Animal species are very important for food and agriculture production and the domestication process of animal species has continued for almost last 12,000 years (http://www.fao.org/dad–is/html). There are around more than 40 domestic livestock species available but amongst them only 14 % contribute to the world’s food and agriculture production. India is rich in farm animal diversity and cattle is considered to be the most important farm animal with 219.64 million cattle population which is 16.21% of world cattle population (FAO 2001). But cattle genetic diversity is declining from last century (Sodhi et al., 2008). Decreasing genetic diversity in cattle population prevents farmers from selecting stocks or developing new breeds in response to environmental change, threats of disease, new knowledge of human nutrition requirements, changing market conditions and societal needs, all of which are largely unpredictable. There are thirty seven phenotypically recognized breeds of indigenous cattle in India (NBAGR-Annual Report 2014). Indigenous breeds have distinct merits over exotic breeds, like better disease resistance, low Rajasthan state stands 2^nd in the country in terms of milk production and 4^th in productivity of milk from indigenous cattle breeds (Basic Animal Husbandry Statistics, 2014).

Fig. 1: Distribution of cattle population

It is the only state where the population of indigenous cattle is more than 90% of the total cattle population. The population of indigenous cattle has decreased by 8.94 per cent (19^th annual census 2012 statistics). Despite of the fact that the state owes important indigenous cattle breeds like Rathi, Tharparkar, Gir, Kankrej, Haryanvi, Malvi, Naguri, Sahiwal etc., more than 50% of total indigenous cattle are still being rated as non-descript. It is therefore important that indigenous breeds of cattle are conserved, developed and proliferated through professional breeding programs and farm management. Microsatellite markers have high degree of polymorphism and co dominance making them extraordinarily informative for discrimination of breeds (Vaiman et al., 1994). It is widely used to assess the genetic diversity and population structure amongst the species for ex-situ and in-situ conservation procedures (Kantanen et al., 2000).The present study focused on genetic diversity among four native indigenous cattle breeds i.e. Rathi, Tharparkar, Kankrej and Gir using microsatellite markers and also estimated the informativeness of markers with inbreeding estimation (heterozygote deficiency) within and between selected cattle breeds population.

Material and Methods

Sample Collection

From the native breeding grounds of i.e. Rathi, Tharparkar, Gir and Kankrej, a total of 109   blood samples were collected. All the animals were randomly selected, taking into consideration that the animals should be genetically unrelated. The information of the animals was collected after consulting pedigree records maintained and interviewing the owners in detail. Blood samples were collected in tubes having EDTA as anticoagulant and further processed for DNA extraction. DNA extraction was carried out using rapid salt extraction method with some slight modifications (Aljanabi et al., 1997).

Microsatellite Analysis

A total of 18 microsatellite marker loci were chosen for the present study to detect polymorphism in breeds. PCR amplification was carried out in 25 ul reaction mixture having 1.5 mm MgCl₂, 200 µm dNTPs, 10 pMol of forward and reverse primers and 1u/µl of TaqDNA polymerase and 50-100 ng of genomic DNA samples from each individual. All the microsatellites were amplified using eppendroff thermal cycler with the following conditions: initial denaturation at 94˚C for 5 min, 30 repeated cycles of 1 min at 94˚C, 1 min for different annealing temperatures for each microsatellite loci, 72˚C for 30 seconds and a final extension of 10 min. at 72˚C. Amplified PCR products were resolved on 8% non-denaturing polyacrylamide gels. Allele size and genotypes were scored manually and data was further analyzed using different statistical tools.

Statistical Analysis

Allele frequency, observed number of alleles (Na), effective number of alleles (Ne), private alleles, observed heterozygosity (Ho) and expected heterozygosity (He) were calculated by GenAlEx 6.5 version(Peakall and Smouse, 2006; 2012). Genetic variability among the breeds were calculated by Wright’s F-statistics (Fis/Fit/Fst) (Nei’s, 1972). Genetic distance and genetic distance based phylogenetic tree were analysed by Neighbor-joining algorithm using population genetic analysis software POPGENE-1.32 version (32 bit) (Yeh et al., 1997). Hardy-Weinberg equilibrium (HWE) was estimated using GENEPOP 1.2 version (Raymond and Rousset, 1995). Fixation Index (Fis) was estimated using FSTAT version 2.9.3 (Goudet, 2001). Polymorphic Information content (PIC) for each locus was estimated according to (Botstein et al., 1980).

Results and Discussion                        

Genetic Diversity in Terms of Mean Number of Alleles

Overall 18 microsatellite markers were successfully amplified in four cattle breeds except INRA005 in Rathi, ILSTS002 and INRA005 in Tharparkar, HAUT24, ILSTS006 and INRA005 in Gir and BM1818 and ILSTS006 in Kankrej and are in accordance with the results reported by (Upreti et al., 2012) but on the contrary HAUT24 was successfully amplified in Kankrej population in the present study. A total of 236 alleles ranging 4 – 23 (Mean= 13.1) were found across 18 microsatellite loci. The most polymorphic marker was INRA035 with a total of 23 alleles.

Table 1: Diversity measures in four Indian cattle across 18 microsatellite marker

Ho= Observed heterozygosity, He= Expected heterozygosity, Na= Observed number of allele, PIC= Polymorphic information content

Breed-wise, the highest total number of alleles was detected in Kankrej (155) with a mean of 8.6 ± 4.8.  Tharparkar had lowest total number of alleles (135) with mean 7.5 ± 4.3. Deepika et al. (2014) reported mean number of alleles 10.3 in Kankrej and 9 in Tharparkar. The subsequent allelic numbers and mean observed number of allele in Rathi and Gir were 144 with a mean of 8.0 ± 3.4 and 147 with a mean 8.1 ± 4.7, respectively (Table 2). Of all the considered cattle breeds, Kankrej displayed substantially high genetic diversity in terms of mean number of alleles (MNA = 8.6).  However the three breeds, Rathi and Tharparkar and Gir displayed relatively lower diversity (MNA = 8.0, 8.0 and 8.1) which is higher than the other reported indigenous cattle breeds (Mukesh et al., 2004; Panday et al., 2006) as well as European cattle breed (Moazami et al., 1997; MacHugh et al., 1997; Kantanen et al., 2000). Similarly, lower allelic diversity has also been reported in exotic cattle-Burlina-6.7 (Dalvit et al., 2008), Brown Swiss-5.4 (Schmid et al., 1999) and Creole cattle-7.2 (Egito et al., 2007). However, allelic diversity of similar magnitude has been reported from cattle populations of state of Orissa in India (Sodhi et al., 2008; Sharma et al., 2012). Allelic diversity in cattle breeds studied lower than that reported by Sharma et al. (2015). Furthermore, 53 private alleles were also detected in four cattle breeds i.e. 11 in Rathi, 8 in Tharparkar, 15 in Gir and 19 in Kankrej suggesting genetic distinctiveness in the population sampled. The effective number of allele (Ne) at each locus was less than the observed number of allele. Ne ranged from 1.097 (ILSTS006) to 10.349(INRA035) with a mean value of 5.39. Breed wise mean effective number of alleles in four breeds i.e. Rathi, Tharparkar, Gir, and Kankrej was 5.40±2.50, 5.5±3.5, 5.2±3.1, 5.5±3.0, respectively (Table 2).

Table 2: Observed number of alleles, effective number of alleles and private allele

Na= Observed number of allele, Ne= Effective number of allele

Polymorphic Information Content

Overall loci were highly polymorphic with a 100% value of polymorphism in all the four populations. Highest percentage of polymorphic loci were found in Rathi (94.44%) population and polymorphic information content (PIC) value ranged between 0.536 (ILSTS006) and 0.895 (HEL5). Likewise in population of Tharparkar, Gir and Kankrej, polymorphic loci detected were 88.89%, 83.33% and 88.89% respectively. The overall PIC value indicating that all the markers considered for the study are highly informative (>0.5) to characterize the given four cattle breed populations. Whereas, marker INRA005 was only amplified in Kankrej breed signifying breed specific marker. The mean percentage of polymorphic loci was detected as 88.89% among the four cattle population.

Observed Heterozygosity and Expected Heterozygosity

The heterozygosity estimates at individual loci are present in Table 1. The average expected gene diversity (H_exp) ranged from 0.215 to 0.897 with an overall mean of 0.716. Observed heterozygosity (H_obs) across 18 loci ranged between 0.083 and 0.911 with a mean of 0.530. Breed-wise, the mean H_obs (H_exp) were 0.52 (0.75) in Rathi, 0.47 (0.70) in Tharparkar, 0.50 (0.68) in Gir and 0.62 (0.72) in Kankrej, respectively (Table 3).

Table 3: Observed heterozygosity, expected heterozygosity and PIC

Ho= Observed heterozygosity, He= Expected heterozygosity, PIC= Polymorphic Information Content

Thus, Rathi and Tharparkar breed showed lowest level of heterozygosity value. Where Deepika et al. (2014) reported mean H_obs (H_exp) in Tharparkar and Kankrej higher than the present study. Amongst four cattle breeds observed heterozygosity between 0.273 to 1 in Rathi, 0.071 to 0.929 in Tharparkar, 0.333 to 0.885 in Gir and 0.333 to 1.00 in Kankrej respectively. The expected Heterozygosity (He) ranged between 0.611- 0.903 in Rathi, 0.449 – 0.920 in Tharparkar, 0.661 to 0.909 in Gir and 0.715 to 0.915 in Kankrej. The present study revealed that expected heterozygosity (He) was higher than observed heterozygosity (Ho). Various factors such as inbreeding, genetic hitchhiking, null alleles and occurrence of population substructure (Wahlund effect) have been reported as the reasons for heterozygote deficiency in population (Nei, 1987).

Genetic Differentiation

The mean Fis, Fit, and Fst values of all cattle populations were 0.257±0.24, 0.3956±0.24 and 0.1859±0.23 respectively (Table 4).

Table 4­: F-stats

The overall estimates were significantly (P<0.05) different from zero. Fis statistics, ranged from -0.1655(INRA005) to 0.6856(ILSTS006). Except for two loci (INRA005 and TEXAN15), all other loci contributed significantly to the within population heterozygote deficit. Further, within population inbreeding estimates (Fis) indicated a deficit of heterozygotes ranging from 17% (Kankrej) to 37% (Tharparkar). The highest numbers of loci exhibiting deficiency in heterozygosity were observed in the Rathi and Tharparkar breeds, while the number of such loci was lowest in Kankrej breed and are in accordance with the reports of (Sodhi et al., 2011). The knowledge of the population structure of the considered cattle breeds discovered overall positive value of Fis (0.257), Fit (0.3956), and Fit>Fst (0.1859) recommending departures from arbitrary mating and proper selection of animals for a particular breed. Majority of the loci (16=18) contributed for heterozygote deficiency (Fis>0) with maximum contribution of ILSTS006 (0.6856), BM2113 (0.6758), and BM1818 (0.5596) locus. Less heterozygote deficiency in Kankrej breed detected in present study could be credited to less genetic segregation between the sub-population of this breed from the region of study (nonappearance of Wahlund impact) in accordance (Sodi et  al., 2011). The presence of comparable circumstance couldn’t be precluded for rest of the three Indian dairy cattle breeds showing heterozygotes deficiency. Other factors like null alleles (non-amplifying alleles), sample relatedness and linkage with loci under selection (genetic hitchhiking) could be the reason for this heterozygote deficiency. The genetic differentiation value (Fst) per locus varied from 0.0405 to 0.7836 with an average of 0.1859±0.23 across all the loci. Across different breed pairs, maximum differentiation was observed in Tharparkar- Gir pair (0.204) whereas Rathi-Tharparkar (0.137) was the least differentiated pair. The level of genetic differentiation among Indian cattle breeds measured in terms of Fst (18.5%) was moderate and significant.

Overall, the mean value of Fst differed significantly from zero, subdivisions within the subpopulation. Fst values ranged from 0.137(Rathi-Tharparkar) to 0.204 (Tharparkar-Gir). The analysis implied that 13.7% to 20.4%, with an average of 18.5% of the total genetic variation, corresponded to breed differences due to unique allelic differences between the breeds while the remaining 81.5% was the result of differentiation among individuals within the breed across 18 markers. The genetic differentiation value in this study has shown higher value than previously reported 0.113 (Mukesh et al., 2004) cattle breeds in India. However, the lower value of (Fst) has been reported in North European breeds Fst = 0.107 (Kantanen et al., 2000), seven European cattle breeds Fst= 0.112 (McHugh et al., 1998), and Swiss cattle Fst= 0.090 (Schmid et al., 1999). While Orissa and hill cattle (Sharma et al., 2012) likewise zebu cattle (Uzzaman et al., 2014) has been reported much lower Fst value. The genetic differentiation value in this study was relatively higher than 0.13 (Sharma et al., 2015). This might be either due to distinct barriers separating the four cattle breeds or proper breeding strategies being employed by the breeders.

Hardy-Weinberg Equilibrium

HWE (Hardy-weinberg equilibrium) test depicted that eight microsatellite loci in Rathi cattle, nine loci in Tharparkar, ten loci in Gir and nine loci  in Kankrej were in equilibrium state where as the remaining loci significantly deviated (P<0.01, P<0.05, P<0.001 ) from HWE (Table 5).

Genetic Distance and Dendogram

The genetic distance analysis amongst the four cattle populations showed the smallest distance between Rathi and Tharparkar (0.597) and maximum distance between Tharparkar and Gir (0.907). This observation was also supported by Phylogenetic tree analysis by using UPGMA method  which shows two clusters where Rathi-Tharparkar were clustered in one group of cattle population and Gir-Kankrej were clustered in another group with 100% bootstrapping (Fig. 2).

Table 5: Fixation index (Fis) and Hardy-Weinberg equilibrium

Fis= Fixation Index, HWE= Hardy-Weinberg equilibrium, SIG= Significance level

Fig. 2: Phylogenetic analysis of four cattle breeds Rathi, Tharparkar, Gir and Kankrej population were based on Nei’s (1978) Genetic distance software POPGENE- 1.32 version (32 bit).

Similarly, pair-wise Nei’s genetic distance values showed highest extent of divergence between Tharparkar and Gir (0.907) and lowest between Rathi and Tharparkar (0.597) (Table 6).

Table 6: Pairwise population matrix of Nei genetic distance

Furthermore, Phylogenetic analysis based on Nei’s genetic distance revealed that Rathi and Tharparkar are more closely related to each other than Gir and Kankrej in context of genetic relationship.

Conclusion

The present study genetic diversification among four cattle breed populations i.e. Rathi, Tharparkar, Gir and Kankrej was evaluated using 18 microsatellite markers. The mean polymorphic loci of 88.89% across the four breeds indicated highly informative capability of microsatellite markers used in the study.  Further, significant deviation of microsatellite loci studied by HWE (except 8 loci in Rathi, 9 loci in Tharparkar, 10 loci in Gir, 9 loci in kankrej) and the differences between mean observed and expected heterozygosity within the four cattle breeds suggested tendency of markers toward heterozygote deficiency and same was reflected by inbreeding estimation (Fis) within four cattle breeds population. This study inferred that highest genetic diversity was found in Kankrej breed in comparison to other indigenous breeds; however, it is also insinuate that former breeding strategies were being continued for the management of all studied breeds. Hence, there is demand to change breeding strategies and management programs for further improvement of indigenous breeds population of Rajasthan.

Acknowledgements

The present work was supported by grants from RKVY (RashtriyaKrishiVikasYojna) Bio-Informatics Project (RAJUVAS CSA-RKVY-1(11)), Bikaner, Rajasthan.

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