The genetic structure of the goat breeds belonging to Northwest part of Fertile Crescent

Journal Pre-proof The Genetic Structure of the Goat Breeds belonging to Northwest Part of Fertile Crescent Sabri Gul

PII:

S0921-4488(18)30249-9

DOI:

https://doi.org/10.1016/j.smallrumres.2019.09.009

Reference:

RUMIN 5985

To appear in:

Small Ruminant Research

Received Date:

5 April 2018

Revised Date:

1 August 2019

Accepted Date:

10 September 2019

Please cite this article as: Gul S, The Genetic Structure of the Goat Breeds belonging to Northwest Part of Fertile Crescent, Small Ruminant Research (2019), doi: https://doi.org/10.1016/j.smallrumres.2019.09.009

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The Genetic Structure of the Goat Breeds belonging to Northwest Part of Fertile Crescent

Sabri GUL

Hatay Mustafa Kemal University, Agricultural Faculty, Department of Animal Science, Hatay Mustafa Kemal University, Agricultural Faculty, Department of Anim, 31020, Hatay Turkey

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Email [email protected]

Highlights 

The study was performed to reveal genetic diversity and population structure of four

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native Turkish goat breeds (Hatay, Kilis, Shami and Hair).

The study showed that breeds have a noticeable genetic variability

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Microsatellites used have a highly accurate identification potency for the genetic

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diversity of the studied breeds

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

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Abstract

The present study was performed to reveal genetic diversity and population structure of four native Turkish goat breeds (Hatay, Kilis, Shami and Hair), raised in Hatay and Kilis provinces located in northwest part of Fertile

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Crescent, using twenty-two microsatellite markers. Animal material for the study was consisted of 246 goats from Hair (60), Shami (62), Kilis (64) and Hatay (60) breeds. A total of 458 alleles were detected from twenty-

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two markers studied. Results obtained for the mean number of alleles (20.82), expected heterozygosity (0.89) and polymorphic information content (0.88) indicated that the total analyzed population is characterized by noticeable genetic variability. The value obtained for the global coefficient of gene differentiation showed that the majority of the total genetic variation is due to individual differences (97.50%). It can be said that the microsatellite markers used in the present study are sufficient to identify the genetic diversity of the goat populations studied.

Key words: Goat, Fertile Crescent, Microsatellites, Population structure,

1. Introduction The origin of domestic goats remain ambiguous and controversial issue, but archaeological findings probably indicate that they were domesticated 10 000 years ago in the Fertile Crescent region covering Israel, Turkey, Lebanon, Jordan, and Syria (Zeder and Hesse, 2000; Luikart et al., 2001; Taberlet et al., 2011; Amills et al., 2017). In recent years, goat breeding has become an important livestock activity that stands out economically all

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over the world (Darcan and Silanikove, 2017). Goat breeding in Turkey that has an important traditional background plays an important role in meeting the animal protein deficit by evaluating areas that other farm animals cannot use (Gursoy, 2006).

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The goat population in Turkey is around 10.5 million head according to FAO data (FAO, 2016). Although, Hair goat population constitutes about 90% of total goat population in Turkey, it is possible to mention many different

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goat genotypes such as Norduz, Angora, Honamli, Abaza, Shami, Hatay and Kilis breeds that have been well adapted to different regions (Keskin and Biçer, 1997; Keskin, 2000; Yilmaz et al., 2012).

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It was reported that Kilis and Shami (Damascus) goat breed is known to have high milk yield and reproductive characteristics raised in Kilis, Gaziantep, Hatay, Şanlıurfa provinces located on southern part of Turkey. It is noteworthy that the Shami goat population, which originated from Middle East, has increased and widely raised

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in the provinces such as Hatay, Gaziantep, Şanlıurfa, Kilis and Mardin that located along the border between Turkey and Syria (Güney et al., 1992; Keskin and Biçer, 2002; Keskin et al., 2004; Gül, 2008). The Kilis goat

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breed is thought to have obtained with non-systematic crossbreeding between Shami (Damascus) and Hair goat breeds by farmers in many years (Yalçın, 1986; Keskin and Biçer, 1997; Keskin, 2000; Keskin et al., 2017). It

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has been reported that the Hatay goat genotype, known also as Yayladag breed, raised in the mountainous parts of Hatay province obtained by the crossbreeding between Kilis and Hair goat breeds (Keskin and Biçer, 1997; Kaya, 1999; Gül, 2008). Hair goat breed which has a very good adaptation to difficult environmental conditions are widely raised almost everywhere in Turkey (TAGEM, 2011). The first step for a well-structured and sustainable animal breeding and conservation program is to reveal detailed information on intra and inter-breed genetic diversity. This situation indicates how important it is to

reveal the genetic structure of breeds. The present study was carried out in order to determine genetic diversity and population structure of four different native goat breeds raised in northwest part of Fertile Crescent. 2. Materials and Methods All experimental procedures were carried out according to the permission given by Mustafa Kemal University Local Ethics Committee numbered 40595970-604.01.02/ 2015/10-2. 2.1. Sampling method and DNA isolation Blood samples were obtained from 246 goats which consist of Hair (60), Shami (62), Kilis (64) and Hatay (60) goat breeds raised in seventeen flocks in different districts where located in Hatay and Kilis provinces (Figure 1). Blood samples were collected from Vena jugularis into tubes containing K3EDTA as anticoagulant and stored at

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-20°C until DNA extraction. DNA was extracted by using salting-out technique reported by Miller et al. (1988) and Montgomery and Sise (1990). NanoDrop 2000 (Thermo Scientific, Waltham, MA) spectrophotometer device was used to determinate quality and quantity of DNA samples.

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2.2. PCR reaction and fragment analysis

In this study, twenty-two microsatellite markers recommended by FAO (2011) were used to determine the

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genetic diversity and population structure of goat populations studied. One member of each pair of primers were labeled with one of the D2, D3 and D4 fluorescent dyes and primers were grouped in two multiplex. Touchdown

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PCR protocols reported by Hecker and Roux, (1996) was used for amplification of specific genomic regions (Table 1). Polymerase chain reaction (PCR) amplifications were carried out in 20 μl total volumes, containing 0.10 μM of primers (forward and reverse), 0.20 mM dNTPs, 2.0 mM MgCl 2, 1X PCR buffer, 1 U of Taq DNA

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polymerase (Applied Biological Materials Inc.), and ~50 ng of genomic DNA. Beckman Coulter GeXP genetic analyzer (Beckman Coulter, Inc., USA) was used for the separation of the PCR

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fragments. GenomeLab™ DNA Size Standard Kit 400 was used for determination of fragment size. 2.3. Statistical analysis

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The polymorphism statistics such as number of alleles per locus (Na), mean number of alleles (MNa), effective number of alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He) and Hardy–Weinberg equilibrium were calculated using GenAlEx (Peakall and Smouse, 2006, 2012) and POPGENE (Yeh et al., 1997). Polymorphic information content (PIC) and null allele frequencies were calculated using CERVUS 3.0.3 (Marshall, 1998/2006; Kalinowski et al., 2007), while Wright’s F-statistics (FIT, FIS, FST) (Weir and Cockerham, 1984; Wright, 1990) were obtained with POPGENE 1.32 (Yeh et al., 1997). Population 1.2.32 (Langella, 1999) and FigTree 1.4.2. (Rambout, 2006) software were used to generate phylogenetic tree between breeds according

to Nei's Da distance matrix (Nei et al., 1983). Robustness of the dendrogram topology was tested by bootstrap resampling (n=1000). FSTAT version 2.9.3 software (Goudet, 2001) was used to obtain values belong to genetic diversity statistics such as Nei’s gene diversity (HT), diversity between breeds (DST), and coefficient of gene differentiation (GST). Analysis of molecular variance (AMOVA), which is a method to detect population differentiation utilizing molecular markers, was performed using the ARLEQUIN package version 3.5.2.2 (Excoffier and Lischer, 2010). The STRUCTURE software that include cluster techniques based on the Bayesian approach were used to analyze population structures (Pritchard et al., 2000; Falush et al., 2003, 2007; Hubisz et al., 2009). The population structure analysis was performed using independent allele frequencies and an admixture model (burn

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of 20.000, followed by 100.000 MCMC iterations with 20 replicate runs for each K).

The appropriate number of clusters was identified using ΔK values that expressing the proportion of alteration in the logarithmic probability Pr(X|K) of data between K values according to a method (ΔK=m|L''(K)|/s[L(K)])

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reported by Evanno et al. (2005). The most suitable K value was determined according to with ΔK value calculated by the STRUCTURE harvester program (Earl and Vonholdt, 2012). The CLUMPAK program

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reported by Kopelman et al. (2015) was used to find the best alignment from the obtained STRUCTURE results. 3. Results

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In the present study a total 458 alleles were detected from twenty-two microsatellites used to determine genetic diversity. Molecular genetic polymorphism statistics obtained from the microsatellites used was given in Table 2. The highest number of alleles and effective alleles were observed for BM1818 (33) and SRCRSP15 (13.74)

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loci, while the polymorphic information content values ranged between 0.75 (ILSTS011) and 0.92 (SRCRSP15, TCRVB6, DRBP1 and INRA0132). The mean value of observed heterozygosity was 0.78, varying between 0.57

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(ILSTS011) and 0.96 (DRBP1). In addition, expected heterozygosity values (He) were found to be between 0.78 (ILSTS011) and 0.93 (TCRVB6). The average of FIS, FIT and FST values, described as Wright’ F statistics, were

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0.088, 0.117 and 0.032, respectively. Mean value of DST revealing the diversity between breeds, GST defining the coefficient of gene differentiation, and HT indicating Nei’s gene diversity values were found as 0.022, 0.025 and 0.888, respectively. The twentytwo microsatellite loci used in the study were tested using the χ2 test in terms of compliance with HardyWeinberg equilibrium (HWE). The χ2 test findings demonstrated that the allele distributions of all microsatellite markers did not comply with the Hardy-Weinberg equilibrium. It is seen that all microsatellites have values

below 20%, which is the critical value for null allele frequency, when the null allele frequencies are examined. Genetic polymorphism parameters belong to four autochthonous Turkish goat breeds were given in Table 3. The lowest and highest number of alleles were observed in KG (12.91) and HG (16.55) breeds, respectively, while the highest observed heterozygosity value (0.823) was calculated in HG breed. FIS which is also known as the inbreeding coefficient and measures the reduction in heterozygosity, varied between 0.091 (SG and HG) and 0.127 (HAG). A total of 86 private alleles were identified in the present study. Private allele numbers according to breeds were 11, 31, 14 and 30 for KG, HAG, SG and HG breeds, respectively. Analysis of Molecular Variance (AMOVA), which is a method of determining population differentiation using molecular data, was used for detection of genetic variation between individuals and populations (Table 4).

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It was revealed 86.84% of the total variance was found within individuals while 10.65% among individuals within populations and 2.51% among population. The phylogenetic tree based on Nei's Da (Nei et al., 1983) distance matrix belong to breeds studied was given in Figure 2.

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The dendrogram revealed three clusters (Figure 2). The first cluster was consisted by Kilis and Hatay goat breeds, the second cluster was formed by Shami goat sampled from Reyhanlı district and the third cluster was

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formed by Hair goat breed sampled from Belen district.

Population structure analysis result which is containing different clustering numbers (K=2-4) was given in

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Figure 3. Findings that include estimates of posterior probabilities ([Ln Pr (X | K)] for ΔK values were presented in Table 5.

The results obtained from the STRUCTURE analysis were similar to the defined dendrogram using Nei's Da

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distance matrix (Nei et al., 1983) as expected. It is seen that the optimal number of groups was 3 considering the value of ΔK obtained by the method reported by Evanno et al. (2005).

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4. Discussion

The mean numbers of alleles and polymorphic information content indicated that the microsatellites used in the

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study show high polymorphism. This situation can be regarded as an important indicator of high genetic diversity in goat populations studied. Values obtained in the present study for molecular genetic parameters such as total allele number, PIC, Ho and He were higher than values reported in the previous studies (Serrano et al., 2009; Agaoglu and Ertugrul, 2012; Hykaj et al., 2012; Amie Marini et al., 2013; Meutchieye et al., 2014; Awobajo et al., 2015; Mishra et al., 2015; Singh et al., 2015; Zaman and Shekar, 2015; El-Sayed et al., 2016). FIS values, which is a measure of the deviation of genotypic frequencies from panmixia in populations in terms of heterozygous deficiency or excess, showed that loss of heterozygosity at five microsatellite loci (BM1818,

OarFCB20, INRABERN185, DRBP1 and INRABERN172). Similar findings have been expressed in the previous literature conducted in different goat breeds (Gurler and Bozkaya, 2013; Martinez et al., 2015; Zaman and Shekar, 2015; Du et al., 2017). The global FST value (0.032), which is lower than the values stated in the earlier studies (Meutchieye et al., 2014; Murital et al., 2015; Zaman and Shekar, 2015), can be interpreted as a sign that the genetic diversity among populations is relatively low. The global GST value showed that 97.50% of the total genetic variation can be explained by genetic differences among individuals. It can be accepted that the overall genetic diversity value (DST) obtained from the present study was an indication that the inter-population variability is not high. As a matter of fact, this finding supported the previously mentioned FST and GST results.

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Overall Nei’s gene diversity (HT) value was 0.888 which was higher than the values obtained from Jamunapari (Gour et al., 2006), Marwari (Kumar et al., 2005) and Africa Sub-saharan indigenous goat breeds (Muema et al., 2009) but lower than those of native goats raised in Algeria (Tefiel et al., 2018). This situation is an indication

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that goats have a high genetic diversity of populations studied. On the other hand, these results are consistent with findings by Lenstra et al. (2017) and Freeman et al. (2006) that show that high genetic diversity is an

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expected phenomenon in populations raised in areas close to domesticated regions.

The χ2 test results showed that allele distributions of twenty-two microsatellite markers studied were not in the

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Hardy-Weinberg equilibrium. It is expected results that the studied loci will deviate from the Hardy-Weinberg equilibrium because of the intensive selection studies which are carried out in the populations studied. Observed null allele frequencies for the all microsatellites below the critical value (20%) reported by Dakin and Avise

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(2004) indicated that these markers studied can be used confidently to identify genetic diversity in these native goat breeds.

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In the present study, the calculated MNa value in terms of the breeds was found to be higher than the values stated in some studies on domestic and foreign breeds (Gurler and Bozkaya, 2013; Mahrous et al., 2013; Al-

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Atiyat et al., 2015; Awobajo et al., 2015; Zaman and Shekar, 2015; Bulut et al., 2016). Expected heterozygosity (He) values for KG, SG, HAG and HG breeds were higher than the values reported by Bulut et al. (2016) for the same breeds. This is thought to be due to the difference in the number of microsatellites and sampling methodology used in this study. FIS value, defined as inbreeding coefficient, indicated that there is no loss of heterozygosity in populations. The χ2 test results revealed that the 17, 17, 13 and 11 microsatellite loci deviated from the Hardy-Weinberg equilibrium in the Kilis, Hatay, Shami and Hair goat populations, respectively. Deviations from the Hardy-

Weinberg equilibrium should be regarded as a natural consequence of the intensive animal breeding activities that have been practiced in the populations for many years. Although a total of 86 private alleles were identified for four populations, only one allele frequency defined in the HG population was above 5%. In other words, it can be said that observed private alleles do not have sufficient efficiency to identify populations studied. It is seen that the essential genetic diversity is realized within individuals, when the results of the analysis of molecular variance (AMOVA) are examined. Fixation index values give an idea in terms of inbreeding coefficient and population differences. Analysis of molecular variance (AMOVA) results pointed out that these four native goat breeds can be differentiated weakly. The FST value obtained from the AMOVA analysis shows that 97.50% of the genetic diversity can be explained by the genetic difference between individuals, just as it is

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in the GST value.

It was noticed that there were 3 clusters when the dendrogram was examined. Dendrogram, which is showed position of the breeds, in the present study was different from the findings obtained from the study conducted on

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the same breeds by Bulut et al. (2016). This is due to the differences in number of markers used and sampling locations in both studies.

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STRUCTURE results showed a low level of differentiation and a high level of admixture in Shami and and Hair goat populations while the value of ΔK obtained by the method reported by Evanno et al. (2005) shows that the

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optimal number of groups is 3 as in the dendrogram. This revealed that STRUCTURE analysis and dendrogram results supported each other. It can be said that there was a high gene flow between Shami and Hair goat populations when examined the STRUCTURE Harvester results. It is thought that the Hair goat breeders in the

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region may have benefited from Shami bucks in order to increase milk and meat yield. It is reasonable that Hatay goat genotype, which is obtained by the crossbreeding between Kilis and Hair goat breeds (Keskin and Biçer,

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1997; Kaya, 1999; Gül, 2008), is genetically close to the Kilis goat breed. 5. Conclusion

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In conclusion, the present study has revealed an important knowledge about the genetic diversity and the relationship between some goat breeds raised in the Fertile Crescent region, which is known as one of the most important domestication sites. The results showed that the breeds studied have a high genetic diversity. Microsatellites used in the study have a highly accurate identification potency for the genetic diversity of the studied breeds. The information obtained in the study has made a significant contribution to the future animal genetic conservation and breeding programs. Conflict of interest

The authors declare that they have no conflict of interest. Acknowledgments This study was supported by Mustafa Kemal University Scientific Research Projects Institutional Coordinator (Project No: 15380). We acknowledge Republic of Turkey Ministry of Food, Agriculture and Livestock for supplying animal materials and Adnan Menderes University Agricultural Biotechnology and Food Safety Application and Research Centre (ADÜ-TARBİYOMER) for providing laboratory facilities to carry out

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molecular genetic analysis.

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Zaman, G., Shekar, M.C., 2015. Genetic diversity of indigenous goat populations of north east India including West Bengal based on microsatellite markers. Animal Molecular Breeding 5, 1-7. Zeder, M.A., Hesse, B., 2000. The initial domestication of goats (Capra hircus) in the Zagros mountains 10,000

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years ago. Science 287, 2254-2257.

Hatay

Kilis

Hair Goat (HG) 5 Flock (N=60)

Kilis Goat (KG)

Belen

5 Flock (N=64) Reyhanlı

Shami Goat (SG) 4 Flock (N=62)

Yayladağı

Hatay Goat (HAG)

ro of

3 Flock (N=60)

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Figure 1. Origin and sample size of goat breeds

Figure 2. Dendrogram based on Nei's Da (Nei 1983) distance matrix among four native goat breeds (bootstrap

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resampling methodology (1000 replicates))

K=2

K=3 K=4

Hatay

Kilis

Kıl

Shami

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Figure 3. CLUMPAK plot of STRUCTURE assignment results (K=2-4)

Table 1. Thermal cycling conditions used for touchdown PCR Multiplex

First Loci

Group

Final Denaturation Annealing Extension

Cycles

Denaturation

Extention

OarAE54 INRA0132 BM1818 OarFCB20 INRA0005 95 °C

95 °C

60-50 °C

72 °C

INRA0023

72 °C 30

(5 min)

(40 s)

(40 s)

ILSTS011 SRCRSP9 ILST0019

(10 min)

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SRCRSP15

(60 s)

ro of

M1

TCRVB6

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SRCRSP3 BM1329

SRCRSP7

na

McM0527

lP

ETH10

CSRD0247

95 °C

M2

95 °C

60-50 °C

72 °C

INRABERN185

(5 min)

ur

SRCRSP0023 ILSTS0087

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SRCRSP0005 DRBP1

INRABERN172 HSC(OLADRB)

72 °C 30

(40 s)

(40 s)

(60 s)

(10 min)

Table 2. Genetic polymorphism parameters of the twenty-two investigated loci in all goat population studied Allelic range

PI Na

Ne

Ho

101-147

24

8.72

0.8 0.71

8 12.8 INRA0132

142-200

33

2

9.97

17

7.17

16

7.48

11.8 187-251

6

1

2

19

4.58 5 0.8

SRCRSP9

101-141

19

8.41

211-251

4

2

13.5

0.9

18

0.9

2

ur 156-200

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BM1329

SRCRSP7

19

109-139

20

5.52

8

6.92

141-189

207-249

21

0

0.174

1

0.01 0.203

8

0.01

0.86

3

4

0.05

0.86

7

8

0.03

0.91

9

9

0.02

0.78

5

4

0.00

0.88

9

3

0.01

0.92

7

9

0.00

0.92

7

8

0.03

0.82

0

3

0.01

0.89

5

2

0.02

0.85

7

8

0.02

0.90

7

8

0.00

0.88

0.025 5

0.8 0.69

7

0.03 0.145

2

0.023 5

0.9 0.75

8.59

0.03 0.207

6

0.9

7

0.013 3

0.8

0.8 CSRD0247

0.262

9

4

6

0.02

0.179

0.90

0.025 7

0.245

0.68

22

0.120

0.8 0.66

0.8 16

0.03 0.086

0.05

0.006 3

2

0.8

10.5 McM0527

0

9.06

0.01

0.8 0.72

5

0.016

0.044

3

0.8

88-128

6

4

0.031

9

0.008

0.088

3

0.89

0.01

0.02 0.065

0.92

0.019

3

0.190

0.9

0.85

0

SRCRSP3

0.176

8

0.9

na

TCRVB6

20

0.03

0.265

0.8

lP 13.7

150-202

8

0.01

0.036

6

0.240

0.71 7

SRCRSP15

0.7 0.57

0.173

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248-298

0.04

0.133

0

0.050

4

0.9 0.76

0.7 ILSTS011

0.147

7

0.9

9

0.06

0.088

1

0.011

9

0.8 0.74

31

0.01 -0.043 -0.022

F(Null

E

)

***

0.108

***

0.031

***

0.003

***

0.018

0.89

0.051 3

0.8

5

INRA0023

0.000

HW HT

0.018

0.06 -0.067

0.88

0.05

7

0

0.8 127-203

0.059

0.9

5

INRA0005

GST

0.045

0.02 0.033

0.90

0.8 89-121

DST

8

2

9

OarFCB20

0.201

0.9 0.87

0.8 200-286

FST*

0.05 0.151

9

0.9

26 6

BM1818

FIT*

C 0.8

OarAE54

FIS*

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(bp)

He

-p

Loci

0.214

0.006 4

7

6

***

0.086

***

0.098

***

0.161

***

0.109

***

0.046

**

0.027

***

0.065

***

0.150

***

0.116

***

0.094

***

0.120

0.9

20

5

0.9 0.90

9

1

77-129

24

7.15

13

6.70

18

9.95

13.1 183-231

INRABERN17 232-262

173-303

10.5

0.9

16

2

HSC

2

0

12.1

0.9

21 7 20.8

Mean

1

0.8 0.78

8

0.061

2

0.8

2

0.01

0.043

9

6

3

0.04

0.85

4

3

0.01

0.90

3

2

0.02

0.92

3

5

0.01

0.90

6

7

0.01

0.92

0.012

9

0.03

0.088

0.86

0.014

2

0.9

1

9.55

0.02

-0.036 -0.013

0.86

0.01

0.021 9

0.9 0.92

6

0.02 -0.066 -0.035

2

5

0.012 0

0.9 0.96

3

0.166

0

0.9

25

0.02 0.149

0

0.037 1

0.9 0.75

9

DRBP1

0.174

5

0.8 154-188

0.05 0.130

0.91

0.014 3

0.8 0.70

4

SRCRSP0005

0.126

6

0.8 131-155

0.02 0.106

0.02 0.019

7

0.8 0.75

5

ILSTS0087

0.014

1

0.8 SRCRSP0023

0.02 -0.013

***

0.007

***

0.064

***

0.097

***

0.092

***

0.018

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11.5 257-295

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INRABERN18

0.117

2

3

0

0.02

0.88

5

8

***

0.007

***

0.029

0.022

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Na: number of alleles, Ne: effective number of alleles, PIC: polymorphic information content, Ho: observed heterozygosity, He: expected heterozygosity, * Wright’s F-statistics (Weir and Cockerham, 1984), DST: the diversity

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between breeds, GST: coefficient of gene differentiation, HT: Nei’s gene diversity, HWE: Hardy-Weinberg

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Equilibrium, F(Null): null allele frequency, * P<0.05, ** P<0.01, *** P<0.001

Table 3. Genetic diversity parameters per breed across 22 microsatellites studied Ĥ Breeds

NPA

MNa

FIS Ho

HWE

Freq.

Freq.

≥5%

<%5

He

Total

KG

12.91

0.737

0.835

0.125***

17

11

11

HAG

16.55

0.763

0.865

0.127***

17

31

31

SG

15.55

0.807

0.859

0.091***

13

14

14

HG

15.86

0.823

0.874

0.091***

11

29

30

1

Ho: mean observed heterozygosity, He:

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KG: Kilis, HAG: Hatay, SG: Shami, HG: Hair, MNa: Mean number of alleles, Ĥ: Average heterozygosity, mean expected heterozygosity, FIS: within-breed heterozygote

deficiency, HWE: number of loci not in the Hardy- Weinberg equilibrium (P < 0.05), NPA: number of private

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alleles, ***: P < 0.001

Table 4. Results for analysis molecular variance (AMOVA) revealing the distribution of genetic diversity Variation Sources

DF

SS

VC

PV (%)

FI

Among population

3

64.043

0.1286 Va

2.51

FIS= 0.109

Among individuals within populations

242

1339.53

0.5451 Vb

10.65

FST= 0.025

Within individuals

246

1093.5

4.4451 Vc

86.84

FIT= 0.132

Total

491

2497.073

5.1188

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DF: degree of freedom, SS: sum of square, VC: variance components, PV: percentage of variance, FI: fixation index

Table 5. Estimated posterior probabilities [Ln Pr(X|K)] for different numbers of inferred clusters (K) and ΔK statistics ΔK

[Ln Pr(X|K)]

2

-24502.055000

3

-24451.445000

3.139936

4

-24569.290000

2.304268

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K