random mating within population and hence FIS values significantly higher and lower than zero reveal inbreeding or outbreeding, respectively. High FIS values noticed in the present study might be due to the consanguinity present in the population and subpopulations. Other reasons might be that the locus is under selection, genetic hitchhiking effect, null alleles and presence of population sub-structure (Wahlund effect). The Polymorphism Information Content (PIC) values for the twenty four loci ranged from 0.6945 (HUJ616) to 0.8961 (OarCP49). The mean PIC value estimated for Nellore Brown sheep in the present study (0.8240 ± 0.061) was in conformity with the mean PIC values estimated by Hoda et al. (2014) in Albanian sheep (0.708), Surekha (2015) in Nellore Jodipi sheep (0.819) and Vani (2017) in Nellore sheep (0.7582). Mean PIC values lower than the present study were reported in Najdi (0.71; Musthafa et al., 2012), Coimbatore (0.5851; Hepsibha et al., 2014), Kail (0.60; Ahmed et al., 2014) and Nguni sheep (0.52; Kunene et al., 2014). The PIC values observed by Ghazy et al. (2013) in Egyptian sheep breeds ranged from 0.88 to 0.92 with a mean PIC value of 0.903, which was higher than the value obtained in the present study. The PIC values for all the twenty four loci were more than 0.50 indicating high polymorphism across all the loci studied.

The χ2 test revealed that all the twenty four loci were showing significant deviation (P < 0.01) from Hardy-Weinberg Equilibrium. Population disequilibria similar to present study was also noticed in 19 loci in Egyptian sheep breeds (Ghazy et al., 2013) and 12 out of 24 loci in Coimbatore sheep (Hepsibha et al., 2014). This deviation found at all the loci may be attributed to the presence of low frequency null alleles segregating at these loci, high positive FIS (within population inbreeding estimates) values obtained for several loci, selection process which had been progressing in the population and presence of population substructure (Wahlund effect) and systematic and dispersive processes operating in the population.

Sign test revealed little differences between the number of loci observed and expected with heterozygosity excess (Table 2).

Table 2: Statistical tests for mutation drift equilibrium at 24 microsatellite loci under different mutation models in Nellore Brown sheep

Sign Test Expected number of loci with heterozygosity excess 14.45 14.33 14.13
Observed number of loci with heterozygosity excess 24 23 20
Standardized different Test T2 Values 6.505 5.637 4.021
Wilcox Test One tailed test of heterozygosity excess 0.0000* 0.00000* 0.00002**

IAM- Infinite allele model; TPM- Two Phase Mutation Model; SMM- Step wise Mutation Model (*p≤0.05; **p≤0.01)            

Twenty out of 24 studied microsatellite loci had heterozygosity excess, while remaining showed significant (p≤ 0.01) heterozygosity deficiency under SMM model. The data set showed mutation drift equilibrium under IAM, TPM, SMM models. Standardized difference test showed non-significant heterozygosity deficiency (T2= 6.505; 5.637; 4.021) in Nellore Brown Sheep under IAM, TPM, SMM models, while Wilcox test revealed that the population had undergone recent bottle neck assuming the SMM. The mode shift test, however revealed mutation drift equilibrium in this sheep population (Fig. 1).

Fig. 1: Mode shift analysis

All above statistical tests used to study mutation drift equilibrium revealed little heterozygosity deficiency in Nellore Brown sheep population under TPM and SMM models. The statistical tests conclude that the Nellore Brown Sheep population had not faced any recent genetic bottle neck.


The results in the present study suggested that there is substantial genetic variation and polymorphism across the studied loci in Nellore sheep. The PIC values more than 0.5 at all the loci indicated the usefulness of these markers in further genetic studies in Indian sheep breeds. Mode shift analysis indicated that the Population is non-bottle necked. Based on population size, even though there is no threat to the population, but high inbreeding coefficient levels in studied population suggestive of adopting effective breeding and management practices for the genetic improvement of Nellore sheep.



The authors wish to acknowledge the Sri Venkateswara Veterinary University for the financial assistance provided in the form of Rashtriya Krishi VikasYojana project.


  1. Ahmed, Z., Babar, M. E., Hussain, T., Nadeem, A., Awan, F. I., Wajid, A. and Ali, M. M. (2014).Genetic Diversity Analysis of Kail Sheep by Using Microsatellite Markers. Journal of Animal and Plant Sciences, 24(5): 1329-1333.
  2. Al-Atiyat, R.M., Salameh, N.M. and Tabbaa, M.J. (2014). Analysis of Genetic diversity anddifferentiation of sheep populations in Jordan. Electronic Journal of Biotechnology, 17: 168-173.
  3. Cornuet, J. M. and Luikart G. (1996). Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics,144: 2001-14
  4. Crispim, A.B., de Oliveira Seno, L., do Egito, A. A., de Vargas Junior, F. M. and Grisolia, A. B.(2014). Application of microsatellite markers for breeding and genetic conservation of herds ofPantaneiro sheep. Electronic Journal of Biotechnology, 17(6): 317-321.
  5. Falconer, D.S. and Mackay, T.F.C. (1996). Introduction to Quantitative Genetics. 4th edition. Longman Group Limited, England.
  6. FAO, (2004). Secondary guidelines for development of National farm animal genetic resources management planes. Measurement of domestic animal diversity (MoDAD): Recommended microsatellite markers, Rome, Italy.
  7. Ghazy, A., Mokhtar, S., Eid, M., Amin, A., Elzarei, Md., Kizaki, K. and Hashizume, K. (2013).Genetic diversity of Three Egyptian local sheep breeds using Microsatellite markers. Research in Zoology, 3: 1-9.
  8. Hepsibha, P., Kathickeyan, S.M.K., and Guru, V. (2014). Microsatellite marker based assessment ofgenetic structure of Coimbatore breed of Sheep (Oviesaries) in Tamil Nadu. Indian Journal of Biotechnology, 13: 203-206.
  9. Hoda, A. and Marsan, P.A. (2014). Genetic Characterization of Albanian Sheep Breeds byMicrosatellite Markers, Analysis of Genetic Variation in Animals, Prof. Mahmut Caliskan(Ed.), ISBN: 978-953-51-0093-5, InTech.
  10. Jyotsana, B., Jakhesara, S., Ved, P., Rank, D. K. and Vtaliya, P. H. (2010). Genetic features of Patanwadi, Marwari and Dumba Sheep breeds (India) inferred by microsatellite markers. Small Ruminant Research, 93: 57-60.
  11. Kunene, N.W., Ceccobelli, S., Lorenzo, P.D., Hlophe, S.R., Bezuidenhout, C.C. and Lasagna, E.(2014). Genetic diversity in four populations of Nguni (Zulu) sheep assessed by Microsatellite analysis. Italian Journal of Animal Sciences, 13: 76-80.
  12. Ligda, Ch., Altarayrah, J. and Georgoudis, A. (2009). Genetic analysis of Greek sheep breeds using microsatellite markers for setting conservation priorities. Small Ruminant Research 83: 42-48.
  13. Mukesh, M., Sodhi, M. and Bhatia, S. (2006). Microsatellite -based diversity analysis and genetic relationships of three Indian sheep breeds. Journal of Animal Breeding and Genetics 123:258-264
  14. Musthafa, M.M., Aljummah, R.S. and Alshaik, M.A. (2012). Genetic diversity of Najdi sheep based onmicrosatellite analysis. African Journal of Biotechnology, 11: 14868-14876.
  15. Nanekarani, S., Amirinia, C. and Amirmozafari, N. 2011. Genetic analysis of Karakul sheep breed using microsatellite markers. African Journal of Microbiology Research 5: 703-707.
  16. Nei, M. (1973). The theory and estimation of genetic distance in genetic structure of populations. (Ed).N.E. Moton, University of Hawaii Press, Honolulu.
  17. Nei, M. (1978). Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89: 583-590.
  18. Pandey, A.K., Sharma, R., Prakash, B. and Mishra, B.P. 2009. Genetic Diversity study in Bonpala sheep breed of North-East India. Indian Veterinary Journal 86: 151-154.
  19. Radko, A. and Rychlik, T. (2010). Evaluating the usefulness of Polymorphism of microsatellite DNA markers for genetic diversity studies of sheep. Annual Animal Sciences, 10: 373-378.
  20. Rani, M., Ekambaram, B. and Punyakumari B. (2014). Biometrical measurements of Nellore Sheepunder field conditions of Andhra Pradesh. Indian Veterinary Journal, 91:17-21.
  21. Sambrook, J., Fritsch, E. and Maniatis, T. (1989). Molecular cloning. A Laboratory Manual. ColdSpring Harbour Laboratory Press, Cold Spring Harbour, New York.
  22. Sassi-Zaidy, Y. B., Maretto, F., Zanetti, E., Hajji, G. M., Charfi-Cheikrouha, F., & Cassandro, M.2014. Genetic structure and variability within and among populations of the fat-tailed Barbarine sheep breed using microsatellites markers. African Journal of Biotechnology, 13(1):44-54.
  23. Surekha, J. 2015. Molecular genetic characterization of Nellore sheep. M.V.Sc Thesis submitted toSriVenkateswara Veterinary University, Tirupathi.
  24. Tolone, M., Mastrangelo, S., Rosa, A.J.M. and Portolano, B. (2012). Genetic diversity and population structure of Sicilian sheep breeds using microsatellite markers. Small Ruminant Research,102: 18-25.
  25. Vani, S., Punyakumari, B., Gnanaprakash, M. and Rao,V. H. (2017). Microsatellite marker analysis in Nellore breed of sheep. International Journal of Science Environment and Technology, 6(1): 702-707.
  26. Yeh, F.C., Boyle, T., Rongcai, Y., Ye, Z., Xian, J.M. (1999). POPGENE, Version 1.32. A Microsoft Window Based Free Ware for Population Genetic Analysis. University of Alberta and Centre for International Forestry Research, Edmonton, AB.
  27. Site: DAHD. 2012 Official Website- http:// dahd.Nic.In / dahd/ statistics.aspx’.
Full Text Read : 2484 Downloads : 470
Previous Next

Open Access Policy