Livestock Research for Rural Development 25 (4) 2013 Guide for preparation of papers LRRD Newsletter

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 FST value 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 FIS parameter, ranged between -0.989 (ACVR2) to 0.499 (DQA) having a total mean of 0.034 for all loci. FIT values ranged from -0.9894 (ACVR2) to 0.512 (DQA). The global heterozygosity deficit (FIT) was estimated 0.052 and global breed differentiation evaluated by FST, was estimated 0.018.

 

Table 1: Major allele frequency (MAF), observed heterozygosity (Ho), expected heterozygosity (He), PIC values, FST values, deviation from HWE (p-value) for each SNP.

 

 

Marker

MAF

HE

HO

PIC

FIT

FST

FIS

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 (HO), average expected heterozygosity (HE) and FIS over population

Breed

HO

HE

FIS

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 FIS values (-0.008 and -0.038) respectively.  

Genetic distances 

Nei’s standard genetic distance (DS) and pairwise FST 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 FST values ranged from 0.0007 (Muzhake – Liqenasi) to 0.0374 (Dukati – Capore). FST per locus ranged from -0.012 (GDFSNP452R) to 0.081 (DRB-G-3) with an average value of 0.018. The FST 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. cit_af ref_bf(Belkhir, 2001 ref_num133)ref_af 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 FST 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

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 FST 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.


References

Baudouin L and Lebrun P 2000 An operational Bayesian approach for the identification of sexually reproduced cross-fertilized populations using molecular markers. International Symposium on Molecular Markers for Characterizing Genotypes and Identifying Cultivars in Horticulture 546 81-93.

Belkhir K, Borsa P, Chikhi L, Raufaste N and Bonhomme F 2001 GENETIX, software under Windows TM for the genetic of populations. Montpellier, France: Laboratory Genome, Populations, Interactions CNRS UMR 5000.

Bozgo V, Hoda A, Bišoku Y and Bajramaj R 2012: Visible genetic profile and genetic distances of local goat populations in Albania. LivestockResearch for Rural Development.Volume 24, Article #49.Retrieved February 27, 2013, from http: //www.lrrd.org/lrrd24/3/hoda24049.htm

Cappuccio I, Pariset L, Ajmone-MarsanP, Dunner S, Cortes O, Erhardt G, Luhken G, Gutscher K, Joost S, Nijman IJ and others 2006 Allele frequencies and diversity parameters of 27 single nucleotide polymorphisms within and across goat breeds. Molecular Ecology Notes 6, (4): 992-997.

Evanno G, Regnaut S and Goudet J 2005 Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular ecology 14, (8): 2611-2620.

Goudet J 2001 FSTAT, a program to estimate and test gene diversities and fixation indices (version 2.9. 3).

Hoda A, Hyka G, Dunner S, Obexer-Ruff G and others 2011a Genetic diversity of Albanian goat breeds based on microsatellite markers. Archivos de Zootecnia 60, (231): 607-615.

Hoda A, Hykaj G, Sena L and Delia E 2011b Population structure in three Albanian sheep breeds using 36 single nucleotide polymorphisms. ActaAgriculturaeScand Section A 61, (1): 12-20.

Hoda A, Sena L and Hykaj G 2012 Genetic diversity revealed by AFLP markers in Albanian goat breeds. Archives of Biological Sciences 64, (2): 799-807.

Kijas JW, Lenstra JA, Hayes B, Boitard S, Neto LR, San Cristobal M, Servin B, McCulloch R, Whan V, Gietzen K and others 2012 Genome-wide analysis of the world's sheep breeds reveals high levels of historic mixture and strong recent selection. PLoS Biology 10, (2): e1001258.

Liu K and Muse S 2004 PowerMarker: new genetic data analysis software. Distributed by the author at http: //www.powermarker. net.

Negrini R, Nicoloso L, Crepaldi P, Milanesi E, Colli L, Chegdani F, Pariset L, Dunner S, Leveziel H, Williams JL and others 2008a Assessing SNP markers for assigning individuals to cattle populations. Animal Genetics 40, (1): 18-26.

Negrini R, Nicoloso L, Crepaldi P, Milanesi E, Marino R, Perini D, Pariset L, Dunner S, Leveziel H, Williams JL and others 2008b Traceability of four European Protected Geographic Indication (PGI) beef products using Single Nucleotide Polymorphisms (SNP) and Bayesian statistics. Meat science 80, (4): 1212-1217.

Neto LR and Barendse W2010 Effect of SNP origin on analyses of genetic diversity in cattle. Animal Production Science 50, (8): 792-800.

Paetkau D, Calvert W, Stirling I and Strobeck C 1995 Microsatellite analysis of population structure in Canadian polar bears. Molecular Ecology 4, (3): 347-354.

Pariset L, Cappuccio I, Joost S, D'Andrea M, Marletta D, AjmoneMarsan P and Valentini A 2006a Characterization of single nucleotide polymorphisms in sheep and their variation as evidence of selection. Animal Genetics 37, (3): 290-292.

Pariset L, Cappuccio I, Marsan PA, Dunner S, Luikart G, England PR, Obexer-Ruff G, Peter C, Marletta D, Pilla F 2006b Assessment of population structure by single nucleotide polymorphisms (SNPs) in goat breeds. Journal of Chromatography B 833, (1): 117-120.

Pariset L, Cuteri A, Ligda C, Ajmone-Marsan P, Valentini A and Econogene Consortium 2009a Geographical patterning of sixteen goat breeds from Italy, Albania and Greece assessed by Single Nucleotide Polymorphisms. BMC Ecology 9, 20.

Pariset L, Joost S, Marsan PA, Valentini A 2009b Landscape genomics and biased FST approaches reveal single nucleotide polymorphisms under selection in goat breeds of North-East Mediterranean. BMC genetics 10, (1): 7.

Piry S, Alapetite A, Cornuet JM, Paetkau D, Baudouin L and Estoup A 2004 GENECLASS2: a software for genetic assignment and first-generation migrant detection. Journal of heredity 95, (6): 536-539.

Pritchard JK, Stephens M and Donnelly P 2000 Inference of population structure using multilocus genotype data. Genetics 155, (2): 945-959.

Rannala B and Mountain JL 1997 Detecting immigration by using multilocus genotypes. Proceedings of the National Academy of Sciences 94, (17): 9197-9201.

Rousset F 2008 genepop’007: a complete re-implementation of the genepop software for Windows and Linux. Molecular Ecology Resources 8, (1): 103-106.

Yang W, Kang X, Yang Q, Lin Y, Fang M and others 2013 Review on the development of genotyping methods for assessing farm animal diversity. Journal of animal science and biotechnology 4, (1): 1-6.




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

Go to top