Citation of this paper

Population structure and genetic distances between six Albanian local goat breeds using 26 SNP markers

Gentian Hykaj, Anila Hoda, Petrit Dobi, Lumturi Papa, Rigers Bakiu and Mariol Novaku*

Agricultural University of Tirana, Albania
* Agricultural and Rural Development Agency (ARDA), Ministry of Agriculture, Food and Consumer Protection, Albania
ahoda@ubt.edu.al

Abstract

Goats are an important species for the Albanian livestock farmers. The present study intends to estimate genetic diversity and population structure of 6 local goat breeds using 26 SNPs. Therefore 185 unrelated individuals belonging to 6 local sheep breeds were analyzed. Genetic diversity measures and genetic distance were estimated. Population’s structure analysis, and the assignment of individuals to their reference population was carried out.

Breed assignment methods displayed an overall low sensitivity of 32% (60 out 185 individuals). Average probability of assignment was higher than 50%. Average specificity index for all methods was 32%. The mean F_STvalue was 0.018, demonstrate that almost, all of the genetic variation is due to differences between individuals. Factorial component analysis and model-based clustering displayed a high level of breed admixture. All results obtained here and from previous studies reflect the management differences of Albanian goat breeds.

Key words: breed assignment, genetic structure, genetic variability

Introduction

Goats are an important livestock species, for Albanian farmers, especially in the mountainous areas, where there are several local goat breeds. Goats provide an important source of milk, and meat, mainly for family consumption. There is no breeding program for the goat breeds and in consequence there is a high risk for those local breeds to be loss. The lack of herd book has facilitated the gene flow and the admixture of the breeds resulting to a low level of genetic differentiation (Hoda et al 2012). Albanian goat breeds have been studied previously based on the visible genetic profile (Bozgo et al 2012) and using several molecular markers like microsatellites (Hoda et al 2011a), or AFLP markers (Hoda et al 2012).

Molecular markers are widely used for evaluation of genetic diversity in animal genetic resources. For a long time microsatellites which are multiallelic codominant markers, have been widely used and were considered as the marker of choice for this kind of studies. AFLPs (amplified fragment length polymorphism) are other set of markers which are biallelic dominant. SNP (single nucleotide polymorphism), are a new type of molecular markers that recently have gained a great popularity. SNPs are just a single base change in DNA sequence. These kinds of markers are biallelic codominant (Yang et al 2013).

Negrini et al (2008a) have investigated the effectiveness of single nucleotide polymorphisms (SNPs) for the assignment of cattle to their source breeds. SNP have been used to estimate genetic diversity in cattle (Neto and Barendse 2010), sheep (Pariset et al 2006a, Hoda et al 2011b), and goat (Cappuccio et al 2006, Pariset et al 2006b, Pariset et al 2009a). Kijas et al (2012) have used SNPs to characterize the genetic consequence of domestication and selection in 74 sheep breeds. Negrini et al (2008b) used SNPs in combination with Bayesian statistics for the geographic traceability of 24 cattle breeds.

This study was carried out in the frame of ECONOGENE project, using 26 SNPs as described previously (Cappuccio et al 2006, Pariset et al 2006b). The same Albanian goat breeds have also been analyzed together with other breeds from Italy and Greece (Pariset et al 2009a, b). The aim of the study is estimation of genetic diversity and population structure of six local goat breeds using 26 SNPs.

Material and methods

A total of 6 Albanian goat breeds were analyzed. Thirty to thirty-one unrelated individuals were randomly selected in each of goat breeds. Blood samples were collected from 185 individuals and were genotyped for 26 SNPs. Genotyping of this SNP set is described by Cappuccio et al (2006), Pariset et al (2006b).

Allelic frequencies, observed and expected heterozygosity for each locus, and PIC values were calculated using Powermarker software (Liu and Muse 2004). F-statistics according to Weir and Cockerham were calculated using FSTAT (Goudet 2001). Genepop software (Rousset 2008) was used to test deviations from Hardy – Weinberg equilibrium using a Markovchain of 100,000 steps and 1,000 dememorization steps.

Factorial component analysis (FCA) was performed using Genetix program (Belkhir et al 2001). The analysis of population structure by a clustering analysis based in Bayesian model was carried out by the program STRUCTURE (Pritchard et al 2000). The samples were clustered with number of genetic clusters, K ranging from 1 to 7, applying 20 independent runs for each of the different values of K, with “burning period” of 50,000 iterations and “period of data collection” of 100,000 iterations. Evanno’s method (Evanno et al 2005) was used to identify the appropriate number of clusters using the ad hoc statistic Δk, which is based on the second order rate of change of the likelihood function with respect to successive values of K.

Assignment of individuals to their reference population was evaluated using GeneClass 2 (Piry et al 2004). The assignment of individuals was carried out using likelihood-based methods, according to the criteria of Paetkau et al. (1995) and Bayesian approach (Rannala and Mountain 1997, Baudouin and Lebrun 2000). For each algorithm, sensitivity, specificity and overall average assignment probability were calculated as explained by Negrini et al (2008a).

Result

Genetic variability

Expected heterozygosity for each locus ranged from 0.0059 (FABP4) to 0.526 (CALSNP385R) with an average value for all loci of 0.316, while the values of observed heterozygosity (Ho) ranged from 0.0059 (FABP4) to 0.517 (mel-g-1), with an average value of 0.282.

The frequencies of major alleles ranged from 0.524 (mel-g_1) to 0.997 (FABP4). FABP4 showed a tipical rare allele frequency of 0.003. Three other loci CTSK-G-2, IL2 and CALPA showed frequencies of rare alleles of 0.035, 0.023 and 0.011, respectively. All the other loci have frequencies of rare alleles which are higher than 5%.Significant deviation from HWE overall populations were observed in 5 loci (Table 1).

The within-breed deficit in heterozygosity, as evaluated by the F_IS parameter, ranged between -0.989 (ACVR2) to 0.499 (DQA) having a total mean of 0.034 for all loci. F_IT values ranged from -0.9894 (ACVR2) to 0.512 (DQA). The global heterozygosity deficit (F_IT) was estimated 0.052 and global breed differentiation evaluated by F_ST, was estimated 0.018.

Table 1: Major allele frequency (MAF), observed heterozygosity (Ho), expected heterozygosity (He), PIC values, F _ST values, deviation from HWE (p-value) for each SNP.
Marker	MAF	H_E	H_O	PIC	F_IT	F_ST	F_IS	Exact p-value

ACVR2	0.7787	0.3446	0.3391	0.2852	-0.989	0	-0.989	0.8228
CALPA	0.9888	0.0222	0.0225	0.0220	-0.012	-0.011	0	1.0000
CALSNP385R	0.5810	0.5257	0.4469	0.4327	0.158	0.032	0.128	0.0035*
CSN1S1/5	0.6706	0.4418	0.4353	0.3442	0.027	0.023	0.003	0.8599
CSN3/Ex4	0.7944	0.3266	0.2667	0.2733	0.183	0.023	0.165	0.0191
CTSK-G-2	0.9649	0.0677	0.0585	0.0654	0.177	0.014	0.163	0.1788
DESMIN	0.5848	0.4856	0.4561	0.3677	0.073	0.046	0.028	0.3245
DQA	0.9024	0.1762	0.1243	0.1607	0.289	0.022	0.275	0.0015*
DQA	0.7550	0.3700	0.1854	0.3015	0.512	0.019	0.499	0.0000*
DRB-G-3	0.8876	0.1995	0.1163	0.1796	0.451	0.081	0.397	0.0001*
FABP4	0.9971	0.0059	0.0059	0.0058	0	-0.002	0.002	1.0000
FN1- G-3	0.8116	0.3058	0.2464	0.2591	0.204	0.041	0.17	0.0092
GDFSNP452R	0.8045	0.3146	0.2235	0.2651	0.292	-0.012	0.301	0.0006*
GHR-G-1a	0.5606	0.4927	0.4182	0.3713	0.152	-0.009	0.16	0.0419
IL2/5p	0.9771	0.0447	0.0457	0.0437	-0.023	-0.004	-0.019	1.0000
IL2/In2	0.8864	0.2014	0.2273	0.1812	-0.128	0.015	-0.145	0.1402
IL4SNP119R	0.6389	0.4614	0.5111	0.3550	-0.107	0.002	-0.109	0.1399
ITGB1-G-2	0.6067	0.4772	0.4207	0.3634	0.123	0.021	0.105	0.0934
Lact-G-1	0.7267	0.3972	0.3733	0.3183	0.075	0.072	0.002	0.4236
LIPE-G-1	0.7190	0.4041	0.3660	0.3225	0.11	0.041	0.07	0.1519
mel-G-1	0.5238	0.4989	0.5170	0.3744	-0.033	-0.003	-0.03	0.7443
MSTNG-5	0.9142	0.1569	0.1598	0.1446	-0.01	0.036	-0.048	0.6121
PRP/EX3	0.6921	0.4262	0.3785	0.3354	0.117	0.012	0.107	0.1146
PRP/IN2	0.7029	0.4177	0.4000	0.3305	0.046	0.002	0.045	0.4700
TL4SNP214R	0.5335	0.4978	0.4302	0.3739	0.137	-0.006	0.143	0.0727
U8	0.9167	0.1528	0.1556	0.1411	-0.021	0.006	-0.026	0.9663
Mean	0.7661	0.3160	0.2819	0.2545	0.052	0.018	0.034

Table 2: Average observed heterozygosity (H_O ), average expected heterozygosity (H_E) and F_IS over population
Breed	H_O	H_E	F_IS

Capore	0.239	0.294	0.139
Dukati	0.314	0.320	-0.008
Hasi	0.290	0.316	0.033
Liqenasi	0.311	0.311	-0.038
Mati	0.258	0.294	0.074
Muzhake	0.278	0.300	0.018

Values of observed heterozygosity ranged from 0.239 (Capore) to 0.314 (Dukati) (Table 2). All breeds have close values of expected heterozygosity. Dukati and Liqenasi displayed negative F_IS values (-0.008 and -0.038) respectively.

Genetic distances

Nei’s standard genetic distance (D_S) and pairwise F_ST values between populations are shown in Table 3. The smallest genetic distance is displayed between Muzhake and Liqenasi (0.008) and the greatest distance between Capore and Dukati (0.0274). Pairwise F_STvalues ranged from 0.0007 (Muzhake – Liqenasi) to 0.0374 (Dukati – Capore). F_STper locus ranged from -0.012 (GDFSNP452R) to 0.081 (DRB-G-3) with an average value of 0.018. The F_ST value of 0.018 indicates that almost 98% of the total variability is due to within breed variation, and that 2% of total variability separates the breeds.

Matrix of Nei’s standard genetic distance is used to construct UPGMA phylogenetic tree (Figure 1). Bootstrap values at the nodes are lower than 50 %, displaying a low robustness of UPGMA tree.

Figure 2 displays the results of Factorial Component Analysis. The multivariate analysis is carried out in order to visualize the relationships between the used individuals. Individuals of all breeds are grouped together indicating that the breeds are not differentiated but display a high level of admixture. These results further support the low bootstrap values characterizing the each of the UPGMA nodes.

Table 3: Nei’s standard genetic distance (below ) and pairwise F_ST values (above)
	Capore	Dukati	Hasi	Liqenasi	Mati	Muzhake

Capore	*****	0.0374	0.0176	0.0119	0.0240	0.0082
Dukati	0.0274	*****	0.0335	0.0176	0.0206	0.0187
Hasi	0.0177	0.0262	*****	0.0187	0.0148	0.0088
Liqenasi	0.0146	0.0175	0.0181	*****	0.0311	-0,0007
Mati	0.0198	0.0184	0.0156	0.0233	*****	0.0100
Muzhake	0.0126	0.0175	0.0128	0.0080	0.0128	*****

Figure 1: UPGMA phylogenetic tree based on Nei’s standard distance

Figure 2: Results of factorial component analysis (FCA showing the
relationship between all the individuals analyzed in the study)

Figure 3: Results of the STRUCTURE analysis showing ΔK values

The statistic Delta K(DK) peaked at K = 3 (Figure 3) indicating support for 3 groups. Figure 4 shows a graphical representation of the estimated membership coefficients to the clusters for each individual, (K= 3). Each individual is represented by a single vertical line, broken into 3 colored segments, whose lengths are proportional to each of the three inferred clusters.

Figure 4: Graphical representation of the estimated membership coefficients (Q)
for each individual in each cluster for K= 3 in six goat breeds.

Table 4: Number of animals sampled per breed and number of animals not correctly assigned, as well as sensitivity, specificity and average probability values calculated for each breed using different assignment methods.
Pop	No. Individuals	Paetkau et al. (1995)				Baudouin& Lebrun (2001)				Rannala& Mountain (1997)
Pop	No. Individuals	Incorrect	Sensitivity	specificity	Average Prob	Incorrect	Sensitivity	specificity	Average Prob	Incorrect	Sensitivity	specificity	Average Prob

pop1	31	17	0.45	0.40	58.42	17	0.45	0.37	54.96	17	0.45	0.38	55.72
pop2	31	20	0.35	0.32	62.21	20	0.35	0.34	58.41	20	0.35	0.33	60.07
pop3	31	18	0.42	0.42	62.84	19	0.39	0.41	59.01	18	0.42	0.43	59.21
pop4	31	26	0.16	0.22	66.49	26	0.16	0.24	61.14	26	0.16	0.23	63.25
pop5	30	16	0.47	0.36	57.20	16	0.47	0.36	54.66	17	0.43	0.35	56.82
pop6	31	28	0.10	0.13	36.39	27	0.13	0.15	34.09	28	0.10	0.12	35.33
Total	185	125	0.32	0.32	57.26	125	0.32	0.32	53.71	126	0.32	0.32	55.07

The assignment of individuals to their reference population is carried out by two Bayesian methods and the frequency based method. All methods performed equally, with an overall sensitivity of 32% (60 out 185 individuals), which is a low value. Average probability of assignment was higher than 50%. Average specificity index for all methods was 32%.

Discussion

Based on overall F_ST value, most of the allelic variations were accounted for within breeds variation (98.2%); the between breeds variation being poor (1.8%). The poor between breed variation is in accordance with the results (2% and 3%) obtained using microsatellite markers (Hoda et al 2011a) and AFLP markers (Hoda et al 2012) for the same breeds, respectively.

The results of Structure analysis are supported also by FCA (figure 2), which indicate that breeds are not differentiated.

Genetic variation is very low referring to the low bootstrapping values at nodes of UPGMA tree (figure 1). A very low percentage of individuals were correctly assigned to their reference population. Analysis showed a low specificity and sensitivity. Also the genetic distances between breeds were very small. The results of assignment test can be used to identify pure breed individuals that might be used in the breeding programs in the near future. The low percentage of correctly assigned individuals to their reference population, reflect also the high level of gene flow and shows that the breeds are genetically very close.

Based on the results of this study and results of previous studies of AFLP (Hoda et al 2012) and microsatellite markers (Hoda et al 2011a) we may conclude that Albanian goat breed are important reservoir of genetic diversity, have a low level of differentiation and high level of admixture. The pure breed individuals identified by assignment tests may be used in the breeding programs. It is already known the effectiveness of SNP markers for identifying the source breed of individuals of unknown origin (Negrini et al 2008a). All this results may be used and help in starting a breeding strategy and policy. A decision has to be done concerning crossbreeding or pure breeding.

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Table S 1: Allelic frequencies, heterozygosity for each locus in six Albanian goat breeds
LOCI		Cap	Duk	Has	Liq	Mat	Muzh	Means

ACVR2/
Frequencies	A	0.516	0.500	0.500	0.500	0.500	0.500	0.503
Frequencies	G	0.484	0.500	0.500	0.500	0.500	0.500	0.497
Heterozygotes proportion		0.968	1.000	1.000	1.000	1.000	1.000	0.995
Nei's genic diversity		0.508	0.508	0.508	0.508	0.508	0.508	0.508

CALPA/
Frequencies	A	0.017	0.019	0.016	0	0.017	0	0.011
Frequencies	G	0.983	0.981	0.984	1.000	0.983	1.000	0.989
Heterozygotes proportion		0.033	0.037	0.032	0.000	0.033	0.000	0.023
Nei's genic diversity		0.033	0.037	0.032	0.000	0.033	0.000	0.023

CALSNP
Frequencies	A	0.650	0.655	0.500	0.650	0.448	0.583	0.581
Frequencies	G	0.100	0.121	0.016	0.033	0	0.050	0.053
Frequencies	T	0.250	0.224	0.484	0.317	0.552	0.367	0.366
Heterozygotes proportion		0.433	0.517	0.484	0.433	0.207	0.600	0.446
Nei's genic diversity		0.514	0.515	0.524	0.484	0.503	0.532	0.512

CSN1S1
Frequencies	A	0.467	0.362	0.229	0.310	0.367	0.214	0.325
Frequencies	G	0.533	0.638	0.771	0.690	0.633	0.786	0.675
Heterozygotes proportion		0.333	0.448	0.375	0.621	0.467	0.357	0.434
Nei's genic diversity		0.506	0.470	0.361	0.436	0.472	0.343	0.431

CSN3/E
Frequencies	A	0.931	0.690	0.742	0.839	0.750	0.817	0.795
Frequencies	G	0.069	0.310	0.258	0.161	0.250	0.183	0.205
Heterozygotes proportion		0.138	0.414	0.387	0.194	0.367	0.100	0.267
Nei's genic diversity		0.131	0.436	0.389	0.275	0.381	0.305	0.319

CTSK-G
Frequencies	A	0.018	0.056	0.093	0	0.017	0.034	0.036
Frequencies	G	0.982	0.944	0.907	1.000	0.983	0.966	0.964
Heterozygotes proportion		0.036	0.111	0.111	0.000	0.033	0.069	0.060
Nei's genic diversity		0.036	0.107	0.171	0.000	0.033	0.068	0.069

DESMIN
Frequencies	A	0.293	0.648	0.407	0.383	0.448	0.328	0.418
Frequencies	G	0.707	0.352	0.593	0.617	0.552	0.672	0.582
Heterozygotes proportion		0.448	0.407	0.444	0.433	0.414	0.586	0.456
Nei's genic diversity		0.422	0.465	0.492	0.481	0.503	0.448	0.468

DQA/Ch
Frequencies	A	1.000	0.907	0.914	0.808	0.914	0.857	0.900
Frequencies	G	0	0.093	0.086	0.192	0.086	0.143	0.100
Heterozygotes proportion		0.000	0.185	0.034	0.308	0.103	0.143	0.129
Nei's genic diversity		0.000	0.171	0.160	0.317	0.160	0.249	0.176

DQA/Ch
Frequencies	A	0.417	0.148	0.190	0.250	0.269	0.204	0.246
Frequencies	G	0.583	0.852	0.810	0.750	0.731	0.796	0.754
Heterozygotes proportion		0.083	0.148	0.190	0.269	0.231	0.185	0.185
Nei's genic diversity		0.496	0.257	0.316	0.382	0.401	0.331	0.364

DRB-G-
Frequencies	A	0.119	0.048	0.136	0.300	0	0.095	0.116
Frequencies	G	0.881	0.952	0.864	0.700	1.000	0.905	0.884
Heterozygotes proportion		0.048	0.095	0.273	0.200	0.000	0.095	0.118
Nei's genic diversity		0.215	0.093	0.241	0.431	0.000	0.177	0.193

FABP4/
Frequencies	A	1.000	1.000	1.000	0.983	1.000	1.000	0.997
Frequencies	G	0	0	0	0.017	0	0	0.003
Heterozygotes proportion		0.000	0.000	0.000	0.033	0.000	0.000	0.006
Nei's genic diversity		0.000	0.000	0.000	0.033	0.000	0.000	0.006

FN1- G
Frequencies	A	0.800	0.864	0.661	0.905	0.800	0.886	0.819
Frequencies	G	0.200	0.136	0.339	0.095	0.200	0.114	0.181
Heterozygotes proportion		0.200	0.273	0.393	0.095	0.320	0.136	0.236
Nei's genic diversity		0.328	0.241	0.456	0.177	0.327	0.206	0.289

GDFSNP
Frequencies	A	0.200	0.241	0.161	0.233	0.138	0.200	0.196
Frequencies	G	0.800	0.759	0.839	0.767	0.862	0.800	0.804
Heterozygotes proportion		0.200	0.207	0.194	0.267	0.207	0.267	0.223
Nei's genic diversity		0.325	0.373	0.275	0.364	0.242	0.325	0.317

GHR-G-
Frequencies	A	0.481	0.577	0.648	0.577	0.533	0.552	0.561
Frequencies	G	0.519	0.423	0.352	0.423	0.467	0.448	0.439
Heterozygotes proportion		0.593	0.385	0.481	0.231	0.467	0.345	0.417
Nei's genic diversity		0.509	0.498	0.465	0.498	0.506	0.503	0.496

IL2/5p
Frequencies	A	0.983	0.980	0.983	0.966	1.000	0.952	0.977
Frequencies	G	0.017	0.020	0.017	0.034	0	0.048	0.023
Heterozygotes proportion		0.033	0.040	0.033	0.069	0.000	0.097	0.045
Nei's genic diversity		0.033	0.040	0.033	0.068	0.000	0.094	0.045

IL2/In
Frequencies	A	0.966	0.889	0.914	0.871	0.800	0.883	0.887
Frequencies	G	0.034	0.111	0.086	0.129	0.200	0.117	0.113
Heterozygotes proportion		0.069	0.222	0.172	0.258	0.400	0.233	0.226
Nei's genic diversity		0.068	0.201	0.160	0.228	0.325	0.210	0.199

IL4SNP
Frequencies	A	0.383	0.276	0.387	0.350	0.310	0.452	0.360
Frequencies	G	0.617	0.724	0.613	0.650	0.690	0.548	0.640
Heterozygotes proportion		0.500	0.345	0.516	0.633	0.414	0.645	0.509
Nei's genic diversity		0.481	0.407	0.482	0.463	0.436	0.503	0.462

ITGB1-
Frequencies	A	0.296	0.519	0.239	0.383	0.448	0.446	0.389
Frequencies	G	0.704	0.481	0.761	0.617	0.552	0.554	0.611
Heterozygotes proportion		0.296	0.519	0.391	0.500	0.345	0.464	0.419
Nei's genic diversity		0.425	0.509	0.372	0.481	0.503	0.503	0.465

Lact-G
Frequencies	A	0.212	0.500	0.160	0.375	0.173	0.240	0.277
Frequencies	G	0.788	0.500	0.840	0.625	0.827	0.760	0.723
Heterozygotes proportion		0.346	0.500	0.240	0.583	0.192	0.400	0.377
Nei's genic diversity		0.340	0.511	0.274	0.479	0.292	0.372	0.378

LIPE-G
Frequencies	A	0.241	0.340	0.315	0.152	0.444	0.167	0.276
Frequencies	G	0.759	0.660	0.685	0.848	0.556	0.833	0.724
Heterozygotes proportion		0.407	0.440	0.407	0.217	0.370	0.333	0.363
Nei's genic diversity		0.372	0.458	0.440	0.264	0.503	0.284	0.387

mel-G-
Frequencies	A	0.500	0.500	0.630	0.435	0.538	0.522	0.521
Frequencies	G	0.500	0.500	0.370	0.565	0.462	0.478	0.479
Heterozygotes proportion		0.333	0.583	0.519	0.609	0.538	0.522	0.517
Nei's genic diversity		0.511	0.511	0.475	0.502	0.507	0.510	0.503

MSTNG-
Frequencies	A	0.839	0.926	0.827	0.966	0.983	0.931	0.912
Frequencies	G	0.161	0.074	0.173	0.034	0.017	0.069	0.088
Heterozygotes proportion		0.250	0.148	0.346	0.069	0.033	0.138	0.164
Nei's genic diversity		0.275	0.140	0.292	0.068	0.033	0.131	0.156

PRP/EX
Frequencies	A	0.345	0.276	0.383	0.397	0.183	0.267	0.308
Frequencies	G	0.655	0.724	0.617	0.603	0.817	0.733	0.692
Heterozygotes proportion		0.345	0.414	0.367	0.517	0.367	0.267	0.379
Nei's genic diversity		0.460	0.407	0.481	0.487	0.305	0.398	0.423

PRP/IN
Frequencies	A	0.700	0.680	0.633	0.661	0.817	0.724	0.703
Frequencies	G	0.300	0.320	0.367	0.339	0.183	0.276	0.297
Heterozygotes proportion		0.267	0.480	0.333	0.613	0.367	0.345	0.401
Nei's genic diversity		0.427	0.444	0.472	0.455	0.305	0.407	0.418

TL4SNP
Frequencies	A	0.467	0.517	0.597	0.600	0.534	0.483	0.533
Frequencies	G	0.533	0.483	0.403	0.400	0.466	0.517	0.467
Heterozygotes proportion		0.467	0.414	0.355	0.533	0.310	0.500	0.430
Nei's genic diversity		0.506	0.508	0.489	0.488	0.506	0.508	0.501

U8?SNP
Frequencies	A	0.950	0.845	0.935	0.950	0.897	0.919	0.916
Frequencies	G	0.050	0.155	0.065	0.050	0.103	0.081	0.084
Heterozygotes proportion		0.100	0.310	0.129	0.100	0.207	0.097	0.157
Nei's genic diversity		0.097	0.267	0.123	0.097	0.189	0.151	0.154

ALL LOCI
Mean alleles number		1.962	2.000	2.000	1.962	1.885	1.962
Number of alleles standard deviation		0.344	0.283	0.283	0.344	0.326	0.344
Mean heterozygotes proportion		0.266	0.332	0.316	0.338	0.284	0.305
Heterozygotes proportion standard deviation		0.226	0.218	0.213	0.252	0.221	0.238
Mean Nei's genic diversity		0.308	0.330	0.326	0.326	0.307	0.310
Nei's genic diversity standard deviation		0.193	0.178	0.166	0.182	0.192	0.170

Received 28 February 2013; Accepted 27 March 2013; Published 2 April 2013

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