7 software. Estimates of genetic diversity (mean number of alleles, rare, effective and private alleles and expected heterozygosity) were calculated using GenAlEx 6.5 (Peakall and Smouse, 2012). Deviations from the Hardy–Weinberg equilibrium and linkage disequilibrium were tested using 10,000 permutations with the Genepop 4.0 programme (Rousset, 2008). Inbreeding coefficient FIS was calculated and tested (10,000 permutations) with the SpaGeDi 1.3 programme ( Hardy and Vekemans, 2002). Temporal changes in allele frequencies were tested using MI-773 in vitro the simulation test
(ST) and FT test ( Sandoval-Castellanos, 2010), and the Waples test (WT; Waples, 1989) using the TAFT 2.3 programme ( Sandoval-Castellanos, 2010). ST is a statistical test based on the Bayesian theorem in which the distribution of the distances among sampling frequencies is simulated. Binominal sampling is used for generation changes and hypergeometric sampling for effective populations and samples. The simulation procedure has been described in detail by Sandoval-Castellanos (2010). The FT statistic corresponds to the fixation AZD8055 nmr index (FST) minus the average FST calculated among simulated samples and can be interpreted as the divergence which the population has undergone through time if the effect of gene drift is excluded. WT is a Chi-Square test adjusted to consider gene drift. The null hypothesis tested with all three tests
was ‘changes in observed allele frequencies between two samples
taken from the same population at different times are the result of genetic drift and sampling error’. The following parameters were used for the above tests: full Bayesian algorithm, Plan I sampling strategy and one generation separated the two temporal samples. Population size was set at 10,000 and effective population size at 6000. The number of simulations was 106. For comparison, pairwise FST values according to Weir and Cockerham (1984) were calculated and significance was determined using 10,000 permutations with the SpaGeDi programme. Additionally, standard genetic distance (DS) according to Nei (1978) was calculated in SpaGeDi. Potential differences in the genetic structure between the cohorts were also investigated using a model-based clustering algorithm implemented Bay 11-7085 in the Structure 2.3.4 programme (Pritchard et al., 2000, Falush et al., 2003 and Hubisz et al., 2009). The best estimated number of distinct clusters was calculated according to Evanno et al. (2005) using Structure Harvester (Earl and von Holdt, 2012), whereas the ‘Greedy algorithm’ implemented in CLUMPP 1.1.2 (Jakobsson and Rosenberg, 2007) was used to average the results of the replicated runs. The default model parameters using populations’ priors were used for simulations, allowing number of populations K to vary from 1 to 6. Each run, replicated 10 times, consisted of 150,000 burn-in iterations and 350,000 data collection iterations.