Evaluation of genetic diversity and population structure of five yellow catfish Pelteobagrus fulvidraco populations by microsatellite markers

Yellow catfish specimens were collected from five lakes in the middle and lower reaches of the Yangtze River, China, including Poyang Lake (PY) in the middle reaches, Caohu Lake (CH), Gehu Lake (GH), Hongze Lake (HZ) and Taihu Lake (TH) in the lower reaches (Fig. 1). Twenty five individuals were randomly sampled from each population. A caudal fin clip from each specimen was taken and stored in 95% ethanol for DNA extraction.

Figure 1

Total genomic DNA was extracted from the tail fin using the Ezup Column Animal Genomic DNA Kit (Sangon, Shanghai) following the manufacturer’s protocol. Quantity and quality of the extracted DNA were estimated on 1% agarose gels stained with ethidium bromide (EB). Thirteen specific microsatellite loci of yellow catfish (Table 1) were amplified by PCR on a Mastercycler gradient apparatus (Eppendorf).

Table 1

Information of the thirteen microsatellite loci analyzed in five populations of yellow catfish

PCR was conducted in a reaction mixture of 20 μl containing 1 μl genomic DNA (50 ng pl^-1), 2μl of 10 × PCR buffer [40 mmol KCl, 8 mmol Tris-HCl (pH 8.8), 120 nmolKCl, 1.2 mmol MgCl₂], 400 nmol each of reverse and forward primers, 0.5 U Taq DNA polymerase. The PCR amplification conditions used herein were as follows: initially denatured at 94°C for 5 min followed by 30 cycles (denaturation at 94°C for 30 sec, annealing at 51–60°C for 30 sec, and extension at 72°C for 1 min), with a final extension for 10 min at 72°C. Electrophoresis was conducted in 1.4% agarose gel to confirm successful DNA amplification.

The PCR products were run on 8% polyacrylamide gels in 0.5 × TBE buffer for 2–3 hours. After electrophoresis, the gels were silver stained and photographs were taken using a Nikon Coolpix 4500 digital camera. The 25 bp DNA Ladder (Invitrogen, USA) was used to determine the allele size. Each sample was screened 2–3 times for each primer in order to reduce allele misscoring.

The expected heterozygosity corrected for sampling bias, the observed heterozygosity (H_O), the expected heterozygosity (H_E), the polymorphic information content (PIC), the inbreeding coefficient (F_IS) and the estimated null allele frequencies were calculated for each locus across each population using POPGEN32 (Rousset & Raymond 1995). The GENEPOP 3.4 software (Raymond & Rousset 1995) was used to perform the exact test for the Hardy-Weinberg equilibrium (HWE) using the Markov Chain method with 1000 iterations and considering heterozygote deficiency as the alternative hypothesis. Exact tests for deviations from HWE were performed using the GENEPOP software and applying the default parameters (dememorization number 1000; batches 100; and repetitions per batch 1000). In addition, to determine whether deviations from HWE were toward heterozygote excess or deficit, HWE tests were carried out for each locus in each population along with a global test for all populations. Corrections for multiple significance tests were performed using Fisher’s method. Analysis of molecular variance (AMOVA) was performed with Arlequin version 3.01 (Excoffier et al. 2005) to test for significant differences in genetic diversity between the populations.

The MEGA 6.0 software package (Tamura et al. 2013) was used to construct an UPGMA tree of relationships between the populations. Bayesian clustering analysis implemented with Structure 2.3.4 (Pritchard et al. 2000) was performed to estimate the most likely number of genetic clusters (K) of populations and assign individuals to those clusters without prior information on the origin of samples. The admixture model was employed, with 20 000 burn-in periods and 1 000 000 Markov-chain Monte Carlo (MCMC) iterations based on the discriminating loci. To identify the most likely posterior probability K value, the simulation program was run with increasing numbers of clusters (K) from two to five, and a plateau was used to indicate the most likely K (Falush et al. 2007). For each successive value of K, the inferred clusters were analyzed and visualized as colored box plots using the Distruct program (Rosenberg 2004).

For the 125 individuals from the five populations, a total of 58 alleles were identified across 13 microsatellite loci ranging from 90 to 450 bp. The average number of alleles per locus was 4.4, ranging from 3 (loci CT42, HLJYC31 and HSY105) to 9 (loci AG128). The observed heterozygosity (H_O) and the expected heterozygosity (H_E) of all microsatellite loci were 0.46 and 0.60, respectively. The polymorphic information content (PIC) ranged from 0.08 to 0.81, with an average of 0.53 per locus (Table 2).

Table 2

Polymorphic information at 13 microsatellite loci of five populations of yellow catfish

Diversity measures for each population showed that the mean number of alleles per locus was very similar in each population. The GH and HZ populations were the most diverse populations having the highest allelic richness, 3.13 and 3.08 respectively, while the PY population – the lowest one (2.61). The observed heterozygosity (H_O) and the expected heterozygosity (H_E) for each population ranged from 0.42 (PY) to 0.49 (CH) and 0.51 (PY) to 0.61 (CH and GH). For all populations, the observed heterozygosity values were marginally lower than the expected values. The polymorphic information content (PIC) ranged from 0.46 to 0.55. Most populations were found to be highly informative (PIC > 0.50), except the PY population. Data on each locus for each population are given in Table 2.

Of the 65 population-locus cases, 42 (64.6%) were in HWE (p > 0.05), while another 23 (35.4%) showed significant deviation (p < 0.05). Significant deviations from HWE at the locus level were found at HLJYC17 in five populations. At the locus AG12 and HSY105, departure for HWE was observed in four populations. Of the 13 markers assessed for HWE by the multi-population test, significant and highly significant departures were observed in two loci. However, global HWE tests revealed no deviations (p > 0.05) in all populations (Table 3). This was also reflected by the positive inbreeding coefficient (F_IS) value. The F_IS value in each population was moderate and ranged from 0.182 (PY) to 0.257 (GH).

Table 3

p-value of Hardy-Weinberg test on five populations of yellow catfish

The pairwise F_ST presented in Table 4 showed a significant difference between PY and the other four populations, indicating a moderate genetic differentiation (0.05 < F_ST < 0.15), whereas there was a low genetic difference between the other four populations. The genetic distances (D_A), displaying large variation between pairs of the populations, revealed close relationships between the HZ and TH populations (0.0541) and the GH and HZ populations (0.0551). The D_A value for the PY-HZ pair (0.1409) was comparatively greater, which further supported the highest degree of divergence between these populations (Table 4). AMOVA showed significantly low to moderate population differentiation, with 4.67% of the molecular variance among the populations from five lakes (p < 0.001), while most variation was found within the populations (95.33%). There were significant differences between the middle and low groups, accounting for 0.44% of the total variation (Table 5).

Table 4

Pairwise F_ST values (above diagonal) and genetic distances (below diagonal) between populations

Table 5

AMOVA analysis of five populations of yellow catfish

A UPGMA dendrogram of the five populations based on genetic distance estimates is shown in Fig. 2. The PY population from the lake located in the middle reaches of the Yangtze River separated as an independent branch and the other four populations from the lakes in the lower reaches of the Yangtze River clustered together as a separate branch. Bayesian clustering suggested the presence of two groups (K = 2) as the most likely, although most individuals showed mixed ancestry. There was a relatively little admixture exhibited by the PY population. The Bayesian clusters were similar to those revealed by UPGMA typology (Fig. 3).

Figure 2

Figure 3