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Genetic Diversity Analysis of Macherla Brown Sheep Using Microsatellite Markers

Mohankishore Molabanti Punyakumari Bhupati Suresh Jeepalyam Bharathi Gutti Amol Jagannath Talokar
Vol 9(4), 84-93
DOI- http://dx.doi.org/10.5455/ijlr.20180912031912

A total of 275 alleles were observed for the twenty four marker loci under investigation in the sampled Macherla Brown population. The number of alleles at each locus varied from eight (BM6506, HSC, OarCP34) to eighteen (MAF214) with a mean of 11.458 alleles. Allele size ranged from 69 bp (OarCP20) to 313 bp (MAF214), while allele frequency varied between 0.0102 (HSC, INRA63, MAF214, OarP49 and OarVH72) to 0.2551 (HSC). The number of effective alleles across twenty four microsatellite loci studied ranged from 5.8065 (HSC) to 14.1652 (OarFCB48). The mean number of effective alleles was 9.597 ± 2.454. The observed heterozygosity ranged from 0.0612 (BM8125) to 0.2653 (OarCP34) whereas, the expected heterozygosity varied from 0.8278 (HSC) to 0.9294 (OarFCB48). All the twenty four microsatellite loci (100 percent) were found to be highly polymorphic and the PIC values varied from 0.8052 (HSC) to 0.9295 (OarFCB48) with the mean PIC value of 0.882 ± 0.036. The Chi-square test revealed that all the twenty four loci significantly deviated from Hardy-Weinberg Equilibrium. The inbreeding estimates obtained in this study were all positive and ranged from 0.6915 (OarCP34) to 0.9341 (OarFCB48) with the mean FIS value of 0.876 ± 0.057 indicating the high deficiency of heterozygotes. The bottle neck analysis revealed that population has not undergone any recent reduction. The study revealed that Macherla brown sheep with increasing population trend needs genetic management for the conservation and improvement.


Keywords : Bottleneck Macherla Brown Sheep Microsatellite Markers PIC

India is a rich repository of sheep genetic resources with 42 recognized sheep breeds and ranks 3rd in the world with 65.09 million sheep population (19th Livestock Census India., 2012). Indigenous breeds in general demonstrate low production performance when compared to exotic stock. However, they have better adaptability to pressures of the specific local environment. Adaptive traits that are usually associated with the indigenous breeds include disease resistance, heat tolerance and ability to survive under low input conditions. The Southern peninsular region is endowed with mutton breeds like Nellore, Mandya, Deccani, Hassan, Bellary, Khenguri, Coimbatore, Madras Red, Mecheri etc. Andhra Pradesh possess a sizeable population of non-descript local sheep besides Nellore and Deccani breeds. Macherla Brown, a popular local sheep found predominantly in the villages mainly adjacent to the Krishna river flowing through Prakasam, Guntur, Krishna and Nalgonda districts of Andhra Pradesh and Telangana. The farmers in this area prefer Macherla brown due to their adaptability to adverse climatic conditions. Macherla Brown sheep are characterized by convex head, pendulous ears and black muzzle. Based on coat colour, three phenotypes i.e bicolour of brown and white (61.96%), brown (36.46%) and brown and black (14.44%) were noticed. The least squares means for body weights at 2, 4, 6 and 8 teeth of age were 33.79, 39.74, 44.52 and 46.98 kg, respectively (Choudary et al., 2015).

The Food and Agricultural Organization (FAO) of the United Nations has proposed a global programme for the management of genetic resources using molecular methodology for breed characterisation (Bjornstad and Roed, 2001) and this strategy places a strong emphasis on the use of molecular markers to assist the conservation and assessment of endangered breeds and also to determine the genetic status of these breeds. Molecular DNA polymorphisms are now the tools of choice for the assessment of genetic diversity among livestock breeds and has potential for discovery of fundamental parameters important in conservation, viz., effective population size (Garrigan et al., 2002), bottlenecks (Luikart et al., 1998), population origin (Cornuet et al., 1999) and inbreeding status (Ellegren, 1999; Lynch and Ritland, 1999) of the population. Microsatellites are the most commonly and widely used molecular markers for assessing relatedness in livestock. ISAG/FAO has recommended a list of microsatellite markers for genetic diversity studies in livestock (http://www.fao.org/dad-is).

Phenotypic (Choudary et al., 2015) and cytogenetic characterization (Nityanand et al., 2017) has already been carried out for Macherla Brown sheep. But molecular information is lacking. Hence, the present study is undertaken to characterize Macherla Brown sheep at molecular level with the aid of microsatellite markers.

Materials and Methods                                             

A total of 50 blood samples of genetically unrelated sheep were collected randomly from farmers flocks and the genomic DNA was isolated by using phenol chloroform method (Sambrook et al., 1989). One per cent agarose in 1X TAE buffer (pH 8.0) was used to check the quality of genomic DNA and quantification was done by UV spectrophotometer. The yield of DNA ranged from 58.5 to 851.8 ng/µl with a mean of 170.964 ng/µl and the purity of DNA (OD ratio) ranged from 1.689 to 1.971 with a mean of 1.795. A total of twenty four microsatellites were selected based on degree of polymorphism and genomic coverage (FAO, 2004). The selected microsatellite markers complied with the recommendations of the FAO and the International Society for Animal Genetics (ISAG). PCR was carried out in a final reaction volume of 25 μl. Quantity of sample DNA added varied from 2 μl (OarVH72) to 4 μl (BM6526, OarCP34, OarHH41) and a  master mix for minimum of ten samples was prepared and aliquoted into each PCR tube to make the final volume of 25 μl. PCR amplification was done on Kyratech thermal cycler. Each 25 µl of PCR reaction mixture consist of 50-100 ng of genomic DNA, 2.5 µl of 10 X Taq buffer, 1 µl of 1.5 mM Mgcl2, 0.5 µl of 200 µM dNTPs, 0.2 µl of 1 unit Taq polymerase with 10 picomoles of 1 µl forward and reverse primers. The cycling protocol was 5 min denaturing at 94°C followed by 35 cycles of denaturation at 94°C for 45 seconds annealing at different temperatures (47 to 70°C) depending on primers for 45 seconds, extension at 72°C for 45 seconds and final extension at 72°C for 10 minutes.

The PCR products were resolved on 2% agarose gels stained with ethidium bromide in 1X TAE buffer with 100 bp DNA ladder using as a size standard. The amplified product was visualized as a single compact fluorescent band of expected size under UV transilluminator and photographed with UVi-tech gel documentation system (THERMO SCIENTIFIC ®). The genotypes were scored based on the presence of a single band (homozygotes) or double bands (heterozygotes) on the agarose gel slab. Based on the size of the alleles obtained on the agarose gel electrophoresis, microsatellite allele frequencies, effective number of alleles (Ne), polymorphism information content (PIC), test of Hardy-Weinberg equilibrium, observed (Ho) and expected heterozygosity (He) and F-statistics (FIS) were calculated using the POPGENE version 1.3.2 (Yeh et al., 1999). Polymorphism information content was calculated using PIC calculator in the google home page.Genetic bottleneck effect was inferred for the population using the qualitative graphical method (mode shift analysis) under the assumption of two phase microsatellite mutation model (TPM), Infinite allele model (IAM), step wise mutation model (SMM) implemented in the programme bottle neck version 1.2.02 (Cornuet and Luikart 1996). This programme is based on the principle that any population that has experienced a recent reduction in its effective population size exhibits a correlative reduction in the allele numbers and gene diversity at polymorphic loci.

Results and Discussion

All the 24 microsatellite loci amplified successfully and were found to be polymorphic. The parameters of genetic variability estimated were detailed in Table 1. Allele diversity: A total of 275 alleles were identified across twenty four microsatellite loci with a mean of 11.458 ± 2.734 alleles.

 

Table 1: Genetic structure of Macherla 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.
BM1314 11 8.843 157 – 180 0.031 – 0.194 0.102 0.8869 0.8849 0.8869 421.7129** 55
BM6506 8 7.659 175 – 228 0.082 – 0.163 0.102 0.8694 0.8826 0.8548 294.2818** 28
BM6526 14 13.049 154 – 201 0.041 – 0.102 0.102 0.9234 0.8895 0.9234 631.0162** 91
BM757 12 9.379 166 – 216 0.020 – 0.174 0.0816 0.8934 0.9086 0.8934 506.2322** 66
BM8125 11 8.294 134 – 182 0.031 – 0.174 0.0612 0.8794 0.9304 0.8794 484.3134** 55
BM827 11 10.349 165 – 197 0.061– 0.112 0.0612 0.9034 0.9322 0.9035 494.2495** 55
CSSM31 10 8.795 112 – 147 0.020 – 0.143 0.0816 0.8863 0.9079 0.8751 389.8391** 45
CSSM47 10 7.931 146 – 209 0.021 – 0.208 0.1875 0.8739 0.7854 0.8609 304.7294** 45
HSC 8 5.806 203 – 232 0.010 – 0.255 0.0612 0.8278 0.926 0.8052 281.4185** 28
HUJ616 9 7.41 128 – 171 0.02– 0.174 0.0816 0.8651 0.9056 0.8497 319.8519** 36
INRA63 10 8.827 162 – 221 0.010 – 0.14 0.1224 0.8867 0.8619 0.8751 343.0737** 45
MAF214 18 13.839 195 – 313 0.010 – 0.122 0.1429 0.9277 0.846 0.9278 765.3591** 153
OarCP20 14 9.84 69 – 94 0.020 – 0.122 0.1224 0.8984 0.8637 0.8984 638.5662** 91
OarCP34 8 7.135 102 – 148 0.041 – 0.184 0.2653 0.8599 0.6915 0.8435 192.8396** 28
OarCP49 16 12.571 123 – 183 0.010 – 0.112 0.102 0.9204 0.8891 0.9205 725.2924** 120
OarFCB48 11 10.791 130 – 216 0.041 – 0.102 0.0612 0.9073 0.9325 0.9074 485.1454** 55
OarFCB128 15 14.165 133– 153 0.082 – 0.122 0.0612 0.9294 0.9341 0.9295 726.5161** 105
OarHH35 13 11.857 130 – 178 0.051 – 0.112 0.102 0.9157 0.8886 0.9157 547.5290** 78
OarHH41 13 11.516 117 -162 0.031 – 0.122 0.1429 0.9132 0.8436 0.9132 504.4707** 78
OarHH47 9 7.287 126 – 171 0.020 – 0.184 0.0612 0.8628 0.929 0.8472 369.4592** 36
OarHH64 15 12.67 131– 191 0.020 – 0.122 0.1633 0.9211 0.8227 0.9211 550.0499** 105
OarJMP8 10 8.514 114 – 160 0.020 – 0.153 0.1224 0.8825 0.8613 0.8708 387.6712** 45
OarJMP29 10 7.977 142– 181 0.061 – 0.194 0.1623 0.8749 0.8133 0.8614 340.3891** 45
OarVH72 9 5.828 136 – 178 0.010 – 0.235 0.0816 0.8284 0.9015 0.8054 271.5215** 36
Mean 11.458±2.734 9.597±2.454 69- 313 0.0102-0.2551    0.1098 +0.0494    0.8891 +0.0286    0.8763 +0.0566    0.8821 +0.0362

** Highly significant (p<0.01)                     

 

The number of alleles at each locus varied from a minimum of eight (BM6506, HSC and OarCP34) to a maximum of eighteen (MAF214). The loci BM1314, BM6506, BM6526, BM757, BM8125, BM827, CSSM31, CSSM47, INRA63, MAF214, OarCP20, OarCP49, OarFCB48, OarFCB128, OarHH35, OarHH41, OarHH64, OarJMP8 and OarJMP29 have amplified more than ten alleles, whereas the rest of loci amplified less than ten alleles. The total number of alleles observed in the present study for Macherla Brown sheep were almost in conformity with the findings of earlier workers (Kunene et al., 2014; Sassi-Zaidy et al., 2014 and Sheela Manjari et al., 2018). The allele size range (69 to 313) measured in Macherla brown sheep was almost comparable to allele size reported in other sheep breeds viz. Jordan sheep (Al-Atiyat et al., 2014), Coimbatore sheep(Hepsibha et al., 2014) and Nellore Jodipi sheep (Vani et al., 2017). The mean observed number of alleles (11.458) in Macherla Brown Sheep were almost in accordance with the results noticed in Jordan sheep (12.67; Al-Atiyat et al., 2014), Balaeric sheep (13.65; Pons et al., 2015), while Ligda et al. (2009) in Greek sheep (8.34) breeds and Jyotsana et al. (2010) in Patanwadi (8.25) and Marwari (9.05) breeds observed lower mean number of alleles. The effective number of alleles varied between 5.806 (HSC) to 14.165 (Oar FCB 48) with an overall mean of 9.597 ±2.454. The mean number of effective alleles (9.597) recorded in the study was in harmony with Sassi-Zaidy et al. (2014) in Fat-tailed Barbarian sheep breed (8.66). The effective number of alleles in Macherla Brown sheep is higher than those reported by Kumarasamy et al. (2009) in Coimbatore (4.93), Pandey et al. (2009) in Bonpala (2.47), Selvam et al. (2009) in Madras Red (6.98), Radha et al. (2011) in Kilakarsal (3.85) Sheep breeds, whereas, Ghazy et al. (2013) reported higher mean effective allelic number in Rahmani (11.38) and Ossimi (11.81) breeds of Egypt with a mean of 14.13.

Earlier literature (Takezahi and Nei, 1996) revealed that microsatellite loci for genetic diversity studies should have more than four alleles in order to reduce the standard error of estimates for genetic distance. Thus the number of alleles for each locus in the present study suggested the suitability of these markers to analyse genetic diversity. The variation in allele number and size in the present study compared to published literature may be attributable to the study of unrelated local populations spread over different geographical areas, which possessed high degree of genetic variation.

Heterozygosity

The observed heterozygosity values in Macherla Brown sheep ranged from 0.0612 (BM8125) to 0.2653 (OarCP34) with a mean value of 0.1098 + 0.049, whereas, the expected heterozygosity ranged from 0.8278 (HSC) to 0.9294 (OarFCB48) with a mean value of 0.8891 + 0.029 (Table1). The observed genetic variation in Macherla Brown sheep is much lower than the expected heterozygosity and also coincided with the earlier works reported by Kunene et al. (2014), Sheela Manjari et al. (2018) and Surekha et al. (2018) in Nguni, Nellore Brown and Nellore Jodipi sheep breeds, respectively.

The expected heterozygosity values noticed in this study were almost in harmony with studies of Hepsibha et al. (2014) in Coimbatore sheep (0.6255), Al-Atiyat et al. (2014) in Jordan sheep(0.678), Das et al. (2015) in Turkey sheep breeds (0.734) and Pons et al. (2015) in Balearic sheep (0.69). Expected heterozygosity is considered to be a better estimate of the genetic variability, while, the value obtained in the present study indicated that the population had retained several alleles and this correlates with the existence of genetic variability in overall population of Macherla Brown sheep.

Within Population Inbreeding Estimate

The FIS values in the sampled population ranged from 0.6915 (OarCP34) to 0.9341 (OarFCB48). The mean heterozygote deficit (FIS) value observed was 0.8763 + 0.057 and the FIS values reported are positive across all the twenty four loci studied in Macherla Brown sheep. A positive value of inbreeding estimate was also noticed in Vembur (0.2954; Pramod et al., 2009) and Kilakarsal sheep (0.147; Radha et al., 2011). Whereas, Hepsibha et al. (2014) in Coimbatore sheep (-0.0024) reported negative mean FIS value indicating the absence of inbreeding. Positive FIS value suggested inbreeding to be one of the main causes for lack of heterozygotes in Macherla Brown sheep. The shortage of heterozygotes and excess of homozygotes  by the investigated populations might be attributed to a number of factors, viz. assortative mating (sample relatedness), linkage with loci under selection (genetic hitch hicking), population heterogeneity or null alleles ( Nei, 1987).The foremost rationale for significant FIS values in these populations seems to be relatedness of few samples under field conditions. From the flock structure of these Macherla Brown sheep it is apparent that rams breed with all the ewes in the flock, as the rams and ewes are housed and grazed together there by no controlled mating is practiced at farmer’s level. Generally, few rams breed with all the ewes in the flock and this factor i.e. related individuals used for reproduction might be responsible for high heterozygote deficiency observed in this study.

Polymorphism Information Content

The polymorphism information content (PIC) values ranged from 0.8052 (HSC) to 0.9295 (OarFCB48) with the mean PIC value of  0.8821 + 0.0362, which was well agreed with the findings of Kumarasamy et al. (2009) in Coimbatore sheep (0.8106), Nanekarani et al. (2011) in Karakul sheep breed (0.808), Surekha (2015) in Nellore Jodipi sheep (0.819) and Kavitha et al. (2015) in Tiruchy Black sheep (0.844), while, a higher PIC values were noticed by Ghazy et al. (2013) in Egyptian sheep breeds with a mean PIC value of 0.903. Mean PIC values lower than the present study were recorded in Coimbatore (0.5851; Hepsibha et al., 2014), Balochi (0.55), Rakhshani (0.57; Wajid et al., 2014), Turkey sheep (0.705; Das et al., 2015) and Nellore Jodipi (0.753; Vani et al., 2017). The PIC values for all the twenty four microsatellite loci were more than 0.50 indicating the suitability of these markers for the genetic diversity analysis in Macherla Brown sheep and also suggested the utility of the selected set of microsatellites in molecular characterization and thereby the genetic variability of the investigated population can be exploited.

Hardy–Weinberg Equilibrium

The χ2 test values revealed the significant deviation of all the twenty four loci from Hardy-Weinberg Equilibrium which was in agreement with the findings of Selvam et al. (2009) in Madras Red, where all the ten loci showed significant departure from HWE, while, Nanekarani et al. (2011) in Karakul sheep, found that all the fifteen loci showed significant departure from HWE. Population disequilibria similar to present study was also noticed in 19 out of 27 loci (Kumarasamy et al., 2009) in Coimbatore sheep, 19 loci in Egyptian sheep breeds (Ghazy et al., 2013), 12 out of 24 loci in Coimbatore sheep (Hepsibha et al., 2014) and only ten among the 25 microsatellites studied in Tiruchy Black sheep (Kavitha et al., 2015). This deviation of all the loci of Macherla Brown sheep from HWE may be attributable to the presence of low frequency null alleles segregating at all these loci. High positive FIS (within population inbreeding estimates) values and presence of population substructure (Wahlund effect) could be the other reasons for this deviation obtained at all loci.

The qualitative graphical method of Cornuet and Luikart(1996) was used to visualize the allele frequency spectra in Fig. 1. The microsatellite alleles were classified into ten frequency classes. No mode-shift was detected in the frequency distribution of alleles and normal L-shaped curve was observed.  Similar findings were also reported by earlier research workers in Jalauni,Vembur, Nellore jodipi and Nellore Brownbreeds (Arora et al., 2008, Pramodet al., 2009, Surekhaet al., 2018, Sheela Manjariet al., 2018).

Fig. 1: Mode shift analysis

The results of bottleneck analysis using three tests viz., sign rank test, standardized difference test and Wilcoxon test in each of three models of mutations namely, infinite allele model (IAM), two phase model (TPM) and step wise mutation model (SMM) are detailed in Table 2. The results of bottle neck analysis showed that the Macherla Brown sheep population has not undergone any recent reduction in the effective population size and remained at mutation drift equilibrium.

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

Model Infinite Allele Model Two Phase Model Step wise Mutation Model
Sign rank test (No. of loci with heterozygosity excess)
Expected 14.62 14.37 13.99
Observed 24 24 24
Probability 0.00001 0 0
Standardized differences test (T2 values) 7.489 7.21 6.315
probability 0 0 0.00007
Wilcoxon test (One tail test of heterozygosity excess) 0.00000* 0.00000* 0.00000*

(*P<0.05, **P<0.01)

The present study revealed that Macherla Brown sheep has sufficient potentiality for genetic improvement with respect to body weights and meat output, by adopting appropriate breeding strategies such as selective breeding, supply of sufficient number of superior rams in the breeding tract, establishing a nucleus flock and implementing an open nucleus breeding system. Creation of awareness among the sheep farmers for better health and managemental practices, exchange of breeding rams between the populations, making timely availability of superior quality rams needs to be ensured for exploitation of genetic diversity and sustainability of this unique germplasm in its home tract. Further, integrating genetic improvement programmes for this Macherla Brown sheep with market oriented production strategies will raise the economy of its rearers and there by ensure its sustainable conservation.

Conclusion

The results in the present study suggested that there is substantial genetic variation and polymorphism across the studied loci in Macherla Brown 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. Based on population size, even though there is no threat to the population, but high inbreeding coefficient levels in studied population was suggestive of adopting effective breeding and management practices for the genetic improvement of Macherla Brown sheep.

 

 

Acknowledgement

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

References

  1. 19th Livestock Census-2012 All India Report Ministry of Agriculture Department of Animal Husbandry, Dairying and Fisheries, Government of India.
  2. Al-Atiyat, R.M., Salameh, N.M. and Tabbaa, M.J. (2014). Analysis of Genetic diversity and differentiation of sheep populations in Jordan. Electronic Journal of Biotechnology 17: 168-173.
  3. R., Bhatia, S., Sherawat, A., Maity, S.B. and Kundu, S.S. (2008). Genetic variability in Jalauni sheep of India inferred from microsatellite data. Livestock Research for Rural Development. Article no. 4 retrieved from www.cipav.org.co/irrrd20/1/1 aror20004.htm on 2/5/2008.
  4. Bjørnstad, G. andRøed, K. H. (2001). Breed demarcation and potential for breed allocation of horses assessed by microsatellite markers. Animal Genetics 32(2): 59-65.
  5. Choudary, P. V., Ekambaram, B. and Prakash, M. G. (2015). Morphological characterization of Macherla brown sheep of Andhra Pradesh. Indian Journal of Small Ruminants21(1): 103-105.
  6. Cornuet J M &Luikart G, Description & power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics, 144 (1996) 2001-2014.
  7. Cornuet, J. M., Piry, S., Luikart, G., Estoup, A., andSolignac, M. (1999). New methods employing multilocus genotypes to select or exclude populations as origins of individuals. Genetics153(4):1989-2000.
  8. Das, H., Turan, I. and Demir, S. (2015). Genetic characterization of the Akkaraman, Morkaraman and Karayaka sheep through microsatellite variation. Indian Journal of Animal Research49(2).
  9. Ellegren, H. (1999). Inbreeding and relatedness in Scandinavian grey wolves Canis lupus. Hereditas130(3): 239-244.
  10. (2004). Secondary guidelines for development of national farm animal genetic resources management plans: Measurement of domestic animal diversity (MoDAD). Recommended microsatellite markers, Rome, Italy.
  11. Garrigan, D., Marsh, P. C., and Dowling, T. E. (2002). Long-term effective population size of three endangered Colorado River fishes. Animal Conservation5(02): 95-102.
  12. 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.
  13. Hepsibha, P., Karthickeyan, S.M.K and Guru, V. (2014).Microsatellite marker based assessment of genetic structure of Coimbatore breed of Sheep (Oviesaries) in Tamil Nadu. Indian Journal of Biotechnology 13: 203-206.
  14. Jyotsana, B., Jakhesara, S., Ved Prakash., 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.
  15. Kavitha, S. T., Subramanian, A., Sivaselvam, S. N., Thiagarajan, R. and Balasubramanian, S. (2015). Genetic characterisation of Tiruchy Black sheep of Tamil Nadu using microsatellite markers. Indian Journal of Animal Research 49(3).
  16. Kumarasamy, P., Prema, S., Ganapathi, P., Karthickeyan, S.M.K. and Kanakaraj, P. (2009). Molecular Characterization of Coimbatore Breed of Sheep (Ovisaeris) in South India. The Icfai University Journal of Genetics and Evolution 11: 1-10.
  17. 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.
  18. 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.
  19. Luikart, G., Allendorf, F. W., Cornuet, J. M. and Sherwin, W. B. (1998). Distortion of allele frequency distributions provides a test for recent population bottlenecks. Journal of Heredity89(3): 238-247.
  20. Lynch, M.and Ritland, K. (1999). Estimation of pairwise relatedness with molecular markers. Genetics152(4): 1753-1766.
  21. 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.
  22. Nei M. (1987) molecular Evolutionary genetics. Columbia University press, New York, NY.
  23. Nityanand, B. Ekambaram, B. Punyakumari, B. and Suresh, J. (2017). Cytogenetic characterization of Macherla Brown sheep of Andhrapradesh. Indian Journal of Small Ruminants 23 (1):77-80.
  24. 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.
  25. Pons, A. L., Landi, V., Martinez, A. and Delgado, J. V. (2015). The biodiversity and genetic structure of Balearic sheep breeds. Journal of Animal Breeding and Genetics132(3): 268-276.
  26. Pramod, S., Kumarasamy, P., Rosalyn Mary Chandra, A., Sridevi, P. and Rahumathulla, P.S. (2009). Molecular characterization of Vembur sheep (Ovisaries) of South India based on Microsatellites. Indian Journal of Science and Technology 2: 55-58.
  27. Radha, P., Sivaselvam, S.N., Kumarasamy, P. and Kumanan, K. (2011). Genetic diversity and bottleneck analysis of Kilakarsal sheep by microsatellite markers. Indian Journal of Biotechnology, 10: 52-55.
  28. Sambrook, J. and Russell, D. W. (1989, 2001). Molecular cloning. A laboratory manual. Third. Cold Spring Harbor Laboratory Press, New York.
  29. Sassi-Zaidy, Y. B., Maretto, F., Zanetti, E., Hajji, G. M., Charfi-Cheikrouha, F. and 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.
  30. Selvam, R., Rahumathulla, P. S., Sivaselvam, S. N., Karthickeyan, S. M. K. And Rajendran, R. (2009). Molecular genetic characterization of Madras Red sheep in Tamil Nadu, India using microsatellite markers. Livestock Research for Rural Development21(4).
  31. Sheela Manjari, J., PunyaKumari, B., Ekambaram, B., Ravindra Reddy, Y., Talokar Amol, J. and Bharathi, G. (2018). Studies on genetic Variability in Nellore brown sheep using microsatellite Markers. International Journal of Livestock Research. DOI 10.5455/ijlr.20180331101808
  32. Surekha, J., PunyaKumari, B., Gnana Prakash, M., Suresh, J., and Bharathi, G. (2018). A study of genetic Variation in Nellore Sheep using Microsatellite markers. Indian journal of Small ruminants 24(2):215-220.
  33. Surekha, J. (2015). Molecular genetic characterization of Nellore sheep. M.V.Sc. Thesis submitted to Sri Venkateswara Veterinary University, Tirupathi.
  34. Takezaki, N. and M. Nei. 1996. Genetic distances and reconstruction of phylogenetic tree from microsatellite DNA. Genetics, 144:389-399.
  35. 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.
  36. Wajid, A., Wasim, M., Yaqub, T., Firyal, S., Tayyab, M., Siddique, S. and Hussain, T. (2014). Assessment of genetic diversity in Balochi and Rakhshani sheep breeds of Balochistan using microsatellite DNA markers.  Journal of Animal and Plant Sciences24(5): 1348-1354.
  37. Yeh, F. C., Yang, R. C., & Boyle, T. (1999). PopGene Version 131: Microsoft Window-based freeware for population genetic analysis. University of Alberta and Centre for International Forestry Research: 11-23.
  38. Internet Website: http://www.fao.org/dad-is
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