Genetic diversity of indigenous goat populations of north east India including West Bengal based on microsatellite markers  

G. Zaman1 , M. Chandra Shekar2
1. Department of Animal Genetics and Breeding, College of Veterinary Science, Assam Agricultural University, Khanapara, Guwahati, Assam, India
2. Department of Animal Biotechnology, College of Veterinary Science & A.H., Anand Agricultural University, Anand, India
Author    Correspondence author
Animal Molecular Breeding, 2015, Vol. 5, No. 3   doi: 10.5376/amb.2015.05.0003
Received: 24 Mar., 2015    Accepted: 13 May, 2015    Published: 21 May, 2015
© 2015 BioPublisher Publishing Platform
This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Zaman and Shekar, 2015, Genetic diversity of indigenous goat populations of north east India including West Bengal based on microsatellite markers, Animal Molecular Breeding, Vol.5, No.3, 1-7 (doi: 10.5376/amb.2015.05.0003)


This paper survey the genetic diversity and population structure of four goat populations of Northeast India including West Bengal. A total of 126 individuals were genotyped at 23 loci so as to support conservation and improvement decisions. The polymorphism of genetic diversity in populations correlated with known population histories. The study identified 440 alleles and most of the studied loci were highly polymorphic. The genetic groups under study presented HWE deviations for least number of the loci. The range of alleles was found to be 1.500 to 9.500 with a global mean of 4.848. The overall mean of observed and expected heterozygosities were 0.484 and 0.493 respectively. The within population inbreeding estimate (FIS) (0.041) indicated moderately high heterozygosity in the populations. Microsatellite analysis revealed moderately high genetic diversity in the studied genetic groups. The Analysis of Molecular Variance (AMOVA) showed that 21% of the total variation was due to differences between genetic groups. The study concluded that appropriate conservation efforts should be undertaken to maintain same levels of genetic diversity.

Heterozygosity; Microsatellites; Phylogenesis; Polymorphism Information Content; Principal Component Analysis

India is a rich repository of goat genetic resources with 23 recognized breeds and a large number of non-descript or mixed breeds. Goat with its strong presence (17% of World’s population) provides meat, milk and fiber for the ever increasing human population in India (FAO., 2000). The Northeast region of India including West Bengal state is fortunate to have rich goat genetic resources viz., Assam Local goat (ALG), Assam Hill goat (AHG), Nagaland Long Hair goat (NLHG) and Bengal goat (BG) which are very popular with their local names and also good producers of meat, fiber, quality skin as well as manure. All of these germplasm have differences in their special characteristics, such as physical features, prolificacy, meat quality and strong disease-resistance, but their growth and carcass performances are much lower than that of the commercial breeds.
Information on genetic diversity of a particular species under natural conditions will provide inputs for their domestication (FAO., 2013). Moreover, recent developments in goat farming and its utilization indicate the need to have scientific data on their genetic diversity and variation. Knowledge about genetic diversity levels and breed differentiation through microsatellite analysis on natural populations will be useful for understanding the evolutionary significant lineages, formulating management measures for sustainable exploitation and conservation of this treasured genetic resource. Apart from this, with the information on genetic diversity and population structure of these studied genetic groups, the possibility for effective utilization of these resources for the welfare of the state’s marginal farmer community can also be explored.
Recently, due to tremendous developments in the field of molecular genetics, a variety of techniques have been introduced to analyze genetic variations. Population genetic studies of livestock based on microsatellite markers are useful for the analysis of population structure and relationships as demonstrated by various studies viz., genetic diversity in Hallikar cattle (Bos indicus) (Chandra Shekar et al., 2011), Nilgiri sheep (Hepsiba et al., 2012), Votho pigs from Nagaland (Zaman et al., 2014a) and Doom pigs from Assam (Zaman et al., 2014b) as well as other livestock species from other countries. Microsatellite markers are short tandem repeats, exhibiting high degree of polymorphism and distributed throughout the genome.
Considering the importance and utility of goat in the region, the aim of this study has been planned to quantify the genetic diversity and structure of locally adapted goat populations by covering large numbers and different geographical zones from the region of North East India including West Bengal with the use of microsatellite markers.
1 Materials and Methods
1.1 Genetic stocks
A total of 126 goats representing 4 genetic groups were sampled from their native breeding tract of North East India including West Bengal. The information about native breeding tract utility and number of samples from respective site is furnished in Table 1.

Table 1 Summary of goat populations and DNA repository

1.2 Sample collection and DNA isolation
The blood samples of goats were randomly collected in EDTA coated vacutainers and samples were immediately placed on ice and transported to the laboratory and stored at 4°C until use. Genomic DNA was isolated from whole blood samples by using standard phenol-chloroform method (Sambrook et al., 1989) with few modifications like using of DNAzol (Invitrogen) reagent instead of SDS and proteinase K. The quantity and quality of isolated DNA were confirmed (230 to 403 ng/µl). The concentrated samples were diluted to reach appropriate concentrations for the purpose of PCR amplification.
1.3 Selection of microsatellite markers
The microsatellite markers were selected for the present investigation with an effort to cover different chromosomes, level of polymorphism, allele size range and reliability of allele calling to evaluate genetic diversity and structure. The labeling modifications were done in forward primers of each marker with either FAM, NED, PET or VIC dye. The primers were synthesized at VBC- Biotech (Service GmbH, Brehmstrabe, 14A, A1110, Vienna) as lyophilized pellet at the concentration of 10 nmol. All microsatellite markers were first checked under single locus amplification conditions to evaluate their performance in the multiplex. Based on the guidelines of Henegariu et al. (1997) and Loffert et al. (1999) the initial parameters of multiplex PCR were set up (Henegariu et al., 1997; Loffert et al., 1999). The basic PCR reaction mixture (15 µl) containing 20-50 ng of template DNA; 1.5 mM MgCl2 (Invitrogen); 5 picomoles each of forward and reverse primers (); 1 unit of taq DNA polymerase (Sigma) and 200 mM dNTPs (Kapa Biosystems) was prepared. Amplification was carried out with initial denaturation at 95°C for 2 min followed by 30 cycles of denaturation (95°C for 30 sec), annealing (48°C to 62°C for 30 sec) and extension (72°C for 45 sec) using Applied Biosystems VeritiTM 96- well thermal cycler.
1.4 Genotyping and statistical analysis
Genotyping was carried out on automated DNA Sequencer (ABI HITACHI 3500). The resulting data were analyzed using standard software GENE MAPPER v. 4.0 (Applied Biosystems) to generate genotype calls for each locus by using GS 500 (- 250) LIZ as size standard.Genetic diversity and structure was determined as allele frequencies, effective number of alleles (Ne), test of Hardy-Weingberg equilibrium (HWE), observed (Ho) and expected (He) heterozygosity, F-statistics, Shanon information index (I) and Neighbour-joining consensus tree using POPGENE v. 1.32 (Yeh et al., 1999). Polymorphic information content (PIC) was calculated according to (Nei, 1978).Principal component analysis (PCA) and Analysis of molecular variance (AMOVA) was computed using GENALEX v. 6.5 (Peakall and Smouse, 2012).
2 Results
2.1 Within population diversity
A total of 440 alleles from 23 microsatellite loci were identified in 126 evaluated samples of goats. TNA per locus (Table 2) was 4.848, ranging from 1.500 (OarJMP29 and HH35) to 9.500 (OarFCB304). The observed amplitude of almost all of the markers in allele sizes exceeded the expected range indicating that some new alleles were present in the populations analyzed. However, the effective number of alleles (Table 2) varied from 1.026 (OarJMP29) to 5.302 (OMHC1), with overall mean of 2.678±0.154. The observed heterozygosity (Ho) ranged from 0.026 (OarJMP29) to 0.917 (ILSTS30), with a mean value of 0.484±0.032, which was lower than expected heterozygosity (He) (0.493±0.029) (Table 2).The FIS index (Table 3) was not significant in ALG and NLHG populations. The AHG alone presented significant FIS index value revealing an amount of homozygous individuals beyond the expected, while the BG population was an out-bred one having the FIS value on the negative side. However, Shannon’s information index (I) was sufficiently high with a mean of 1.010±0.063 (Table 2).The PIC value revealed that all the loci studied were polymorphic in nature with a global mean of 0.5565 (Table 2).

Table 2 Estimates of genetic variability indexes per locus based on 126 goats

Table 3 Estimates of genetic variability indexes per population using with 23 microsatellite loci

The within-population analysis (Table 3) showed that the BG and NLHG populations had the highest genetic diversity (MNA, Na, Ne, Ho and He) followed by ALG and AHG. Among the four goat populations, Assam Hill Goat (AHG) had the least level of gene diversity (Ho = 0.434), while BG had highest level of gene diversity (Ho = 0.636). The chi-square (χ2) test for HWE revealed that 6, 10, 7 and 7 loci deviated from equilibrium in ALG, AHG, BG and NLHG populations respectively.
The population differentiation measured by Wright’s F-statistics (FIT, FIS and FST) is shown in Table 2.The divergence between expected and observed heterozygosity measured by the FIT statistic, had a global mean of 0.201 for all markers, and ranged between -0.142 and 0.949.The FIS statistic had a global mean of 0.041. The genetic differentiation among the population measured by the FST statistic had a global mean of 0.157,and ranged between 0.015 and 0.484,indicating that 84.3% of genetic variability was caused by the differences among individuals within populations and 15.7% was due to the differentiation among populations.
2.2 Between population divergence
Nei (1972) standard genetic distance revealed significant genetic distance between the studied populations (Nei, 1972).The genetic distance tended to be the least (0.0732) between ALG and AHG and the widest (0.4943) between AHG and NLHG (Table 4). The genetic differentiation between different pairs of populations was significantly different from zero. Further, an AMOVA analysis was carried out to analyze the variation within and between populations which revealed percentage of variation among and within populations as 21 and 79 respectively.Variance components among population were highly significant for all the studied loci (Table 5), demonstrating significant geographical structuring in studied goat populations.

Table 4 Genetic distance (below diagonal) and paired FST values (above diagonal) between the populations

Table 5 AMOVA analysis of studied goat populations based on microsatellite DNA variation

To understand the relationship among the four goat populations,
Neighbour-joining (NJ) consensus tree were constructed based on Nei’sgenetic distances (Figure 1). The clustering pattern of the goat populations showed their geographical origin. To supplement, principal component analysis (PCA) was performed using the FST values. The first two principal components explained94.54%of the total variation. The global principal component analysis for the first two principal components is presented in Figure 2.The first axis contributed about86.16%of the inertia and distinguished the all studiedpopulations from each other. The second axis contributed 8.38% of the inertia, as a result, these two axes revealed a pattern of association.

Figure 1 Neighbour-joining consensus tree among four goat populations

Figure 2 Multidimensional scaling plot constructed on the basis of pair-wise FST values of goat populations

3 Discussion
In the present investigation 23 markers were used of which 16 markers had more than four numbers of alleles indicating that the markers used are appropriate to analyse diversity in the North East Indian goat genetic groups.
Allelic and gene diversities are considered as responsible indicators of genetic variation within the populations. All the investigated populations in the present study showed low genetic variability based on their estimates of effective number of alleles and observed heterozygosity. The global mean number of alleles observed (4.848) in the present study was lower than the mean number (6.29) reported for Ganjam goat (Sharma et al., 2009) and Gohilwari (10.12) goat (Kumar et al., 2009).Moreover, the mean number of observed alleles in single population microsatellite based analysis of Assam Local goats (5.04) (Zaman et al., 2014c), Assam Hill goats (4.95) (Zaman et al., 2013c), Nagaland Long Hair goat (5.0) (Zaman et al., 2013b) and Bengal goat (5.5) (Zaman et al., 2013a) were similar with the present findings.However, the global mean of effective alleles (2.678) was lower than the observed number of alleles which might be due to very low frequency of most of the alleles at each locus and few alleles might have contributed to the major part of the allelic frequency.The global mean of observed and expected heterozygosities were 0.484 and 0.493 respectively.Though few loci exhibited lower heterozygosity values, most of the loci showed relatively higher expected heterozygosity, which reflects the existence of differentiation in the studied populations.However, the heterozygosities observed in the present investigation are not in accordance with the findings of Rout et al. in Indian domestic goat populations (Rout et al., 2008).
Genetic markers showing PIC values higher than 0.5 are normally considered as informative in population genetic analyses (Botstein et al. 1980). Consequently, with the exception of ILSTS008, ILSTS044, ILSTS34, ETH225, OarJMP29, OarVH72 and HH35, all the loci in the present investigation possessed high PIC values (above 0.50) signifying once again that these markers are highly informative for characterization of native goat genetic groups of North East India including West Bengal. The global mean PIC value (Table 2) in goat populations (0.5565) is in close agreement with the reports of Sharma et al. in Ganjam goat and Kumar et al. in Gohilwari goat (Kumar et al., 2009; Sharma et al., 2009).
The deviation of least number of the loci from HWE (Table 3) and lower global mean FIS (0.041) observed in the present study might be attributed to large random mating populations. Almost similar observation of heterozygosity levels has also been reported in domestic goat populations of India (Rout et al., 2008). The fixation coefficient of populations (FST) had a global mean of 0.157, showing that 15.7% of the genetic variation was explained by differences between populations. The mean FIT value for all loci was 0.201, revealing 20.1% difference between observed and expected heterozygosity.In addition, AMOVA indicated the genetic variation between populations to be 21 %, conforming higher within population diversity in the investigated genetic groups. However, the measures of population differentiation indicated significant differences between groups.
In NJ phylogenetic tree NLHG and BG were grouped as separate clusters while ALG and AHG were not clearly divided into different clusters. Further, the principal component analysis (PCA) supported the grouping of animals and the distance between genetic groups was significant. The phylogenetic and PCA analysis indicated that populations were grouped according to their geographic locations. A similar observation of population clustering according to their geographic origin has been reported in Indian domestic goats Rout et al. (2008).
Out of the 4 goat populations the Assam Local and Assam Hill goats appear to be closely related as they clustered together in the dendrogram which can also be suggested from the Multidimensional scaling plot constructed on the basis of pair-wise FST values where NLGH and BG were separated out of the cluster. However, the Bengal goat population is an out-bred one, having the FIS value on the negative side. The PIC values observed in the present study is indicative of the fact that the markers used are highly informative for characterization of indigenous goat germplasms of North East India. With the actual genetic diversity and the population structure of these four goat genetic groups evaluated, it was possible to clarify the importance as well as to propose some management strategies for these genetic resources. This fact, coupled with evident environmental adaptation, emphasizes the importance of genetic regulation and conservation of these indigenously evolved germplasms for sustainable utilization. The results show that levels of genetic diversity in natural populations of specific genetic group are moderate to high; this might be due to maintaining of effective populations. Estimates of differentiation and genetic structure confirm the history of individual populations and suggest considerable uniqueness in these genetic groups.
Authors wishes to extend gratitude to the Indian Council of Agricultural Research, New Delhi, India for the financial assistance for molecular characterization work through Network Project on Animal Genetic Resources under National Bureau of Animal Genetic Resources, Karnal, India.
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