Genetic Diversity Analysis of the Gohilwari Breed of Indian Goat (Capra hircus) Using Microsatellite Markers

Problem statement: Gohilwari breed of goat is a multipurpose goat main ly for milk and meat purposes and best suited in its harsh climatic condition. This breed is inadequately characterize d till now at DNA level. So the present study was und ertaken for population genetic analysis at molecular level to exploit the breed for planning s ustainable improvement, conservation and utilization, which subsequently can improve the liv elihood of its stake holders. Approach: The experiment was conducted on 50 genomic DNA samples of unrelated goat using 25 microsatellite markers selected from the list suggested by Interna io l Society for Animal Genetics (ISAG) and FAO’s (DAD-IS). Results: All of the 25 microsatellites were well amplified. The observed number of alleles detected per locus ranged from 4-24 with an overall mean of 10.12±5.46. Overall mean observed heterozygosity of 0.505 was lower than the overall mean expected heterozygosity of 0.684. Most of the loci showed the heterozygote deficit as also depicted by F is value. There was substantial genetic variation and polymorphism across studied l oci in the Gohilwari breed of goat. And this population was not in Hardy-Weinberg equilibrium at most of the studied loci. This population was also receiving new genetic materials through introd uction of immigrants. Conclusion: The strong inference that the Gohilwari breed of goat has not undergone bottleneck is also important for goat breeders and conservationists, as it suggests that any unique alleles present in this breed may not ha ve been lost. Therefore, it can be recommended that wi thin-breed diversity is actively maintained to enable these extensively unmanaged stocks to adapt to fu ure demands and conditions and there is ample scope for further improvement in its producti vity through appropriate breeding strategies. Though, microsatellites are neutral to selection wi th Ewens-Watterson test for neutrality some microsatellites were found not neutral or linked to some selective trait that must be further investig ated for association to selective traits.


INTRODUCTION
Gohilwari breed of goat is a multipurpose goat mainly reared by the Maldharis (Bharwar and Rabbari communities) for milk and meat purposes. The breed derived its name from the Gohilwad, which was a part of the Kathiawar region and was also the old name of Bhavnagar district of Gujarat state of India. The animals of this goat breed are mainly found in Junagarh, Amrelli and Bhavnagar districts and also to other adjacent districts of Gujarat. The goats are best fit under the harsh climate conditions of this region. In spite of their ecological and economic importance, the Gohilwari goats are inadequately characterized particularly at DNA level. Microsatellites in particular are useful in conservation genetics because the high degree of polymorphism makes them extremely informative and gives them very high discriminating power [12] , allowing for a thorough assessment of genetic variation and structure within and among populations [6] . Genetic diversity is essential for the long-term survival of the species and populations because it provides the raw material for adoption and evolution, especially when environmental conditions have changed [10,29] . A central objective of genetic resources conservation, therefore, is to maintain genetic integrity and natural levels of genetic diversity and to enhance genetic diversity in populations and species where it has been eroded [29] . Therefore, to find out within breed genetic diversity a set of twenty five selected microsatellite s have been used. This study has been undertaken to search for the genetic variability, which could be exploited for planning sustainable improvement, conservation and utilization of the breed, which subsequently can improve the livelihood of its stake holders.

MATERIALS AND METHODS
Isolation of genomic DNA and its amplification through PCR: Genomic DNA was isolated from blood samples of 48 unrelated animals of the breed by the method described by Sambrook et al. [33] . A battery of 25 microsatellite markers (Table 1) was selected based on the guideline of ISAG and FAO's DADIS programme to generate data. Only forward primers at 5' end of each pair were labeled with one of the four fluorophore i.e., FAM (Blue), VIC (Green), NED (Yellow) and PET (red). Most of the microsatellite primers used was independent and belonged to different chromosome except (ILSTS30 and ILSTS082 on Chromosome 2, RM088 and Oar HH64 on chromosome 4, ILSTS008 and ETH225 on chromosome 14, OarFCB48 and ILSTS058 on chromosome 17). Polymerase Chain Reaction (PCR) was carried out on about 50-100 ng genomic DNA in a 25 µL reaction volume. The reaction mixture consisted of 200 µM of each dNTP, 50 nM KCL, 10 mM Tris-HCL (pH 9.0), 0.1% Triton X-100, 2.0 mM MgCl 2, 0.75 unit Taq DNA polymerase and 4 ng µL −1 of each primer using PTC-200 PCR machine (MJ Research). The 'touchdown' PCR protocol used with initial denaturation of 95°C for 3 min, 3 cycles of 95°C for 45 sec and 60°C for 1 min, 3 cycles of 95°C for 45 sec and 57°C for 1 min, 3 cycles of 95°C for 45 sec and 54°C for 1 min and 20 cycles of 95°C for 45 sec and 51°C for 1 min with final extension at 72°C for 5 min. PCR products were loaded on to a 2% agarose gel, electrophoresed and visualized over UV light after ethidium bromide staining to detect the amplification.
Genotyping and allele detection: After determining the optimal pooling ratio and dilution ratio for a set of primers, the PCR products were mixed in ratio of 1:1.5:2:2 of FAM (blue), VIC (green), NED (yellow) and PET (red) labeled respectively. 0.5 µL of this mixture was combined with 0.3 µL of Liz 500 as internal lane standard (Applied Biosystems) and 9.20 µL of Hi-Di Formamide per sample. The resulting mixture was denatured by incubation for 5 min at 95°C. These denatured samples were run on automated DNA sequencer of Applied Biosystems (ABI 3100 Avant). The electropherograms drawn through Gene Scan were used to extract DNA fragment sizing details using Gene Mapper software (version 3.0) (Applied Biosystems).
Statistical analysis: Genetic diversity within population was determined as the observed and expected number of alleles [17] and Shanon's Information Index [22] using Popgene software [39] . Observed and expected heterozygosity were calculated as per Levene [21] as implemented in Arlequin software (version 3.11) [11] . A Monte Carlo method [14] , with forecasted chain length 1000000 was used to compute unbiased estimate of the exact probability (p-value) also implemented in the Arlequin. Wright's F-statistics [37] were estimated in accordance with the procedures described by Weir and Cokerhan [35] using the F-stat 2.9.3 [13] . A more appropriate measure of genetic variation within a population is gene diversity (average expected heterozygosity) [27] at each locus was calculated by the same software. Polymorphic Information Content (PIC) value was calculated according to Botstein et al. [5] implemented in Cerevus 3.0.3 software package [17] . Hardy-Weinberg Equilibrium (HWE) at each locus was tested by Chi Squire (χ 2 ) goodness-of-fit test with Yat's Correction and significant test was done with Bonferroni corrections [30] to reduce the type I error, implemented in Cervus 3.0.3 software package [40] . Ewens-Watterson test was performed to test the neutrality for microsatellite markers; the statistics F (sum of square of allelic frequency) and limit (upper and lower) at 95% confidence region for the test were calculated using the algorithm by Manly [25] using 1000 simulated samples and implemented in Popgene software package [39] . Bottleneck events were tested by three methods. The first method consisted of three excess heterozygosity tests developed by Cornuet and Luikart [9] ; (i) sign test (ii) standardized difference test and (iii) wilcoxon sign-rank test. The probability distribution was established using 1000 simulations under three models; Infinite Allele Model (IAM), Step wise Mutation Model (SMM) and Two Phase Model of mutation (TPM).
The second method was the graphical representation of mode-shift indicator originally proposed by Luikart et al. [23] . Loss of rare alleles in bottlenecked populations is detected when one allele class have a higher number of alleles than the rare allele class [23] . This test was rescaled so that frequency distribution of the allele frequency class would be based on equal 0.05 increments. These two methods were conducted using Bottleneck (version 1.2.03) [9] .

RESULTS
Various measures of genetic variation in terms of allele number, information index, PIC value and gene diversity are presented in Table 2. The observed number of alleles detected per locus ranged between 4 (ILST008, ETH225, OarJMP29 and RM088) to 24 (OarFCB304) with an overall mean of 10.12±5.46.
Shannon's Information Index [22] , which measures the level of diversity, was sufficiently high with an overall mean of 1.603. Most of the studied loci showed the Polymorphic Information Content (PIC) values greater than 0.5 except a very few loci with an overall mean 0.647.
The average expected heterozygosity was with an over all mean of 0.686 (Table 2). In Gohilwari goat breed, the mean effective number of alleles (4.78) was less than the half of the observed number of alleles (9.04) ( Table 2).  [17] ; I: Shannon's Information index [22] ; PIC: Polymorphic Information Content Observed heterozygosity was lowest (0.074) at ETH225 locus and highest (0.979) at ILSTS082 locus with overall mean of 0.505 (Table 3). Expected heterozygosity ranged from 0.0869 (OarJMP29) to 0.935 (ILSTS058) with an over all mean of 0.684. The observed heterozygosity was lower than that of the expected heterozygosity at most of the loci except OarJMP29, ILSTS029, OarAE129 and ILSTS058.
This breed of Goat also deviated from HWE at 15 loci out of 25.

Ewens-Watterson test for neutrality of microsatellite markers:
As the microsatellite markers have the specific property, as they are neutral to selection even the neutrality of each microsatellite marker was tested by Ewens-Watterson test for neutrality. In Gohilwari goat, F value (sum of square of allelic frequency) lied outside the lower and upper limit of 95% confidence region of expected F value at 6 loci (ILSTS044, ILSTS002, OarHH64, OarJMP29, OMHC1 and ILSTS030) ( Table 4).

DISCUSSION
All measures of genetic variation: observed number of alleles, effective number of alleles, Shannon's Information Index and PIC values showed that most of the studied loci were highly informative, indicating high polymorphism across the loci, thus suggesting suitability of these markers for genetic diversity studies in goats. Suitability of these studied markers was further strengthened as the number of alleles for each marker was higher, than the minimum number of four alleles recommended for microsatellite markers to be used in the estimation of genetic distance [41] in order to reduce the standard error.
The average expected heterozygosity i.e., gene diversity [27] was in the range of 0.3 to 0.8 as determined by Takezaki and Nei [34] for markers to be useful in measuring genetic variation in a population.
Overall mean observed heterozygosity was lower than the overall mean expected heterozygosity. Most of the loci showed the heterozygote deficit as also depicted by F is value (Table 3).
Mean number of alleles observed over a range of loci in different populations is considered to be a reasonable indicator of genetic variation within the populations [31] . This breed of goat showed the drastic low number of the effective number of alleles (even lower than half) than the observed number of alleles. This is due to very low frequency of most of the alleles at each locus and a very few alleles might have contributed the major part of the allelic frequency at each locus. Even these revealed the high level of allelic diversity; a more appropriate measure of genetic variation within a population is gene diversity (average expected heterozygosity) [27] . Overall mean of 0.686 (Table 2) of gene diversity was higher to the value reported in Swiss goat breeds (0.51 to 0.58) for 20 microsatellite loci [32] and 11 indigenous south east Asian goats (0.43-0.60) [3] but is slightly lower than those reported in Chinese goat breeds (0.777-0.823) for 6 microsatellite loci [38] .
Another measure of genetic variation is observed heterozygosity. This population had higher mean observed heterozygosity than what was observed in Jakhrana and Marwari [19] , Attapady [1] and many other Asian goats [3] but lower in Chegu breed of goat {4] . Higher genetic variation in this studied breed may be due to its large effective population size, immigration of new gene due to intermixing of different population and low selection pressure. Breeding policies and different crossbreeding programmes might have contributed to higher genetic variation in Gohilwari goat population.
Majority of loci in this breed exhibited deficiency of heterozygosity at majority of loci. Overall mean F is value of 0.264 was significantly different from zero. Significant heterozygote deficiency has been also reported in other studies of goat [3,42] . Heterozygote deficiency in this breed of goat could be due to one or more of the following reasons: segregation of nonamplifying (null) allele, Wahlund effect or inbreeding. However distinguishing among these was generally difficult [7] . Null alleles arise more in case of heterologous primer (Microsatellite of different species) leads to underestimation of heterozygosity but Callen et al. [8] identified null alleles using homologous microsatellite primers. This may be due to Wahlund effect or the fact that few bucks were used for the whole and nearby villages in the breeding region for breeding.
Deviation from HWE had also been reported in many other studies. Kim et al. [43] reported HWE deviations in Korean, Chinese and Saanen goats. The main reasons for the deviation from HWE are most likely the genetic drift; non-random mating, nonamplifying alleles or the population might be divided into a series of closely related or inbred family groups.
In Ewens-Watterson test for neutrality for markers the observed loci, which lied outside the limit of 95% confidence region, were not neutral and may be linked with some selection traits. If a neutral allele statistically associated with a selected allele at another locus or genes where selection is operating significantly may be carried along and alleles cannot be separated from their genetic background. This phenomenon is known as hitchhiking. Genetic hitchhiking can be potent force in changing allelic frequency and heterozygosity.
Maynard-Smith and Haigh [26] first suggested that molecular polymorphism may be modified by hitchhiking of neutral alleles adjacent to loci undergoing allelic substitution. Potentially one of the most important effects of hitchhiking is the reduction of heterozygosity of such molecular variation in area of low recombination due to selective sweeps at some of these loci substantially low level of heterozygosity has been observed (Table 3). In another specific study, Haiguo et al. [15] found that the some alleles of Microsatellite markers (ETH10 and IDVGA46) was linked to beef performance of cattle and showed positive or negative correlation with the different beef performance of cattle. Microsatellite ETH10 was also found linked to milk production performance in cattle [18] . In this study, microsatellite that were found not neutral or linked to some selective trait must be further investigated for association to selective traits. This may help in MAS (marker assisted selection) in breeding programmes if the association to selective traits is established.
Genetic bottleneck: Genetic bottleneck occurs when population experiences some temporary reduction in size. This may influence distribution of genetic variation within and among populations. Loss of genetic diversity may reduce the potential of small populations to respond to selective pressure [2] and increased inbreeding may reduce population viability [20,28,36] .
The three tests (sign test, standard difference test and wilcoxon rank test) under these three model (IAM, TPM and SMM) for heterozygosity excess can detect the bottleneck for only a short duration of time after a bottleneck has been initiated. These are the quantitative test [9] that can detect bottleneck up to 50-250 generations. As discussed above, the null hypothesis of mutation drift equilibrium was accepted overall, there was no serious recent genetic bottleneck in Gohilwari goat breed.
In case of existence of bottleneck event the rare alleles are lost more often than the commonly occurring alleles and consequently there is a reduction in population size. Allele loss does not occur at the extreme of allele size distribution so the range in allele size remains constant. The non-bottleneck populations that are near mutation drift equilibrium are expected to have a large proportion of alleles in the range of low frequency and proportion of alleles decreasing or even nil at higher frequency class so normal L shaped curve. It can detect the recent bottleneck up to 40-80 generations only.

CONCLUSION
In conclusion, there was substantial genetic variation and polymorphism across studied loci in the Gohilwari breed of goat. And this population was not in Hardy-Weinberg equilibrium at most of the studied loci. This population was also receiving new genetic materials through introduction of immigrants. The strong inference that the Gohilwari breed of goat has not undergone bottleneck is also important for goat breeders and conservationists, as it suggests that any unique alleles present in this breed may not have been lost. Therefore, it can be recommended that withinbreed diversity is actively maintained to enable these extensively unmanaged stocks to adapt to future demands and conditions and there is ample scope for further improvement in its productivity through appropriate breeding strategies.

ACKNOWLEDGEMENT
We are most graceful to Hon'ble Vice-Chancellor, BAU, Kanke, Jharkhand (India) Dr. N. N. Singh for his constant promotional, progressive and financial support for the research work. We are extremely thankful to Dr. S. P. S. Ahlawat, Direcor, IVRI, Izzatnagar, for permitting to carry the research work at NBAGR, Karnal during his directorate ship at NBAGR, Karnal, Haryana (India). We also thank all those that have contributed to the field and lab work.