Printer Friendly

Evaluation of genetic variation and phylogenetic relationship among north Indian cattle breeds.

INTRODUCTION

India possesses the largest livestock population in the world including the highest number of 185.18 million cattle (Livestock Census, 2003). The indigenous zebu cattle (Bos indicus) are characterized by prominent hump, a long face, upright horn or drooping ears, dewlap and slender legs. Color varies from white to grey and black (Acharya and Bhat, 1984). Zebus have relatively lower basal metabolic rate and better capacity of heat dissipation. Therefore, they easily adapt to tropical heat and have also developed resistance to diseases, especially tick born diseases.

Potentially there is much unrecognized beneficial genetic variation present in the rare, especially the semi managed breeds and populations, which form important reservoirs of non-exploited resources. There is a tendency for world wide animal production to be based on a few, highly selected breeds which is causing pressure leading to a reduction in number of local breeds (Blott et al., 1998; Barker, 1999). In India, increasing number of cattle breeds is showing the declining trend in population. These breeds were very popular and widespread till first half of last century as draft or dual purpose breeds, but registered a consistent reduction in their number afterwards due to three major factors: mechanization in agriculture, urbanization, and competition from high yielding cross bred cattle populations. Thus, it is essential that the resources (personal and financial) available be best used to ensure that as much valuable genetic diversity as possible survives into the future. Consequently, first step in assessing genetic conservation needs is development of baseline information: evaluation of their genetic variability and their distribution among the populations.

The biological unit for conservation in domesticated animals is usually the breed. When we are selecting breeds for conservation it may be important not just to consider taxonomic distinctness or between population variations but also to take measures of within population diversity. Such measures could be included into a diversity index and population selected for conservation on the basis of this index. Microsatellite markers had been widely used to analyze phylogenetic relationships among various animal groups and different breeds (Bradley et al., 1994; Edwards et al., 2000; Pandey et al., 2006). Microsatellite loci comprise an attractive potential source of information about population histories and evolutionary processes, as these loci permit simple and accurate typing in combination with high levels of polymorphism and widespread distribution in the genome.

In order to develop objective criteria for conservation of the cattle breeds of northern region viz. Ponwar, Kherigarh, Gangatiri and Kenkatha, we collected data for 20 microsatellite loci in these breeds to determine genetic relationship between them.

MATERIALS AND METHODS

Sample collection and DNA extraction Blood samples were collected from total of 181 individuals representing four cattle breeds: 40 samples from Ponwar breed (P) and 47 each of Kherigarh (K), Gangatiri (G) and Kenkatha breeds (Kn), respectively. In Sampling we attempted to avoid closely related animals and sampled the animals that met the standards for each breed. All these breeds were sampled from their breeding tracts in different districts of Uttar Pradesh state of India. Ponwar cattle were sampled from Puranpur and Madhotanda subdivision of Pilibhit district. Kherigarh cattle were sampled from Nihasan and Pallia subdivision of Lakhimpur- Kheri district. Gangatiri cattle were sampled from Ballia district and Kenkatha was sampled from Banda district. DNA was extracted from blood samples as per the standard protocol (Maniatis et al., 1982).

Microsatellite analysis

A set of 20 microsatellite markers (Table 1) recommended for cattle in FAO's DADIS MoDAD programme were utilized for generating microsatellite genotyping data. Since microsatellite markers are co-dominant, 181 samples correspond to 362 alleles for each microsatellite locus. An amalgamation of 20 co-dominant loci and 181 samples were projected to create 7240 allelic data for the population included in this study. Polymerase Chain Reaction (PCR) was performed utilizing 50-100 ng genomic DNA in a 25 [micro]l reaction volume using PTC-200 PCR machine (MJ Research Inc., MA, USA). The PCR reaction cycle was accomplished by denaturation for 1 min at 95[degrees]C, 30 cycles of '95[degrees]C for 1 min, precise annealing temperature of primer for 1 min, 72[degrees]C for 1 min' and finally extension at 72[degrees]C for 5 min. The PCR products were resolved on 6% denaturing polyacrylamide gels (Sequi GT System, Bio-Rad) and silver stained according to protocol given by Bassam et al. (1991). All the microsatellite markers showed reproducible and discernable bands. Exact allelic size was determined by direct comparisons with the 10 bp ladder (Invitrogen, Life Technologies, CA, USA). Size of the alleles was calculated online using 'INCHWORM' programme (http://www.molecular workshop.com/programs/inchworm.html).

Statistical analysis

Allele frequency, the mean number of alleles per locus, observed heterozygosity, and heterozygosity expected from Hardy-Weinberg assumptions for each locus were computed using the POPGENE software package (Yeh et al., 1999). The two measures of heterozygosity are highly correlated, but our study focused on the expected heterozygosity since it is considered to be a better estimator of the genetic variability present in a population. The computer program FSTAT (Goudet, 1995) was used to obtain the estimates of inbreeding coefficients and population sub division based on F-statistics (Weir and Cockerham, 1984). Nei's standard genetic distance (D) (Nei, 1978), Nei's genetic distances [D.sub.A] (Nei, 1983) and Cavalli-Sforza and Edwards Chord distance DC (Cavalli-Sforza and Edwards, 1967) were estimated using MICROSATELLITE ANALYZER (MSA) version 3.15 (Dieriger and Schlotterer, 2003). Pairwise distance matrix based on the proportion of the shared alleles with populations as taxonomic unit was utilized to construct UPGMA tree using PHYLIP version 3.5 (Felsenstein, 1993) and the tree was visualized using TREEVIEW version 1.6.6 software (Page, 1996). Breed differentiation was further investigated using Bayesian clustering approach implemented in STRUCTURE program (Pritchard et al., 2000). Individual animals were assigned to different clusters based on their multilocus genotypes. Admixture model was used with a burn in period of 1,000,000 iterations and 100,000 Markov Chain Monte Carlo (MCMC) repetitions to calculate the probable number of genetic clusters. The program STRUCTURE was first run with K = 1 to K = 5, so as to model the whole data set. The best value of ln Pr (X/K) was obtained for K = 3 (-9,915.4). These three inferred populations would correspond to the "ancestral" populations from which our current breeds were derived.

RESULTS AND DISCUSSION

The twenty investigated microsatellites represented 15 autosomal chromosomes in cattle. Total number of alleles at each locus and their size range are presented in Table 1. The observed and effective number of alleles and observed and expected heterozygosity are presented in Table 2. A total of 163 alleles were observed at the twenty loci analyzed. All the loci were polymorphic (raw microsatellite data and allele frequencies are available from corresponding author). Mean number of alleles (MNA) observed per locus were between 5.75 (Kenkatha) and 6.05 (Kherigarh) while number of effective alleles were between 3.19 (Kenkatha, Gangatiri) and 3.59 (Kherigarh) in these cattle breeds. MNA per locus in the individual breed varied from 3 (ILSTS011) to 11 (ILSTS034) in P, 3 (BM1824) to 10 (ILSTS034) in K, 3 (ETH10, ILSTS011 and INRA063) to 11 (ILSTS034) in G and 2 (ILSTS011) to 11 (ILSTS034) in Kn. Although several alleles were found uniquely in each cattle breed, they are unlikely to be useful as breed markers due to their low frequencies in the studied sample size.

The mean observed ([H.sub.o]) and expected ([H.sub.e]) heterozygosity values averaged over loci showed an overall pattern similar to that obtained for MNA per locus. The observed and expected heterozygosity of all the breeds varied from 0.48 (G) to 0.58 (K) and 0.65 (Kn) to 0.70 (K), respectively. The results of microsatellite analysis revealed relatively high degree of heterozygosity in all the four populations. The expected mean hetyerozygosities varied from 0.65 to 0.70 indicating that there are no appreciable differences in the level of genetic variability among these cattle breeds. Kherigarh had the largest genetic variability followed by Ponwar, Gangatiri and Kenkatha in the descending order. Surprisingly even in comparatively small population such as Ponwar (10,000 heads) and Kherigarh (15,000 heads) the mean expected heterozygosity ([H.sub.e]) was 0.69 and 0.70 respectively. [H.sub.e] of four breeds was comparable to the He reported for other Indian breeds including Sahiwal (0.61), Haryana (0.66), Red Kandhari (0.64), Deoni (0.69) and Hallikar (0.785) (Mukesh et al., 2004; Sodhi et al., 2005; NaveenKumar et al., 2006).

Heterozygote deficiency ([F.sub.IS]) estimates for all the breeds indicated significant deficit of heterozygotes, being 25.9%, 17.7%, 28.8% and 17.7% in P, K, G and Kn, respectively (Table 3). Similarly heterozygote deficiency has been observed in some other Indian native cattle; Sahiwal (32.6%), Hariana (21.1%) and Deoni (17.2%) (Mukesh et al., 2004) and Red Kandhari (27.8%) (Sodhi et al., 2005). Numerous factors, such as inbreeding, genetic hitchhiking, null alleles (non amplifying alleles) and the occurrence of population substructure (Wahlund effect) has been established as reasons for heterozygote deficiencies in populations (Nei, 1987). The inbreeding detected in this population is likely to be a manifestation of diminished population size coupled with lack of sufficient number of breeding males in the breeding region. Male calves of six to twelve months of age are traded to farmers outside the breeding region to be exploited in agricultural operations and transportation after castration, thus leading to their genetic death. As a result reproductable males are significantly reduced in the breeding tract. Moreover semen of these breeds is also not available in their respective area. Altogether the effective population size is curtailed and breeding between relatives stimulates inbreeding and genetic drift. Thus the fundamental cause for heterozygote deficiency in these cattle breeds is likely to be inbreeding prompted by the above expressed issues and demonstrated by the overall positive f-value.

Overall means for the F statistics across all the four breeds (Table 4) were significantly different from zero; F ([F.sub.IT]) = 0.301[+ or -]0.036 (total inbreeding estimate), f ([F.sub.IS]) = 0.223[+ or -]0.031 (within population inbreeding estimate) and . ([F.sub.ST]) = 0.106[+ or -]0.021 (measure of population differentiation). Global analysis indicated that on an average breed had a 22.3% (p<0.05) deficit of heterozygotes whereas, the total population had 30.1% (p<0.05) deficit of heterozygotes. It should be noted that levels of apparent breed differentiation were modest in UP cattle breeds. The average genetic differentiation among breeds, measured as [F.sub.ST] values was 10.6%. This is comparable with the 11% differentiation observed between Marathwada cattle breeds of India (Sodhi et al., 2005), 11.2% in seven European cattle (Mac-Hugh et al., 1998) and 10.7% in 20 north European cattle breeds (Kantanen et al., 2000). However, even this moderate differentiation is comparatively higher more than 9% observed in Swiss cattle (Schmid et al., 1999).

In a simulation study Takazaki and Nei (1996) studied the efficiency of several genetic distance measures, such as [D.sub.A] (Nei, 1983), [D.sub.C] (Cavalli-Sforza and Edwards, 1967), [D.sub.SW] (Shriver et al., 1995) and [delta][[mu].sup.2] (Goldstein et al., 1995), when applied to microsatellites and found that the accuracy of the Cavalli- Sforza and Edwards Chord distance ([D.sub.C]) and Nei's [D.sub.A] distance were generally higher than other distances. Accuracy of the dendrogram obtained from such distances, however can only be confirmed for nodes with bootstrap values above 0.70 (Lanyon, 1985) and the nodes with bootstrap values below 0.50 were not significant. Nei's [D.sub.A] distances ranged from 0.182 (between Gangatiri and Kenkatha) to 0.249 (between Ponwar and Kenkatha) and DC ranged from 0.338 (between Gangatiri and Kenkatha) to 0.400 (between Ponwar and Kenkatha) (Table 5). High degree of genetic divergence was observed for P from G and Kn by both the distance methods. Genetic divergence was highest between Ponwar and Kenkatha and lowest in Gangatiri and Kenkatha cattle breeds. Phylogenetic tree based on both the distance methods with 10,000 bootstraps showed similar pattern. Gangatiri and Kenkatha joined together with 80.6% and 71.4% bootstrap values followed by Kherigarh with 100% bootstrap value and finally by Ponwar (Figure 1). Ponwar cattle were found to be genetically distinct from all the three breeds analyzed in the present study, which coincides with the geographic location of these cattle populations. Ponwar and Kherigarh breeds are concentrated in northern part while Gangatiri and Kenkatha are distributed in southern parts of the state. Also, it has to be mentioned that morphologically too, Ponwar had been bred to have predominantly black coat colour, while the other three breeds are with white coat colour.

[FIGURE 1 OMITTED]

The program STRUCTURE computes the allelic frequencies expected in each locus for the inferred populations and also the proportion of membership of the four sampled breeds in each of the three inferred populations (Table 7). The first inferred population was basically formed by Ponwar individuals, the second by Kherigarh and the third inferred population was formed by Gangatiri and Kenkatha individuals, thus supporting the mode of clustering obtained from distance based methods. 95% of Gangatiri and 97.7% individuals of Kenkatha were found to represent the cluster 3. Theoretically, this population structure would be a consequence of the genetic background of the original populations from which present day breeds were derived. However, the inferred populations do not necessarily correspond to real ancestral populations, and they can be determined simply by the sampling scheme (Pritchard et al., 2000).

Nei's standard genetic distance (D) was used to estimate the divergence time between these breeds using the formula, D = 2[alpha]t (Nei, 1976), where, [alpha] is the assumed mutation rate and t being the time of divergence. The mutation rate was assumed to be 1.4 x [10.sup.-4]/locus/gamete (Crawford and Cuthbertson, 1996). Assuming a generation interval of 8 years (Barker et al., 1997) we obtained divergence time estimates varying from 6,912 years to 11,136 years (Table 6).

In conclusion our results show relatively high genotypic diversity within north Indian zebu cattle breeds. Among the four populations divergence of Ponwar appears to be earlier in time than other three breeds. The collected data allows an insight into the genetic structure of the analyzed populations, which reveals the relatedness of Gangatiri and Kenkatha, while Ponwar is genetically distinct from all the other three breeds.

ACKNOWLEDGMENTS

This work was supported by Network Project, Indian Council of Agricultural Research (ICAR, New Delhi). The authors are grateful to the Director, National Bureau of Animal Genetic Resources, Karnal for providing facilities. The assistance and contribution of State Animal Husbandry Department, Government of Uttar Pradesh in the collection of blood samples is also acknowledged.

REFERENCES

Acharya, R. M. and P. N. Bhat. 1984. Livestock and poultry genetic resources of India. IVRI, Research Bulletin No. 1, Indian Veterinary Research Institute, Izatnagar, U.P. India.

Barker, J. S. F. 1999. Conservation of livestock breed diversity. AGRI. 25:33-43.

Barker, J. S. F., S. S. Moore, D. J. S. Hetzel, D. Evans, S. G. Tan and K. Byrne. 1997. Genetic diversity of Asian water buffalo (Bubalis bubalis): microsatellite variation and a comparison with protein coding loci. Anim. Genet. 28:103-115.

Bassam, B. J., G. Coetano-Anolles and P. M. Gresshoff. 1991. Fast and sensitive silver staining of DNA in polyacrylamide gels. Anal. Biochem. 196:80-83.

Blott, S. C., J. L. Williams and C. S. Haley. 1998. Genetic variation within the Hereford breed of cattle. Anim. Genet. 29:202-211.

Bradley, D. G., D. G. Mac Hugh, R. T. Loftus, R. S. Sow, C. H. Hoste and E. P. Cunningham. 1994. Zebu-taurine variation in Y chromosome DNA: a sensitive assay for introgression in West African trypnotolerant cattle populations. Anim. Genet. 25:7-12.

Cavalli-Sforza, L. L. and A. W. F. Edwards. 1967. Phylogenetic analysis models and estimation procedure. Am. J. Human Genet. 19:233-257.

Crawford, A. M. and R. P. Cuthbertson. 1996. Mutations in sheep microsatellites. Genome Res. 6:876-879.

Dieringer, D. and C. Schlotterer. 2003. MICROSATELLITE ANANLYZER (MSA): A platform independent analysis tool for large microsatellite data sets. Molecular Ecology Notes 3:167-169.

Edwards, C., G. Dolf, C. Looft, R. Loftus and D. Bradley. 2000. Relationship between the endangered Pustertaler-Sprinzen and three related European cattle breeds as analyzed with 20 microsatellite loci. Anim. Genet. 31:329-332.

Felsenstein, J. 1993. PHYLIP: Phylogeny inference package. Version 3.5p, Department of Genetics, University of Washington, Seattle, WA.

Goldstein, D. V., A. Ruiz-Linares, L. L. Cavalla-Sforza and M. W. Heldman. 1995. Genetic absolute dating based on the origin of modern humans. Proc. Natl. Acad. Sci. USA. 92:6723-6727.

Goudet, J. 1995. FSTAT (version1.2): A computer programme to calculate F-statistics. J. Hered. 86:485-486.

Kantanen, J., I. Olsaker, L. E. Holm, S. Lian, J. Vilkki, K. Brusgaard, E. Eythorsdottir, B. Danell and S. Adalsteinsson. 2000. Genetic diversity and population structure of 20 North European cattle breeds. J. Hered. 91:446-457.

Livestock Census. 2003. Ministry of Statistics and Programme Implementation, Govt. of India.

MacHugh, D. E., R. T. Loftus, P. Cunningham and D. G. Bradley. 1998. Genetic structure of seven European cattle breeds assessed using 20 microsatellite markers. Anim. Genet. 29:333-340.

Maniatis, T., J. Sambrook and E. F. Fritish. 1982. Molecular cloning: A laboratory manual. 2nd ed. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York.

Mukesh, M., M. Sodhi, S. Bhatia and B. P. Mishra. 2004. Genetic diversity of Indian native cattle breeds as analyzed with 20 microsatellites. J. Anim. Breed. Genet. 121:416-424.

Naveen Kumar, S., C. S. Nagaraja, M. G. Govindaiah, S. M. Saravanan and K. Karthickeyan. 2006. Molecular characterization of hallikar breed of cattle using microsatellite markers. Asian-Aust. J. Anim. Sci. 19:622-626.

Nei, M. 1976. Mathematical models of speciation and genetic distance. In: Population genetics and ecology (Ed. S. Karlin and E. Nevo). pp. 723-766. Academic press, New York.

Nei, M. 1978. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genet. 89:583 590.

Nei, M. 1983. Genetic polymorphism and the role of nutrition in evolution. In: Evolution of genes and proteins (Ed. M. Nei and R. Koehn). pp. 165-190. Sunderland, MA: Sinauer Associates.

Nei, M. 1987. Molecular evolutionary genetics. Columbia University Press, New York.

Page, R. D. 1996. TREEVIEW: An application to display phylogenetic trees on personal computers. Computer Applications in the Biosciences, 12:357-358.

Pandey, A. K., R. Sharma, Y. Singh, B. Prakash and S. P. S. Ahlawat. 2006. Evaluation of genetic variability in kenkatha cattle by microsatellite markers. Asian-Aust. J. Anim. Sci. 19:1685-1690.

Pritchard, J. K., M. Stephens and P. Donnelly. 2000. Inference of population structure using multilocus genotype data. Genet. 155:945-959.

Schmid, M., N. Saitbekova, C. Gaillard and G. Dolf. 1999. Genetic diversity in Swiss cattle breeds. J. Anim. Breed. Genet. 116:18.

Shriver, M. D., L. Jin, E. Boerwinkle, R. Deka, R. E. Ferrell and R. Chakraborty. 1995. A novel measure of genetic distance for highly polymorphic tandem repeat loci. Genet. 12:914-920.

Sodhi, M., M. Mukesh, B. P. Mishra, K. R. Mitkari, B. Prakash and S. P. S. Ahlawat. 2005. Evaluation of Genetic differentiation in Bos indicus cattle breeds from Marathwada region of India using microsatellite polymorphism. Anim. Biotech. 16:127-137.

Takazaki, N. and M. Nei. 1996 Genetic distances and reconstruction of phylogenetic trees from microsatellite DNA. Genet. 144:389-399.

Weir, B. S. and C. C. Cockerham. 1984. Estimating F-statistics for the analysis of population structure. Evol. 38:1358-1370.

Yeh, Francis C., R-C Yang, B. J. Boyle Timothy, Z-H Ye and Judy X. Mao. 1999. POPGENE version 1.32, the user-friendly shareware for population genetic analysis. Molecular Biology and Biotechnology Centre, University of Alberta, Canada. (http://www.ualberta.ca/-fyeh/fyeh)

Rekha Sharma, A. K. Pandey *, Y. Singh, B. Prakash, B. P. Mishra, P. Kathiravan, P. K. Singh and G. Singh

National Bureau of Animal Genetic Resources, Karnal-132 001 (Haryana), India

* Corresponding Author: A. K. Pandey. Tel: +91-0184-2267276, Fax: +91-0184-2267654, E-mail: ashwni.pandey@gmail.com

Received January 20, 2007; Accepted March 20, 2008
Table 1. Characteristics of microsatellite loci analyzed
in four Indian zebu cattle

Marker Primer sequences

ETH225 gatcaccttgccactatttcct, acatgacagccagctgctact
ETH10 gttcaggactggccctgctaaca, cctccagcccactttctcttctc
ILSTS006 tgtctgtatttctgctgtgg, acacggaagcgatctaaacg
HEL1 caacagctatttaacaagga, aggctacagtccatgggatt
ILSTS011 gcttgctacatggaaagtgc, ctaaaatgcagagccctacc
MM8 cccaaggacagaaaagact, ctcaagataagaccacacc
BM1818 agctgggaatataaccaaagg, agtgctttcaaggtccatgc
INRA005 caatctgcatgaagtataaatat, cttcaggcataccctacacc
MM12 caagacaggtgtttcaatct, atcgactctggggatgatgt
INRA063 atttgcacaagctaaatctaacc, aaaccacagaaatgcttggaag
INRA035 atcctttgcagcctccacattg, ttgtgctttatgacactatccg
CSRM60 aagatgtgatccaagagagaggca, aggaccagatcgtgaaaggcatag
HAUT27 ttttatgttcattttttgactgg, aactgctgaaatctccatctta
ILSTS030 ctgcagttctgcatatgtgg, cttagacaacaggggtttgg
ILSTS054 gaggatcttgattttgatgtcc, agggccactatggtacttcc
ILSTS033 tattagagtggctcagtgcc, atgcagacagttttagaggg
ILSTS005 ggaagcaatgaaatctatagcc, tgttctgtgagtttgtaagc
HEL9 cccattcagtcttcagaggt, cacatccatgttctcaccac
BM1824 gagcaaggtgtttttccaatc, cattctccaactgcttccttg
ILSTS034 aagggtctaagtccactggc, gacctggtttagcagagagc

 Annealing
 Chromosome temp. No of
Marker number ([degrees]C) alleles

ETH225 9 57 10
ETH10 5 55 8
ILSTS006 7 56 8
HEL1 15 55 9
ILSTS011 14 58 4
MM8 2 55 8
BM1818 23 58 9
INRA005 12 55 7
MM12 9 55 10
INRA063 18 55 6
INRA035 16 55 10
CSRM60 10 55 11
HAUT27 26 55 6
ILSTS030 2 55 6
ILSTS054 21 55 9
ILSTS033 12 55 9
ILSTS005 10 55 5
HEL9 8 59 8
BM1824 1 55 6
ILSTS034 5 57 14

 Size range
Marker (bp)

ETH225 142-162
ETH10 208-222
ILSTS006 277-307
HEL1 101-121
ILSTS011 261-269
MM8 123-145
BM1818 256-286
INRA005 137-151
MM12 100-136
INRA063 178-194
INRA035 104-124
CSRM60 97-121
HAUT27 147-159
ILSTS030 145-155
ILSTS054 133-151
ILSTS033 137-163
ILSTS005 180-192
HEL9 150-170
BM1824 180-198
ILSTS034 142-212

Table 2. Genetic variation in 4 Indian cattle breeds including
observed ([N.sub.a]) and expected ([N.sub.e]) alleles per locus,
observed ([H.sub.o]) and expected ([H.sub.e]) heterozygosity

 Ponwar Kherigarh

 [N.sub.a] [H.sub.o] [N.sub.a]
Locus ([N.sub.e]) ([H.sub.e]) ([N.sub.e])

ETH225 7 (2.40) 0.25 (0.59) 8 (2.68)
ETH10 8 (4.03) 0.45 (0.76) 4 (1.97)
ILSTS006 5 (3.29) 0.62 (0.71) 5 (3.81)
HEL1 5 (2.33) 0.59 (0.58) 6 (2.80)
ILSTS011 3 (2.09) 0.48 (0.53) 4 (2.46)
MM8 7 (2.47) 0.31 (0.60) 7 (4.11)
BM1818 6 (2.14) 0.35 (0.54) 8 (3.77)
INRA005 4 (3.51) 0.54 (0.73) 6 (3.75)
MM12 7 (3.51) 0.53 (0.73) 8 (5.82)
INRA063 5 (2.11) 0.53 (0.53) 6 (1.96)
INRA035 7 (5.34) 0.78 (0.82) 8 (5.48)
CSRM60 7 (3.47) 0.34 (0.73) 5 (3.54)
HAUT27 5 (3.10) 0.22 (0.69) 5 (2.73)
ILSTS030 4 (3.16) 0.30 (0.69) 5 (4.76)
ILSTS054 6 (3.92) 0.55 (0.75) 6 (4.24)
ILSTS033 5 (2.61) 0.63 (062) 4 (2.63)
ILSTS005 5 (4.19) 0.77 (0.77) 5 (3.52)
HEL9 6 (4.86) 0.80 (0.81) 8 (3.69)
BM1824 6 (2.89) 0.59 (0.66) 3 (2.25)
ILSTS034 11 (7.42) 0.57 (0.89) 10 (5.79)
Mean 5.95 (3.44) 0.51 (0.69) 6.05 (3.59)
SD 1.73 (1.31) 0.17 (0.10) 1.82 (1.90)

 Kherigarh Gangatiri

 [H.sub.o] [N.sub.a] [H.sub.o]
Locus ([H.sub.e]) ([N.sub.e]) ([H.sub.e])

ETH225 0.57 (0.63) 8 (2.40) 0.43 (0.59)
ETH10 0.27 (0.50) 3 (2.29) 0.24 (0.57)
ILSTS006 0.70 (0.75) 5 (2.86) 0.33 (0.66)
HEL1 0.58 (0.65) 8 (3.00) 0.56 (0.67)
ILSTS011 0.47 (0.60) 3 (2.38) 0.50 (0.59)
MM8 0.68 (0.76) 5 (2.36) 0.23 (0.58)
BM1818 0.61 (0.74) 7 (2.82) 0.30 (0.65)
INRA005 0.67 (0.74) 5 (4.09) 0.63 (0.76)
MM12 0.48 (0.84) 8 (4.02) 0.40 (0.76)
INRA063 0.49 (0.49) 3 (2.13) 0.47 (0.54)
INRA035 0.70 (0.83) 8 (5.93) 0.62 (0.84)
CSRM60 0.56 (0.73) 6 (3.09) 0.40 (068)
HAUT27 0.36 (0.64) 5 (1.71) 0.18 (0.42)
ILSTS030 0.81 (0.79) 4 (2.31) 0.32 (0.57)
ILSTS054 0.57 (0.77) 7 (3.44) 0.67 (0.72)
ILSTS033 0.51 (0.63) 7 (2.40) 0.62 (0.59)
ILSTS005 0.68 (0.73) 5 (4.01) 0.80 (0.76)
HEL9 0.66 (0.74) 7 (5.13) 0.54 (0.82)
BM1824 0.51 (0.56) 4 (2.30) 0.62 (0.57)
ILSTS034 0.65 (0.84) 11 (5.20) 0.64 (0.82)
Mean 0.58 (0.70) 5.95 (3.19) 0.48 (0.66)
SD 0.13 (0.10) 2.11 (1.17) 0.17 (0.11)

 Kenkatha

 [N.sub.a] [H.sub.o]
Locus ([N.sub.e]) ([H.sub.e])

ETH225 6 (2.10) 0.38 (0.53)
ETH10 4 (2.63) 0.45 (0.63)
ILSTS006 8 (3.39) 0.39 (0.71)
HEL1 5 (2.32) 0.56 (0.58)
ILSTS011 2 (1.53) 0.31 (0.35)
MM8 6 (2.89) 0.76 (0.66)
BM1818 6 (2.90) 0.55 (0.66)
INRA005 5 (4.49) 0.62 (0.79)
MM12 8 (4.16) 0.67 (0.77)
INRA063 4 (1.42) 0.32 (0.30)
INRA035 8 (5.47) 0.76 (0.83)
CSRM60 8 (3.51) 0.38 (0.72)
HAUT27 4 (2.37) 0.23 (0.58)
ILSTS030 4 (2.61) 0.72 (0.62)
ILSTS054 6 (4.55) 0.83 (0.79)
ILSTS033 5 (2.80) 0.40 (0.65)
ILSTS005 5 (3.45) 0.74 (0.72)
HEL9 5 (4.64) 0.55 (0.79)
BM1824 5 (2.15) 0.43 (0.54)
ILSTS034 11 (4.33) 0.58 (0.78)
Mean 5.75 (3.19) 0.53 (0.65)
SD 2.02 (1.12) 0.18 (0.14)

Table 3. Heterozygote deficiency ([F.sub.is]) in four
cattle breeds

Locus Ponwar Kherigarh Gangatiri Kenkatha

ETH225 0.581 0.109 0.271 0.280
ETH10 0.416 0.467 0.585 0.276
ILSTS0 0.129 0.071 0.498 0.454
HEL1 -0.027 0.107 0.174 0.025
ILSTS1 0.102 0.222 0.148 0.111
MM8 0.493 0.111 0.613 -0.144
BM1818 0.354 0.182 0.536 0.167
INRA00 0.254 0.092 0.184 0.218
MM12 0.277 0.432 0.483 0.124
INRA63 0.014 0.009 0.133 -0.066
INRA35 0.057 0.160 0.267 0.087
CSRM60 0.528 0.226 0.412 0.481
HAUT27 0.680 0.435 0.565 0.614
ILSTS3 0.567 -0.013 0.448 -0.163
ILSTS5 0.273 0.258 0.061 -0.040
ILSTS3 -0.001 0.186 -0.051 0.387
ILSTS0 -0.001 0.060 -0.048 -0.028
Hel09 0.010 0.108 0.345 0.307
BM1824 0.112 0.094 -0.082 0.198
ILST034 0.352 0.222 0.213 0.255
Mean 0.259 0.177 0.288 0.177
SE 0.050 0.030 0.049 0.046

Table 4. Global F-statistics across four cattle breeds

Locus [F.sub.IT] [F.sub.ST] [F.sub.IS]

ETH225 0.522 0.325 0.292
ETH10 0.581 0.271 0.429
ILSTS0 0.353 0.078 0.298
HEL1 0.290 0.230 0.078
ILSTS1 0.176 0.026 0.154
MM8 0.274 0.039 0.245
BM1818 0.469 0.241 0.301
INRA00 0.207 0.032 0.181
MM12 0.379 0.072 0.331
INRA63 0.063 0.031 0.034
INRA35 0.159 0.015 0.146
CSRM60 0.474 0.110 0.409
HAUT27 0.586 0.038 0.570
ILSTS3 0.254 0.105 0.167
ILSTS5 0.203 0.080 0.133
ILSTS3 0.329 0.216 0.144
ILSTS0 0.003 0.006 -0.003
Hel09 0.267 0.079 0.204
BM1824 0.114 0.035 0.082
ILST034 0.325 0.093 0.256
Mean 0.301 0.106 0.142
SE 0.036 0.021 0.032

Table 5. Genetic distance between 4 Indian zebu breeds
on the basis of 20 markers

 Ponwar Kherigarh Gangatiri Kenkatha

Ponwar 0.000 0.20136 0.24467 0.24893
Kherigarh 0.360 0 0.19379 0.23797
Gangatiri 0.399 0.363 0 0.18159
Kenkatha 0.400 0.399 0.338 0

Above diagonal [D.sub.A] Nei's genetic distance (Nei 1983), below
diagonal DC Chord distance (Cavealli-Sforza and Edwards, 1967).

Table 6. Estimated divergence time of the four cattle breeds
on the basis of 20 microsatellite loci

Breeds Generations Years

Ponwar-Kherigarh 971.4 7,721
Ponwar-Gangatiri 1,392.5 11,136,
Ponwar-Kenkatha 1,322.1 10,576
Kherigarh-Gangatiri 864.3 6,912
Kherigarh-Kenkatha 1,325.7 10,606
Gangatiri-Kenkatha 1,021.8 8,174

Table 7. Proportion of membership of each of the four cattle
breeds in each of the inferred clusters using the program

STRUCTURE
 Inferred clusters

Source populations
 1 2 3

Ponwar 0.968 0.020 0.012
Kherigarh 0.016 0.964 0.020
Gangatiri 0.014 0.037 0.950
Kenkatha 0.015 0.008 0.977
COPYRIGHT 2009 Asian - Australasian Association of Animal Production Societies
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2009 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Sharma, Rekha; Pandey, A.K.; Singh, Y.; Prakash, B.; Mishra, B.P.; Kathiravan, P.; Singh, P.K.; Sing
Publication:Asian - Australasian Journal of Animal Sciences
Article Type:Report
Geographic Code:9INDI
Date:Jan 1, 2009
Words:4918
Previous Article:Lmbr1 expression in early embryo development stages in White Leghorn and Chinese Silky.
Next Article:Inhibition of human [CD8.sup.+] cytotoxic T lymphocyte (CTL)-mediated cytotoxicity in porcine fetal fibroblast cells by overexpression of human...
Topics:


Related Articles
Mitochondrial DNA diversity and origin of Red Chittagong cattle.
Genetic structure and composition of genetic diversity in the Kouchi sub-breed of the Japanese Brown cattle population.
Genetic diversity of wild quail in china ascertained with microsatellite DNA markers.
Study on genetic diversity of six duck populations with microsatellite DNA.
Genetic variation and divergence among swamp buffalo, river buffalo and cattle: a microsatellite survey on five populations in China.
Genetic relationships among Australian and Mongolian fleece-bearing goats.
Evaluation of genetic effects of demographic bottleneck in Muzzafarnagri sheep from India using microsatellite markers.
Genetic evaluation and calculating daughter yield deviation of bulls in Iranian Holstein cattle for milk and fat yields.

Terms of use | Privacy policy | Copyright © 2020 Farlex, Inc. | Feedback | For webmasters