Advertisement

Conservation Genetics Resources

, Volume 10, Issue 1, pp 73–78 | Cite as

Subset of SNPs for parental identification in European bison Lowland-Białowieża line (Bison bonasus bonasus)

  • Kamil Oleński
  • Stanisław Kamiński
  • Małgorzata Tokarska
  • Dorota M. Hering
Open Access
Technical Note
  • 465 Downloads

Abstract

Parentage testing and individual identification are essential for protection and efficient management of animal populations. Panels of highly polymorphic SNP markers have recently become available in microarrays addressed to domestic animals. Such SNPs may also be useful in closely-related species living in the wild. The European bison (Bison bonasus), as an extremely inbred extinct species, requires individual identification to sustain maximum genetic diversity. The aim of the work was to prepare a panel of highly polymorphic SNPs for parental and individual identification for the wisent. 163 bison were genotyped on Illumina BovineHD microarray. Data filtration resulted in the panel of 100 highly polymorphic, unlinked SNPs giving the probability of parentage exclusion equal to 3.90 × 10−6 for one and 3.55 × 10−14 for two known parents and a probability of identity equal to 1.14 × 10−40. The probabilities obtained in this study are sufficient for effective management of the genetic diversity of the Lowland-Białowieża line of the European bison.

Keywords

European bison SNP Individual identification Genetic diversity management 

Parentage testing and animal identification are essential for protection and efficient management of animal populations (Werner et al. 2004), among others, for estimation of effective population size, reduction of the inbreeding level and to minimize mating between close relatives (Quader 2005). Because of the wide availability and high polymorphic information content (PIC), microsatellite markers (STRs) were used for this purpose (Glowatzki-Mullis et al. 1995), but lower mutation and genotyping error rate, automation of genotyping, ease of data manipulation and calculation caused that panels of single nucleotide polymorphisms (SNPs) have displaced STRs (Heaton et al. 2002; Werner et al. 2004). Recently, the International Committee for Animal Recording (ICAR) developed a cattle consensus panel of 100 SNPs for routine parentage testing (Fernández et al. 2013).

The European Bison (EB, Bison bonasus) became extinct in the Białowieża Forest in 1919. The contemporary Lowland-Białowieża line was restored from seven animals preserved in zoological gardens and reintroduced into the wild (Pucek et al. 2004). The high (and still rising) inbreeding coefficient, ranging from 0.35 to 0.48 (Matuszewska et al. 2004; Wołk and Krasińska 2004), is the result of the bottleneck effect. The lack of modern parentage and individual identification based on genetic markers may lead to a further decrease in the genetic diversity of this species. Tokarska et al. (2009) tested the effectiveness of a routine set of cattle STRs for parentage testing and showed that these markers are too homozygous in wisent and suggested the creation of a panel of 50–60 of the most heterozygous loci for paternity and identity analysis. Labuschagne et al. (2015) also proved that SNP markers perform better than STRs in wild populations. Since Kamiński et al. (2012) showed that cattle SNPs genotyped on Illumina bovine microarrays could be used for differentiation of two lines of EB, we hypothesized that bovine 777k SNPs microarray is a dataset suitable to elaborate a subset of SNPs used for individual and parentage control in the bison.

One hundred and sixty-three European bison DNA samples were genotyped using Illumina BovineHD microarray (777,962 SNPs). SNP genotyping quality analysis, pair-wise Linkage Disequilibrium (LD) analysis and SNP selection was performed in Golden Helix SVS 8.3.3 (Bozeman, MT). SNPs with missing genotypes, localized on mitochondrial DNA, chromosome Y, non-pseudoautosomal region of X chromosome, unknown genomic location, deviated from the Hardy–Weinberg equilibrium (P < 0.0001) and with minor allele frequency less than 0.45 were removed from analysis. For purposes of sex verification, the final set of SNPs was supplemented by one SNP localized on the Y chromosome. For ascertainment purposes, nine randomly-selected polymorphic and monomorphic SNPs were sequenced.

Standard formulas were used to convert the allelic frequencies into the probability of parentage exclusion (Jamieson and Taylor 1997):

For one parent:

$$P{E_1}~=1 - 4\mathop \sum \limits_{i=1}^n p_{i}^{2}+2\,{\left( {\mathop \sum \limits_{i=1}^n p_{i}^{2}} \right)^2}+4\mathop \sum \limits_{i=1}^n p_{i}^{3} - 3\mathop \sum \limits_{i=1}^n p_{i}^{4}$$

For two parents:

$$P{E_2}=1+4\mathop \sum \limits_{i=1}^n p_{i}^{4} - 4\mathop \sum \limits_{i=1}^n p_{i}^{5} - 3\mathop \sum \limits_{i=1}^n p_{i}^{6} - 8\,{\left( {\mathop \sum \limits_{i=1}^n p_{i}^{2}} \right)^2}+8\,\left( {\mathop \sum \limits_{i=1}^n p_{i}^{2}} \right)\left( {\mathop \sum \limits_{i=1}^n p_{i}^{3}} \right)+2\,{\left( {\mathop \sum \limits_{i=1}^n p_{i}^{3}} \right)^2}$$

The probabilities were calculated for each locus tested, where p i is the frequency of allele i, n the number of alleles (two per SNP). The total exclusion power was calculated by combining all P values of the tested loci as follows:

$$PE=1-\left( {1-{P_1}} \right){ }\left( {1-{P_2}} \right){ }\left( {1-{P_3}} \right) \ldots { }\left( {1-{P_k}} \right),$$
where k is the number of loci used.

The probability of identity was calculated according to Waits et al. (2001):

$$PI=\prod\limits_{i=1}^r {\left( {\sum\limits_{j=1}^{{n_i}} {p_{i}^{4}+\sum {\sum {p_{i}^{2}p_{j}^{2}} } } } \right)}$$
where p i , p j are the frequencies of the i-th and j-th allele, n is the number of alleles and r is the number of the tested loci. Calculations were performed using Microsoft Excel™.

The Average Call Rate of the genotyped animals was 0.95. Data clean-up resulted in elimination of 776,699 SNPs, which did not meet the selection criteria. Pair-wise SNP LD analysis for remaining 1263 SNPs showed that the minimal distance between two unlinked SNPs was 10 Mb.

A list of 100 SNPs, their rs numbers, allele frequencies, probabilities of identity and parentage exclusion for one and two parents are presented in Table 1. All chosen SNPs were checked in Illumina GenomeStudio software for quality of clustering to ensure the correctness of genotyping process. Moreover, the sequencing of randomly-selected 9 SNPs confirmed the same genotypes in cattle and European bison (Supplementary Fig. 1).

Table 1

Allele frequencies, probability of exclusion (PE) and probabilities of identity (PI) of 100 SNPs selected for parentage testing of European bison

SNP No.

SNP

Rs number

Chr

Position (bp)

Allele 1

Allele 2

Allele 1 frequency

Allele 2 frequency

PI

PE 1

1 − PE 1

PE 2

1 − PE 2

1

BovineHD0100018862

rs136286678

1

66,765,504

A

B

0.494

0.506

0.3750

0.1250

0.8750

0.2812

0.7188

2

BovineHD0100025238

rs135563119

1

88,822,246

A

B

0.455

0.545

0.3770

0.1230

0.8770

0.2800

0.7200

3

BovineHD0100029514

rs42699476

1

103,534,432

A

B

0.497

0.503

0.3750

0.1250

0.8750

0.2812

0.7188

4

BovineHD0100044552

rs43280194

1

153,061,324

B

A

0.464

0.536

0.3763

0.1237

0.8763

0.2804

0.7196

5

BovineHD0200018552

rs136874797

2

64,242,565

B

A

0.485

0.515

0.3752

0.1248

0.8752

0.2811

0.7189

6

BovineHD0200025645

rs109014713

2

90,292,035

B

A

0.476

0.524

0.3756

0.1244

0.8756

0.2809

0.7191

7

BovineHD0200032234

rs109742535

2

111,915,910

A

B

0.494

0.506

0.3750

0.1250

0.8750

0.2812

0.7188

8

BovineHD0300000026

rs13629654

3

178,815

B

A

0.485

0.515

0.3752

0.1248

0.8752

0.2811

0.7189

9

BovineHD0300006218

rs135769437

3

19,492,096

A

B

0.479

0.521

0.3754

0.1246

0.8754

0.2810

0.7190

10

BovineHD0300013843

rs135022334

3

45,374,877

A

B

0.479

0.521

0.3754

0.1246

0.8754

0.2810

0.7190

11

BovineHD0300019972

rs43341054

3

67,472,000

B

A

0.473

0.527

0.3757

0.1243

0.8757

0.2808

0.7192

12

BovineHD0300029062

rs43356243

3

101,588,334

B

A

0.485

0.515

0.3752

0.1248

0.8752

0.2811

0.7189

13

BovineHD0400013038

rs110513205

4

47,512,665

A

B

0.497

0.503

0.3750

0.1250

0.8750

0.2812

0.7188

14

BovineHD0400018716

rs132784204

4

68,104,392

B

A

0.491

0.509

0.3751

0.1249

0.8751

0.2812

0.7188

15

BovineHD0400021348

rs109587413

4

77,115,921

B

A

0.461

0.539

0.3765

0.1235

0.8765

0.2803

0.7197

16

BovineHD0400027021

rs109627788

4

96,774,849

B

A

0.488

0.512

0.3751

0.1249

0.8751

0.2812

0.7188

17

BovineHD0400031738

rs132661534

4

110,758,145

A

B

0.455

0.545

0.3770

0.1230

0.8770

0.2800

0.7200

18

ARSBFGL-NGS-55084

rs29002595

5

60,513,092

B

A

0.479

0.521

0.3754

0.1246

0.8754

0.2810

0.7190

19

BovineHD0500030609

rs133884477

5

106,662,467

B

A

0.461

0.539

0.3765

0.1235

0.8765

0.2803

0.7197

20

BovineHD0500035252

rs132793428

5

120,686,738

B

A

0.452

0.548

0.3773

0.1227

0.8773

0.2798

0.7202

21

BovineHD0600014476

rs132922283

6

52,520,699

A

B

0.470

0.530

0.3759

0.1241

0.8759

0.2807

0.7193

22

BovineHD0600023700

rs133837492

6

86,253,147

A

B

0.494

0.506

0.3750

0.1250

0.8750

0.2812

0.7188

23

BovineHD0700000997

rs135579274

7

3,575,614

B

A

0.482

0.518

0.3753

0.1247

0.8753

0.2810

0.7190

24

BovineHD0700010587

rs109539616

7

36,725,057

B

A

0.470

0.530

0.3759

0.1241

0.8759

0.2807

0.7193

25

BovineHD0700017674

rs41568577

7

61,435,959

B

A

0.455

0.545

0.3770

0.1230

0.8770

0.2800

0.7200

26

BovineHD0700023643

rs109199550

7

81,232,113

B

A

0.458

0.542

0.3768

0.1232

0.8768

0.2802

0.7198

27

BovineHD0700029051

rs136888354

7

99,268,758

A

B

0.485

0.515

0.3752

0.1248

0.8752

0.2811

0.7189

28

BovineHD0800009625

rs43085958

8

31,835,367

A

B

0.482

0.518

0.3753

0.1247

0.8753

0.2810

0.7190

29

BovineHD0800013761

rs42483244

8

46,038,743

A

B

0.488

0.512

0.3751

0.1249

0.8751

0.2812

0.7188

30

BovineHD0800023332

rs134977085

8

77,875,839

B

A

0.497

0.503

0.3750

0.1250

0.8750

0.2812

0.7188

31

ARSBFGL-NGS-32919

rs109033097

8

112,797,372

B

A

0.470

0.530

0.3759

0.1241

0.8759

0.2807

0.7193

32

BovineHD0900019312

rs110150334

9

69,901,898

B

A

0.482

0.518

0.3753

0.1247

0.8753

0.2810

0.7190

33

BovineHD0900026688

rs135010487

9

94,157,917

A

B

0.482

0.518

0.3753

0.1247

0.8753

0.2810

0.7190

34

BovineHD0900029204

rs136183786

9

100,694,891

A

B

0.461

0.539

0.3765

0.1235

0.8765

0.2803

0.7197

35

BovineHD1000002864

rs134809673

10

8,617,403

B

A

0.452

0.548

0.3773

0.1227

0.8773

0.2798

0.7202

36

BovineHD1000009377

rs43618930

10

28,626,325

B

A

0.464

0.536

0.3763

0.1237

0.8763

0.2804

0.7196

37

BovineHD1000013459

rs42733726

10

44,768,055

B

A

0.455

0.545

0.3770

0.1230

0.8770

0.2800

0.7200

38

ARSBFGL-NGS-28353

rs43643434

10

62,361,408

B

A

0.458

0.542

0.3768

0.1232

0.8768

0.2802

0.7198

39

BovineHD1000023118

rs109466870

10

81,085,417

B

A

0.488

0.512

0.3751

0.1249

0.8751

0.2812

0.7188

40

BovineHD1100004462

rs110945789

11

13,615,809

B

A

0.458

0.542

0.3768

0.1232

0.8768

0.2802

0.7198

41

BovineHD1100014461

rs110920732

11

49,178,036

B

A

0.491

0.509

0.3751

0.1249

0.8751

0.2812

0.7188

42

BovineHD1200000275

rs109455392

12

1,203,844

B

A

0.470

0.530

0.3759

0.1241

0.8759

0.2807

0.7193

43

BovineHD1200003800

rs136299989

12

13,099,815

B

A

0.497

0.503

0.3750

0.1250

0.8750

0.2812

0.7188

44

BovineHD1200007015

rs134293192

12

23,321,834

A

B

0.455

0.545

0.3770

0.1230

0.8770

0.2800

0.7200

45

BovineHD1200014680

rs43430233

12

53,231,319

B

A

0.470

0.530

0.3759

0.1241

0.8759

0.2807

0.7193

46

BovineHD1200018970

rs42698329

12

69,224,796

B

A

0.491

0.509

0.3751

0.1249

0.8751

0.2812

0.7188

47

BovineHD1300000700

rs134199917

13

2,529,705

A

B

0.461

0.539

0.3765

0.1235

0.8765

0.2803

0.7197

48

BovineHD1300007444

rs135734935

13

25,662,598

B

A

0.464

0.536

0.3763

0.1237

0.8763

0.2804

0.7196

49

BovineHD1300018524

rs134100887

13

65,193,252

A

B

0.479

0.521

0.3754

0.1246

0.8754

0.2810

0.7190

50

BovineHD1300022064

rs42797972

13

76,266,797

B

A

0.464

0.536

0.3763

0.1237

0.8763

0.2804

0.7196

51

BovineHD1400000437

rs133281796

14

2,745,140

A

B

0.470

0.530

0.3759

0.1241

0.8759

0.2807

0.7193

52

BovineHD1400006448

rs133022208

14

22,324,172

A

B

0.467

0.533

0.3761

0.1239

0.8761

0.2806

0.7194

53

BovineHD1400012101

rs137391153

14

42,496,100

B

A

0.464

0.536

0.3763

0.1237

0.8763

0.2804

0.7196

54

BovineHD1500003622

rs137357332

15

14,596,760

B

A

0.491

0.509

0.3751

0.1249

0.8751

0.2812

0.7188

55

BovineHD1500008772

rs137117576

15

32,296,617

B

A

0.455

0.545

0.3770

0.1230

0.8770

0.2800

0.7200

56

BovineHD1500019459

rs43022148

15

67,533,937

B

A

0.458

0.542

0.3768

0.1232

0.8768

0.2802

0.7198

57

BovineHD1500024106

rs109124429

15

82,636,303

B

A

0.452

0.548

0.3773

0.1227

0.8773

0.2798

0.7202

58

BovineHD1600014245

rs43721038

16

51,191,820

B

A

0.455

0.545

0.3770

0.1230

0.8770

0.2800

0.7200

59

BovineHD1600020203

rs136259430

16

71,169,525

A

B

0.482

0.518

0.3753

0.1247

0.8753

0.2810

0.7190

60

BovineHD1600022094

rs109636458

16

76,613,545

A

B

0.458

0.542

0.3768

0.1232

0.8768

0.2802

0.7198

61

BovineHD1700004751

rs135943066

17

16,572,611

B

A

0.482

0.518

0.3753

0.1247

0.8753

0.2810

0.7190

62

BovineHD1700007032

rs110241846

17

24,942,099

A

B

0.464

0.536

0.3763

0.1237

0.8763

0.2804

0.7196

63

BovineHD1800003099

rs110030820

18

8,998,865

B

A

0.461

0.539

0.3765

0.1235

0.8765

0.2803

0.7197

64

BovineHD1800009810

rs43012445

18

32,413,958

B

A

0.470

0.530

0.3759

0.1241

0.8759

0.2807

0.7193

65

BovineHD1800016132

rs41887541

18

55,148,478

B

A

0.482

0.518

0.3753

0.1247

0.8753

0.2810

0.7190

66

BovineHD1900000287

rs110994739

19

1,486,337

B

A

0.473

0.527

0.3757

0.1243

0.8757

0.2808

0.7192

67

BovineHD1900011494

rs136693026

19

40,148,240

A

B

0.458

0.542

0.3768

0.1232

0.8768

0.2802

0.7198

68

BovineHD1900015332

rs133780449

19

54,659,434

A

B

0.452

0.548

0.3773

0.1227

0.8773

0.2798

0.7202

69

BovineHD2000002364

rs42355588

20

7,441,685

B

A

0.482

0.518

0.3753

0.1247

0.8753

0.2810

0.7190

70

BovineHD2100002747

rs134290268

21

11,130,607

B

A

0.458

0.542

0.3768

0.1232

0.8768

0.2802

0.7198

71

BovineHD2100008014

rs110635528

21

27,672,291

B

A

0.488

0.512

0.3751

0.1249

0.8751

0.2812

0.7188

72

BovineHD2100010317

rs110907540

21

35,528,294

B

A

0.455

0.545

0.3770

0.1230

0.8770

0.2800

0.7200

73

BovineHD2100015489

rs42201368

21

54,211,458

A

B

0.485

0.515

0.3752

0.1248

0.8752

0.2811

0.7189

74

BovineHD2200007244

rs109920926

22

24,572,694

A

B

0.461

0.539

0.3765

0.1235

0.8765

0.2803

0.7197

75

BovineHD2200013405

rs134307845

22

46,673,764

B

A

0.470

0.530

0.3759

0.1241

0.8759

0.2807

0.7193

76

BovineHD2300000288

rs110554784

23

1,727,514

A

B

0.497

0.503

0.3750

0.1250

0.8750

0.2812

0.7188

77

ARSBFGL-NGS-46495

rs42170998

23

13,240,935

A

B

0.485

0.515

0.3752

0.1248

0.8752

0.2811

0.7189

78

BovineHD2300012460

rs137212715

23

43,143,802

A

B

0.455

0.545

0.3770

0.1230

0.8770

0.2800

0.7200

79

BovineHD2300015160

rs137638378

23

52,206,874

A

B

0.482

0.518

0.3753

0.1247

0.8753

0.2810

0.7190

80

BovineHD2400008177

rs42044506

24

30,165,362

A

B

0.464

0.536

0.3763

0.1237

0.8763

0.2804

0.7196

81

BovineHD2400016255

rs109144765

24

56,844,369

A

B

0.491

0.509

0.3751

0.1249

0.8751

0.2812

0.7188

82

BovineHD2500001897

rs134560483

25

7,368,070

A

B

0.452

0.548

0.3773

0.1227

0.8773

0.2798

0.7202

83

ARSBFGL-NGS-28385

rs42068371

25

28,049,888

A

B

0.467

0.533

0.3761

0.1239

0.8761

0.2806

0.7194

84

BovineHD2600001604

rs109999676

26

7,026,224

A

B

0.488

0.512

0.3751

0.1249

0.8751

0.2812

0.7188

85

BovineHD2600007195

rs133371041

26

27,037,762

B

A

0.470

0.530

0.3759

0.1241

0.8759

0.2807

0.7193

86

BovineHD2600014263

rs42267563

26

49,480,029

A

B

0.485

0.515

0.3752

0.1248

0.8752

0.2811

0.7189

87

BovineHD2700004769

rs136772128

27

16,440,007

B

A

0.452

0.548

0.3773

0.1227

0.8773

0.2798

0.7202

88

BovineHD2700009299

rs43731760

27

32,909,364

B

A

0.482

0.518

0.3753

0.1247

0.8753

0.2810

0.7190

89

BovineHD2700012714

rs42232579

27

43,864,575

B

A

0.500

0.500

0.3750

0.1250

0.8750

0.2813

0.7188

90

BovineHD2800005814

rs135672627

28

21,991,716

B

A

0.485

0.515

0.3752

0.1248

0.8752

0.2811

0.7189

91

BovineHD2800011886

rs109445577

28

42,282,392

A

B

0.494

0.506

0.3750

0.1250

0.8750

0.2812

0.7188

92

BovineHD2900000502

rs42160181

29

2,120,188

A

B

0.467

0.533

0.3761

0.1239

0.8761

0.2806

0.7194

93

BovineHD2900005791

rs42766254

29

20,026,356

A

B

0.485

0.515

0.3752

0.1248

0.8752

0.2811

0.7189

94

BovineHD3000046360

rs136542028

X

34,867,125

B

A

0.473

0.527

0.3757

0.1243

0.8757

0.2808

0.7192

95

BovineHD3000031079

rs135507872

X

111,616,675

B

A

0.500

0.500

0.3750

0.1250

0.8750

0.2813

0.7188

96

BovineHD3000039232

rs134960289

X

136,972,415

B

A

0.500

0.500

0.3750

0.1250

0.8750

0.2813

0.7188

97

BovineHD3000039269

rs136115690

X

137,109,768

B

A

0.500

0.500

0.3750

0.1250

0.8750

0.2813

0.7188

98

BovineHD3000047666

rs135918718

X

143,829,946

B

A

0.500

0.500

0.3750

0.1250

0.8750

0.2813

0.7188

99

BovineHD3000041451

rs133539883

X

143,837,030

B

A

0.500

0.500

0.3750

0.1250

0.8750

0.2813

0.7188

100

BovineHD3100000186

rs133685563

Y

0

A

B

0.467

0.533

0.3761

0.1239

0.8761

0.2806

0.7194

 

Mean

     

0.474

0.526

1.14 × 10−40

Total

3.90 × 10−6

 

3.55 × 10−14

The probability of parentage exclusion varied from 3.90 × 10−6 to 3.55 × 10−14 for one and two known parents, respectively. The theoretical probability of identity was calculated as 1.14 × 10−40. Weller et al. (2006) showed that at least eight SNPs are required to achieve a 99% probability of rejection for a match between two individuals, and a panel of 25 markers provides enough power to identify a single individual between any of the five million individuals with less than a 1% chance for a match between any of five million individuals. So panels of 40–100 SNPs with MAF greater than 0.3 may allow accurate pedigree reconstruction even in situations of thousands of potential family trios with a probability at the level of 1.00 (Baruch and Weller 2008; Fisher et al. 2009). The wisent is an extreme case of the bottleneck effect—less than 3% of genotyped SNPs were heterozygous and the average estimated relatedness between analysed individuals was 0.97 (data not shown), so the chosen SNPs have as high MAF as possible to confirm the identity of analysed individuals between other “clones”. According to presented literature, the values of probabilities obtained in this study should be sufficient for effective identification of all individuals of the Lowland-Białowieża line of EB.

Notes

Acknowledgements

Open access funding provided by University of Warmia and Mazury. This work was financially supported by the University of Warmia and Mazury, Grant No. 105–0804.

Supplementary material

12686_2017_768_MOESM1_ESM.docx (13 kb)
Supplementary material 1 (DOCX 12 KB)
12686_2017_768_MOESM2_ESM.tif (5 mb)
Supplementary material 2 (TIF 5156 KB)

References

  1. Baruch E, Weller JI (2008) Estimation of the number of SNP genetic markers required for parentage verification. Anim Genet 39:474–479CrossRefPubMedGoogle Scholar
  2. Fernández ME, Goszczynski DE, Lirón JP et al (2013) Comparison of the effectiveness of microsatellites and SNP panels for genetic identification, traceability and assessment of parentage in an inbred Angus herd. Genet Mol Biol 36(2):185–191CrossRefPubMedPubMedCentralGoogle Scholar
  3. Fisher PJ, Malthus B, Walker MC, Corbett G, Spelman RJ (2009) The number of single nucleotide polymorphisms and on-farm data required for whole-herd parentage testing in dairy cattle breeds. J Dairy Sci 92:369–374CrossRefPubMedGoogle Scholar
  4. Glowatzki-Mullis ML, Gaillard C, Wigger G, Fries R (1995) Microsatellite-based parentage control in cattle. Anim Genet 26:7–12CrossRefPubMedGoogle Scholar
  5. Heaton MP, Harhay GP, Bennett GL et al (2002) Selection and use of SNP markers for animal identification and paternity analysis in U. S. beef cattle. Mamm Genome 13:272–281CrossRefPubMedGoogle Scholar
  6. Jamieson A, Taylor SC (1997) Comparison of three probability formulae for parentage exclusion. Anim Genet 28:397–400CrossRefPubMedGoogle Scholar
  7. Kamiński S, Olech W, Oleński K, Nowak Z, Ruść A (2012) Single nucleotide polymorphisms between two lines of European bison (Bison bonasus) detected by the use of Illumina Bovine 50 K Bead Chip. Conserv Genet Resour 4:311–314CrossRefGoogle Scholar
  8. Labuschagne C, Nupen L, Kotzé A, Grobler PJ, Dalton DL (2015) Assessment of microsatellite and SNP markers for parentage assignment in ex situ African Penguin (Spheniscus demersus) populations. Ecol Evol 5(19):4389–4399CrossRefPubMedPubMedCentralGoogle Scholar
  9. Matuszewska M, Olech W, Bielecki W, Osinska B (2004) The influence of inbreeding on the pathological changes occurrence in European bison male reproductive tract. Natl Park Nat Reserv 23:679–685Google Scholar
  10. Pucek Z, Belousova IP, Krasinski ZA, Krasinska M, Olech W (2004) European bison status survey and conservation action plan IUCN/SSC bison specialist group. IUCN, GlandGoogle Scholar
  11. Quader S (2005) Mate choice and its implications for conservation and management. Curr Sci 89:1220–1229Google Scholar
  12. Tokarska M, Marshall T, Kowalczyk R et al (2009) Effectiveness of microsatellite and SNP markers for parentage and identity analysis in species with low genetic diversity: the case of European bison. Heredity 103:326–332CrossRefPubMedGoogle Scholar
  13. Waits LP, Luikart G, Taberlet P (2001) Estimating the probability of identity among genotypes in natural populations: cautions and guidelines. Mol Ecol 10:249–256CrossRefPubMedGoogle Scholar
  14. Weller JI, Seroussi E, Ron M (2006) Estimation of the number of genetic markers required for individual animal identification accounting for genotyping errors. Anim Genet 37:387–389CrossRefPubMedGoogle Scholar
  15. Werner FAO, Durstewitz G, Habermann FA et al (2004) Detection and characterization of SNPs useful for identity control and parentage testing in major European dairy breeds. Anim Genet 35:44–49CrossRefPubMedGoogle Scholar
  16. Wołk E, Krasińska M (2004) Has the condition of European bison deteriorated over last twenty years? Acta Theriol 49(3):405–418CrossRefGoogle Scholar

Copyright information

© The Author(s) 2017

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of Animal GeneticsUniversity of Warmia and MazuryOlsztynPoland
  2. 2.Mammal Research Institute Polish Academy of SciencesBiałowieżaPoland

Personalised recommendations