Computational protocol: Genetic Variation and Population Structure in Jamunapari Goats Using Microsatellites, Mitochondrial DNA, and Milk Protein Genes

Similar protocols

Protocol publication

[…] Fifty blood samples were collected in 10 villages in which the breed has a major concentration. Samples were collected from the individuals exhibiting typical breed characteristics such as white colour, Roman nose, and pendulous ear (farmers are not selecting for these traits) and at least two samples were collected from each village. An effort was made to collect samples from unrelated individuals based on information provided by farmers. The breeding buck is available with one or two farmers in every village, and some farmers also maintain breeding bucks during breeding season, disposing of them after the breeding season. Blood samples were collected from each animal using EDTA vacutainer and stored at –20°C till further use.Microsatellite analysis was carried out to test for signatures of recent population bottlenecks in Jamunapari goats. This analysis was carried out on 49 DNA samples with 17 microsatellite markers () as reported by Rout et al. []. For these 17 loci, genetic variation was quantified using measures of the total number of alleles, number of polymorphic loci, observed and expected heterozygosity per locus, and allelic richness using GENEPOP (Version 3.4; []), FSTAT2.93 [], and AGArst []. Heterozygosity was measured as the mean observed heterozygosity (Ho) and the mean expected heterozygosity (HE) based on Hardy-Weinberg assumptions. We tested genotypic linkage disequilibrium between all pairs of loci in each population with GENEPOP (Version 3.4; []) based on Markov chain method with 10,000 iterations and 100 batches. We also used FSTAT software to assess 95% confidence intervals of Weir and Cockerham's f, which measures deviation from the Hardy-Weinberg equilibrium (HWE) for populations and corresponds to Wright's within-population inbreeding coefficient FIS.Milk protein genes, which are expected to be nonneutral markers, were also used to analyse the population variability. Two milk protein genes, namely, β-LG gene and CSN1S1 (αs1-casein) were analysed using PCR-RFLP to observe genetic variability in 35 individuals. The αs1-casein (CSN1S1) gene produced an amplified fragment of 223 bp which was digested with the XmnI restriction enzyme. The β-LG gene produced an amplified product of 426 bp, and RFLP analysis was carried out with the SacII restriction enzyme. The PCR-RFLP analysis was carried out as described by Kumar et al. [, ], and the data were analysed separately for mean number of alleles, expected heterozygosity and Hardy-Weinberg equilibrium (HWE) using POPGENE software [].mtDNA HVRI sequencing was carried out as described by Joshi et al. []. Four hundred and fifty-seven base pairs from the mtDNA HVRI regions of 50 individuals were aligned using CLUSTAL X. We used mismatch distribution [] to analyse the population expansion as implemented in ARLEQUIN 3.1 []. Fu's F value was calculated from mtDNA haplotypes to test for deviations from neutral equilibrium condition []. The qualitative and quantitative aspect of the population's genetic history may be uncovered by the analysis of frequency distributions of pairwise sequence mismatches. Mismatch analysis (the distribution of all pair-wise nucleotide differences between sequences) was carried out to test the deviation of the observed data from neutral predictions expected in constant-sized populations.Genetic divergence was analysed by selecting three primers from ovine male-specific region (AMLEY, SRY, and ZFY gene) []. PCR was carried out in a 50 μL reaction volume containing 100 ng of DNA, 20 pM of each primer, 200 μM of dNTP, 2 mM Mgcl2, and %U of Taq DNA polymerase (New India Biolab, MA, USA). The samples were subjected to sequencing after purifying the PCR product by gene elute PCR clean up kit. Individual PCR amplified products were subjected to sequencing in 12 samples. PCR products were sequenced on both the strands directly using 50 ng (2.0 μL) of PCR product and 4 pM (1.0 μL) of primer, 4 μL of Big Dye Terminator ready reaction kit (Perkin Elmer, Foster City, USA), and 3.0 μL of double distilled water to adjust the volume to 10.0 μL. Cycle sequencing was carried out in a Gene Amp 9600 thermal cycler (Perkin Elmer) employing the PCR conditions. Extended products were purified by alcohol precipitation followed by washing with 70% alcohol. Purified samples were dissolved in 10 μL of 50% Hi-Di formamide and analysed in an ABI 3700 automated DNA Analyzer (Perkin Elmer, USA). Nucleotide diversity, expected heterozygosity, Tajima's D, and Fu's Fs values were estimated in ARLEQUIN 3.1 []. Genetic bottleneck was detected using microsatellite data by three approaches, heterozygote excess, mode-shift, and M ratio test. We first used the M ratio (the mean ratio of the number of alleles to total range in allele size) [] as implemented in AGArst [], because of its consistent performance in identifying populations with known bottlenecks. M ratio calculates the changes that occur after a bottleneck in the distribution of allele sizes relative to the number of alleles in a population. It has been established that an M ratio less than 0.71 signifies a bottleneck [].The BOTTLENECK programme [] was used as an alternative measure of genetic bottlenecks to test for excess gene diversity relative to that expected under mutation-drift equilibrium. The heterozygosity excess method exploits the fact that allele diversity is reduced faster than heterozygosity during a bottleneck, because rare alleles are lost rapidly and have little effect on heterozygosity, thus producing a transient excess in heterozygosity relative to that expected in a population of constant size with the same number of alleles [, ]. To determine the population “genetic reduction signatures” characteristic of recent reductions in effective population size (Ne), the Wilcoxon's heterozygosity excess test [] and the allele frequency distribution mode shift analysis [] were performed using BOTTLENECK []. The heterozygosity excess method was used to analyse the population, and the data for the heterozygosity excess test were examined under the two-phased model (TPM; 95% stepwise mutation model with 5% multistep mutations and a variance among multiple steps of 12), which is considered best for microsatellite data [, ]. We also analysed the allele frequency distribution for gaps. A qualitative descriptor of allele frequency distribution (the mode-shift indicator), which is reported to discriminate between bottlenecked and stable population [], was obtained using the programme BOTTLENECK.We used an individual-based clustering approach (STRUCTURE 2.1, []) to determine the most likely number of genetic clusters (k) in the Jamunapari populations. STRUCTURE software sorts individual genotypes into clusters that maximize the fit of the data to theoretical expectation. Based on preliminary analyses, we evaluated the likelihood of k = 2 and k = 3, with 5 runs performed for each k, and a burn-in length of 500,000 and 100,000 MCMC replicates for each run. We assumed an admixture model and correlated allele frequencies among populations []. […]

Pipeline specifications

Software tools Genepop, POPGENE, Clustal W, Arlequin
Application Population genetic analysis
Organisms Capra hircus