Open Access

The BRCA1 Ashkenazi founder mutations occur on common haplotypes and are not highly correlated with anonymous single nucleotide polymorphisms likely to be used in genome-wide case-control association studies

  • Lutécia H Mateus Pereira1,
  • Marbin A Pineda1,
  • William H Rowe1,
  • Libia R Fonseca2,
  • Mark H Greene3,
  • Kenneth Offit4,
  • Nathan A Ellis5,
  • Jinghui Zhang1,
  • Andrew Collins6 and
  • Jeffery P Struewing1Email author
BMC Genetics20078:68

DOI: 10.1186/1471-2156-8-68

Received: 04 December 2006

Accepted: 04 October 2007

Published: 04 October 2007

Abstract

Background

We studied linkage disequilibrium (LD) patterns at the BRCA1 locus, a susceptibility gene for breast and ovarian cancer, using a dense set of 114 single nucleotide polymorphisms in 5 population groups. We focused on Ashkenazi Jews in whom there are known founder mutations, to address the question of whether we would have been able to identify the 185delAG mutation in a case-control association study (should one have been done) using anonymous genetic markers. This mutation is present in approximately 1% of the general Ashkenazi population and 4% of Ashkenazi breast cancer cases. We evaluated LD using pairwise and haplotype-based methods, and assessed correlation of SNPs with the founder mutations using Pearson's correlation coefficient.

Results

BRCA1 is characterized by very high linkage disequilibrium in all populations spanning several hundred kilobases. Overall, haplotype blocks and pair-wise LD bins were highly correlated, with lower LD in African versus non-African populations. The 185delAG and 5382insC founder mutations occur on the two most common haplotypes among Ashkenazim. Because these mutations are rare, even though they are in strong LD with many other SNPs in the region as measured by D-prime, there were no strong associations when assessed by Pearson's correlation coefficient, r (maximum of 0.04 for the 185delAG).

Conclusion

Since the required sample size is related to the inverse of r, this suggests that it would have been difficult to map BRCA1 in an Ashkenazi case-unrelated control association study using anonymous markers that were linked to the founder mutations.

Background

Numerous advances in our understanding of genetic susceptibility to breast cancer have been made over the past decade, most notably the discovery of BRCA1 in 1994 and BRCA2 in 1995 [1, 2]. Mutations in these genes account for approximately 2/3 of families with clearly inherited forms of breast and ovarian cancer (5 or more cases in a family) [3, 4]. In addition to the high-penetrance genes BRCA1/BRCA2, rare mutations in a number of other genes, such as CHEK2, ATM, BRIP1, and PALB1 predispose to breast cancer [59], as do more common variants in CASP8 and TGFB1[10]. The total heritability of breast cancer is at least 10% [11, 12], and possibly up to 25% or higher [13, 14]. Mutations in known high-risk genes, however, account for a relatively small proportion (probably less than 20%) of the excess risk due to genetic factors [15, 16].

Fueled by the completion of the first phases of the HapMap project [17], which characterized common variation within the genome of four population groups, there is considerable interest in using these resources to map susceptibility genes for common, complex diseases. The genome-wide case-control association study, whereby the prevalence of genetic variants is compared between cases and unrelated control subjects without the disease, may have the greatest power to identify novel susceptibility genes [1820]. They rely on using a very dense set of markers that capture a significant fraction of all common genetic variation, such that the variants assayed either include those that are biologically relevant, or those which are highly-correlated with the former due to linkage disequilibrium. Although some design and analysis issues remain, numerous common variants have now been identified for breast cancer and other conditions using this design [21, 22].

Breast cancer may serve as a useful paradigm for common, complex disease mapping studies because, while a portion of susceptibility genes have been identified, the majority of the residual familial clustering remains unexplained, and is likely to be polygenic in nature, due to a number of lower-penetrance genes in the context of environmental exposures [23, 24]. Furthermore, there are two common Ashkenazi Jewish (AJ) founder BRCA1 mutations, 185delAG and 5382insC, initially identified in linkage studies of multiple-case breast/ovarian cancer families [25]. This contrasts with most other populations in which there are numerous unique BRCA1/BRCA2 mutations, with none present at 1% or greater population frequency. The BRCA1 AJ founder mutations account for the majority of Jewish breast-ovarian cancer families, and are present in approximately 1% of the general Jewish population [26]. The AJ founder mutations, owing to their high prevalence compared to other populations, offered an opportunity to test whether they might have been identified through a case-control association study of the kind suggested as the new gene discovery strategy in the post-HapMap era.

Results

SNP allele frequencies

A total of 289 unrelated reference subjects selected without regard to breast cancer from five population groups (48 each from African-Americans, Chinese-Americans and Mexican-Americans, 60 CEPH subjects, and 85 Ashkenazi Jews) were genotyped across BRCA1, spanning a region of approximately 646 kb. Table 1 presents the allele frequencies and Hardy-Weinberg P-vales for all the 112 polymorphic SNPs and the two founder mutations that were typed for all 5 populations. Eight of 570 tests showed departures from equilibrium at the 0.01 level, but because none of the eight showed Mendelian segregation errors within families, and because of the number of comparisons performed, they were not excluded from later calculations. Allele frequencies were generally highly correlated among Ashkenazi Jews, CEPH, Chinese-Americans and Mexican-Americans (minimum r >0.82), whereas African-Americans presented the lowest correlation values with all the other populations (maximum r <0.44). The highest correlation was found between Ashkenazi Jews and CEPH (r = 0.947) and the lowest between African-Americans and Mexican-Americans (r = 0.362). Most of the private SNPs (n = 18) originated in the African-American samples, although private SNPs were also observed in Ashkenazi Jews (n = 4), Chinese-Americans (n = 3), and Mexican-Americans (n = 1) (Table 1). Total observed heterozygosity for each marker across the five populations ranged from 0.4% for private SNPs to 49.3% [see Additional file 1]. FST ranged from 0.0047 (SNP8 – rs8176072) to 0.4338 (SNP102 – rs2593595). Sixty three percent of the SNPs showed little genetic differentiation (FST < 0.05), followed by twenty eight percent with moderate (0.05–0.15) and less than ten percent with higher genetic differentiation. We also calculated pair-wise FST measures, and the distribution was very similar for the Ashkenazi Jews, CEPH, Chinese-Americans and Mexican-Americans versus each other (range from 0.008–0.018) as compared with African-Americans versus all the other populations (range from 0.082–0.092), showing that African-Americans had by far the greatest level of differentiation. These results are congruent with the low allele frequency correlation values observed between African-Americans versus all the other groups.
Table 1

SNP Frequencies and Hardy-Weinberg P-Values

       

Ashkenazi Jews (n = 85)

CEPH (n = 60)

African Americans (n = 48)

Chinese Americans (n = 48)

Mexican Americans (n = 48)

rs number

SNP Number

SNP name

Position b33a

Position b36.1b

Com Allelec

Min Alleled

mafe

P-value

maf

P-value

maf

P-value

maf

P-value

maf

P-value

13119

1

C_9270420

41373486

38718247

c

t

0.424

0.580

0.350

0.712

0.177

0.624

0.396

0.359

0.458

0.961

748620

2

C_9270421

41373037

38717798

c

g

0.440

0.640

0.350

0.712

0.240

0.550

0.391

0.518

0.465

0.669

17599948

3

C_9270454

41364175

38708936

a

g

0.211

0.649

0.192

0.865

0.104

0.022

0.094

0.326

0.362

0.591

11653231

4

C_95201

41240225

38584986

g

a

0.435

0.703

0.350

0.712

0.240

0.550

0.396

0.359

0.467

0.905

9908805

5

C_3178692

41230675

38575436

c

t

  

0.008

0.948

0.375

0.878

  

0.011

0.941

2175957

6

C_11631183

41195587

38540348

t

g

0.447

0.665

0.350

0.712

0.250

0.441

0.396

0.359

0.446

0.935

3092986

7

BR1_000340

41186761

38531522

a

g

0.051

0.636

0.042

0.736

0.010

0.942

  

0.031

0.823

8176072

8

BR1_000392

41186709

38531470

t

a

0.006 f

0.957

        

8176074

9

BR1_000571

41186530

38531291

g

a

    

0.010

0.942

    

3765640

10

C_2615164

41185012

38529773

a

g

0.440

0.640

0.350

0.712

0.228

0.740

0.394

0.433

0.444

0.592

NAg

11

M_185delAG

41184811

38529572

a

-

0.006

0.957

        

8176090

12

BR1_007918

41179183

38523944

c

g

  

0.008

0.948

0.096

0.338

    

1800062

13

BR1_010574

41176528

38521289

g

a

      

0.021

0.883

  

8176101

14

BR1_010796

41176306

38521067

a

t

    

0.021

0.882

    

8176103

15

C_2615171

41175815

38520576

g

a

0.463

0.291

0.350

0.712

0.234

0.640

0.396

0.359

0.458

0.961

8176104

16

BR1_011340

41175762

38520523

g

a

0.024

0.824

0.033

0.789

    

0.010

0.942

8176109

17

C_2615172

41174541

38519302

a

g

0.441

0.497

0.350

0.712

0.239

0.609

0.396

0.359

0.469

0.751

8065872

18

BR1_016775

41170328

38515089

t

a

    

0.135

0.278

  

0.010

0.942

8176120

19

BR1_017105

41169998

38514759

g

a

0.424

0.913

0.350

0.712

0.240

0.550

0.396

0.359

0.458

0.961

799914

20

BR1_018573

41168546

38513307

g

a

    

0.135

0.278

    

799913

21

BR1_019408

41167711

38512472

a

g

0.032

0.772

0.042

0.736

0.365

0.813

  

0.010

0.942

8176128

22

BR1_019904

41167215

38511976

t

a

    

0.063

0.644

    

8176133

23

BR1_020896

41166223

38510984

t

g

0.422

0.738

0.350

0.712

0.167

0.488

0.396

0.359

0.458

0.961

799912

24

C_2615180

41165899

38510660

c

t

0.472

0.701

0.364

0.926

0.125

0.322

0.396

0.359

0.489

0.882

799923

25

BR1_026422

41160696

38505457

c

t

0.232

0.031

0.308

0.858

0.042

0.763

  

0.104

0.459

8176145

26

BR1_029258

41157859

38502620

t

c

0.400

0.038

0.319

0.586

0.240

0.550

0.404

0.309

0.448

0.829

8176146

27

BR1_029448

41157669

38502430

c

t

0.031

0.774

0.008

0.948

      

7503154

28

BR1_030748

41156369

38501130

t

g

0.415

0.618

0.339

0.478

0.223

0.771

0.396

0.359

0.468

0.862

1799950

29

BR1_031875

41155246

38500007

a

g

0.077

0.442

0.042

0.736

0.010

0.942

  

0.031

0.823

4986850

30

BR1_032885

41154236

38498997

g

a

0.133

0.096

0.113

0.071

0.033

0.819

  

0.013

0.935

16940

31

BR1_033119

41154002

38498763

t

c

0.422

0.429

0.350

0.712

0.177

0.624

0.396

0.359

0.448

0.829

799917

32

BR1_033420

41153701

38498462

c

t

0.429

0.766

0.358

0.868

0.125

0.322

0.396

0.359

0.479

0.571

4986852

33

BR1_003927

41153194

38497955

g

a

0.012

0.912

0.042

0.736

0.010

0.942

    

2227945

34

BR1_034226

41152895

38497656

a

g

    

0.052

0.703

  

0.010

0.942

16942

35

BR1_034356

41152765

38497526

a

g

0.433

0.868

0.348

0.615

0.245

0.521

0.396

0.359

0.447

0.716

799916

36

C_7530109

41151955

38496716

t

g

0.438

0.482

0.358

0.868

0.271

0.703

0.394

0.433

0.479

0.894

2070833

37

BR1_035507

41151614

38496375

c

a

0.031

0.001

    

0.271

0.726

0.135

0.167

2070834

38

BR1_036077

41151050

38495811

a

c

0.435

0.626

0.342

0.568

0.250

1.000

0.396

0.359

0.438

0.634

8176158

39

BR1_036793

41150334

38495095

a

g

0.418

0.393

0.353

0.665

0.174

0.532

0.396

0.359

0.458

0.961

8176160

40

BR1_036859

41150268

38495029

a

g

0.435

0.626

0.350

0.712

0.240

0.550

0.396

0.359

0.458

0.961

8176166

41

BR1_038085

41149042

38493803

a

g

0.196

0.391

0.150

0.172

0.104

0.022

0.120

0.632

0.344

0.395

8176174

42

BR1_040350

41146777

38491538

a

t

    

0.063

0.644

    

3950989

43

C_3178696

41146718

38491479

g

a

0.441

0.566

0.348

0.615

0.234

0.640

0.396

0.359

0.457

0.923

8176175

44

BR1_040669

41146458

38491220

t

-

0.006

0.956

    

0.073

0.586

  

8176177

45

BR1_041288

41145840

38490601

a

g

    

0.021

0.883

    

8176178

46

BR1_041721

41145407

38490168

a

g

    

0.042

0.763

    

1060915

47

C_3178676

41143235

38487996

a

g

0.380

0.105

0.352

0.851

0.177

0.624

0.396

0.359

0.468

0.862

3737559

48

C_3178677

41143069

38487830

c

t

0.124

0.481

0.067

0.580

  

0.083

0.208

0.043

0.761

8176187

49

BR1_045154

41141974

38486735

t

c

        

0.010

0.942

8176188

50

BR1_045505

41141623

38486384

t

g

    

0.063

0.644

    

6416927

51

BR1_0046019

41141109

38485870

g

c

      

0.083

0.208

  

8176198

52

BR1_047826

41139302

38484063

t

a

0.422

0.429

0.362

0.427

0.348

0.114

0.426

0.761

0.479

0.571

8176199

53

BR1_0477839

41139289

38484050

a

c

0.341

0.594

0.258

0.502

0.135

0.167

0.177

0.624

0.396

0.135

4239147

54

BR1_048551

41138577

38483338

t

c

0.440

0.640

0.324

0.677

0.271

0.703

0.394

0.433

0.452

0.711

8176206

55

BR1_050244

41136885

38481646

a

g

    

0.052

0.703

  

0.010

0.942

2236762

56

C_11621042

41135440

38480201

a

t

0.435

0.626

0.358

0.868

0.271

0.703

0.406

0.581

0.479

0.894

1799966

57

C_2615208

41131859

38476620

t

c

0.456

0.456

0.350

0.712

0.233

0.781

0.396

0.359

0.469

0.751

3092987

58

BR1_055669

41131488

38476249

a

g

0.416

0.545

0.350

0.712

0.170

0.510

0.396

0.359

0.458

0.961

8176225

59

BR1_056796

41130361

38475122

g

t

    

0.031

0.823

    

8176232

60

BR1_058369

41128788

38473549

c

t

0.013

0.936

        

8176234

61

BR1_058614

41128545

38473306

a

g

0.437

0.345

0.350

0.712

0.239

0.609

0.396

0.359

0.458

0.961

8176235

62

BR1_058834

41128325

38473086

g

a

0.339

0.872

0.258

0.502

0.167

0.488

0.396

0.359

0.436

0.972

8176236

63

BR1_059589

41127570

38472331

t

c

  

0.008

0.948

0.359

0.556

  

0.010

0.942

8176240

64

BR1_060022

41127137

38471898

t

c

    

0.063

0.644

    

8176242

65

BR1_060520

41126639

38471400

g

a

0.433

0.538

0.350

0.712

0.170

0.510

0.406

0.581

0.458

0.961

8176245

66

BR1_061014

41126145

38470906

t

c

    

0.063

0.644

    

3092994

67

C_2615220

41124590

38469351

c

t

0.434

0.471

0.350

0.712

0.228

0.242

0.396

0.359

0.266

0.808

8176259

68

BR1_062588

41124571

38469332

t

-

    

0.052

0.703

    

8176265

69

BR1_0064398

41122761

38467522

g

a

0.417

0.477

0.350

0.712

0.167

0.729

0.394

0.433

0.260

0.848

2187603

70

BR1_064501

41122658

38467419

g

a

0.422

0.429

0.350

0.712

0.167

0.729

0.396

0.359

0.260

0.848

8176273

71

BR1_066741

41120418

38465179

t

c

0.424

0.580

0.342

0.568

0.167

0.729

0.396

0.359

0.260

0.848

8176278

72

BR1_067978

41119181

38463942

a

g

  

0.008

0.948

0.500

0.564

  

0.021

0.883

8066171

73

BR1_068063

41119096

38463857

g

t

    

0.146

0.237

  

0.010

0.942

NA

74

M_5382insC

41117845

38462606

-

c

0.006

0.957

        

8176289

75

C_2615230

41114821

38459582

t

c

0.441

0.566

0.342

0.568

0.223

0.258

0.396

0.359

0.266

0.808

8176293

76

BR1_073023

41114136

38458897

a

-

      

0.031

0.823

  

4793192

77

BR1_074008

41113155

38457916

a

g

0.435

0.626

0.336

0.794

0.229

0.214

0.396

0.359

0.260

0.848

8176296

78

BR1_074807

41112356

38457117

a

g

0.435

0.703

0.350

0.712

0.229

0.214

0.396

0.359

0.260

0.848

3092988

79

C_2615238

41110467

38455228

c

t

0.424

0.580

0.350

0.712

0.170

0.709

0.396

0.359

0.260

0.848

8176303

80

BR1_076933

41110230

38454991

a

g

    

0.010

0.942

0.042

0.763

  

8176305

81

BR1_077034

41110129

38454890

a

g

0.094

0.753

0.092

0.442

0.021

0.883

  

0.010

0.942

8176307

82

BR1_077328

41109835

38454596

t

c

    

0.063

0.644

    

8068463

83

BR1_079220

41107943

38452704

c

t

    

0.125

0.322

    

8176313

84

BR1_079396

41107767

38452528

g

a

0.006

0.957

0.017

0.896

      

8176316

85

BR1_080570

41106594

38451355

c

a

    

0.021

0.883

    

8176318

86

BR1_081125

41106039

38450800

g

t

0.435

0.963

0.350

0.712

0.167

0.729

0.385

0.491

0.250

1.000

12516

87

BR1_081990

41105173

38449934

c

t

0.435

0.626

0.350

0.712

0.229

0.214

0.406

0.581

0.260

0.848

8176320

88

BR1_082035

41105128

38449889

g

a

0.006

0.957

0.033

0.789

    

0.010

0.942

8176321

89

BR1_082199

41104964

38449725

g

a

    

0.010

0.942

    

8176323

90

BR1_082687

41104476

38449237

c

g

0.429

0.766

0.350

0.712

0.229

0.214

0.396

0.359

0.260

0.848

7223952

91

C_11621012

41103649

38448411

t

c

0.441

0.566

0.356

0.765

0.281

0.569

0.396

0.359

0.287

0.931

9911630

92

C_3178665

41097108

38441868

a

g

0.422

0.429

0.364

0.926

0.266

0.808

0.396

0.359

0.283

0.813

11460963

93

C_2615245

41090062

38435121

-

g

0.441

0.497

0.350

0.712

0.228

0.242

0.396

0.359

0.266

0.808

2298861

94

C_3178699

41085596

38430357

g

a

0.441

0.309

0.350

0.712

0.223

0.258

0.396

0.359

0.266

0.808

2298862

95

C_3178698

41085453

38430214

t

c

0.447

0.383

0.350

0.712

0.208

0.343

0.396

0.359

0.260

0.848

443759

96

C_2287905

41074499

38419260

c

t

0.229

0.129

0.241

0.656

0.354

0.520

0.083

0.208

0.146

0.981

11871636

97

C_11617231

41063582

38408343

a

c

0.235

0.304

0.259

0.386

0.302

0.345

0.167

0.083

0.135

0.278

2271539

98

C_1588447

41059714

38404475

a

g

0.388

0.932

0.388

0.339

0.213

0.447

0.448

0.341

0.277

0.768

690971

99

C_765227

41025330

38370091

g

t

    

0.281

0.885

    

528854

100

C_1588417

41006873

38351634

a

g

0.106

0.230

0.098

0.488

0.479

0.990

  

0.063

0.644

323495

101

C_1588405

40983252

38328013

g

a

0.316

0.182

0.342

0.568

0.208

0.001

0.396

0.135

0.443

0.407

2593595

102

C_3256885

40965010

38309771

a

g

0.124

0.007

0.054

0.672

0.156

0.002

0.135

0.883

0.177

<.0001

324075

103

C_3256881

40935288

38280049

a

g

0.171

0.717

0.195

0.063

0.115

0.370

0.156

0.365

0.330

0.558

2290041

104

C_15883310

40856085

38200846

c

t

0.012

0.913

  

0.302

0.103

0.031

0.823

  

1078523

105

C_2160077

40821525

38166286

a

g

0.480

0.158

0.370

0.650

0.135

0.883

0.478

0.369

0.474

0.075

752313

106

C_1075621

40810589

38155350

t

c

0.363

0.314

0.422

0.851

0.292

0.954

0.406

0.250

0.351

0.251

7359598

107

C_3256867

40806234

38150996

c

t

0.386

0.073

0.440

0.674

0.135

0.883

0.438

0.486

0.383

0.243

2271027

108

C_15959277

40779567

38124328

c

t

    

0.065

0.636

0.031

0.823

  

7214055

109

C_1441435

40761936

38106697

c

g

0.149

0.109

0.008

0.948

0.385

0.003

0.031

0.823

0.021

0.882

9766

110

C_1441436

40761606

38106367

g

a

0.353

0.219

0.433

0.700

0.292

0.954

0.406

0.250

0.344

0.823

1553469

111

C_7529639

40751527

38096288

a

c

0.065

0.248

0.058

0.061

0.031

<.0001

  

0.202

0.943

2271029

112

C_1125369

40744687

38089448

c

a

0.405

0.009

0.350

0.444

0.375

0.441

0.385

0.937

0.402

0.117

3760384

113

C_1441438

40744216

38088977

a

c

0.359

0.020

0.392

0.666

0.447

0.716

0.385

0.937

0.458

0.023

2292749

114

C_1441444

40727349

38072110

c

t

0.217

0.219

0.383

0.518

0.192

0.794

0.375

0.878

0.128

0.316

Number of polymorphic loci

83

 

80

 

99

 

71

 

82

 

Number of SNPs with maf<0.05

11

 

13

 

14

 

6

 

17

 

Mean maf

0.305

 

0.269

 

0.181

 

0.327

 

0.287

 

a April 2003 (Build 33) Position

b March 2006 (Build 36.1) Position

c Com Allele = Common allele

d Min Allele = Minor allele

e maf = Minor allele frequency

f Population private SNPs are shown underlined

gNA= Not Applicable – not in dbSNP

LD structure

In order to analyze the LD structure at the BRCA1 locus, we chose two methods that rely on different premises. The first is haplotype block analysis which identifies sequential and non-overlapping sets of variants in high LD, separated by low levels of LD that are consistent with historical recombination. In this method, all htSNPs need to be genotyped in order to capture most of the genetic variation [27]. The second is a binning method in which SNPs in one LD bin can be interleaved with SNPs in other overlapping bins. Under this approach, one TagSNP per bin needs to be tested in order to capture SNP diversity [28].

Our analyses of D' and r2 showed BRCA1 residing in a large region (~288 kb) of high LD (Fig. 1), in agreement with other reports [2931]. The entire region studied showed long-range LD, falling primarily into three blocks among non-African populations. The block containing BRCA1 includes 95 SNPs and overlaps the largest LDSelect bin of SNPs correlated at r2 > 0.8 (Fig. 2 and Fig. 3).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2156-8-68/MediaObjects/12863_2006_Article_547_Fig1_HTML.jpg
Figure 1

Comparison of haplotype blocks at 114 loci across five populations. Blocks were defined as in [27]; markers with MAF <0.05 are shown with a white background and were ignored in the calculations and block boundary estimation. Haplotype tag SNPs (htSNPs) within a block are indicated by arrowheads; htSNPs in only one population are shown on a yellow background while the single htSNP shared between all populations is shown on a green background.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2156-8-68/MediaObjects/12863_2006_Article_547_Fig2_HTML.jpg
Figure 2

Comparison of SNP bins derived from pair-wise measurements of linkage disequilibrium using LDSelect-Comp. SNPs with MAF < 5% do not have a vertical line or arrowhead in the column. A) Scale representation of the ~650 kb region studied, indicating the BRCA1 gene, founder mutations, and genome sequence gap of unknown true size. Anchor lines link to position of the SNP within the region. B-F) LDSelect creates bins of SNPs that have an r2 value of 0.8 or greater with at least one other SNP in the bin. Each vertical line and arrowhead represents a SNP, with dashed lines and shaded background connecting SNPs within the same bin. Down arrowheads indicate Tag SNPs (those with r2 ≥ 0.8 with all other SNPs in a bin). Note that this use of the term Tag-SNP is different from Haploview – with LDSelect, only one Tag-SNP per bin would be required to capture the majority of the nucleotide diversity. Singleton bins (SNPs that did not have r2 ≥ 0.8 with any other SNP) are indicated by solid dots on a single row. SNP number refers to numbering in column 1 of Table 1.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2156-8-68/MediaObjects/12863_2006_Article_547_Fig3_HTML.jpg
Figure 3

Pair-wise measures of linkage disequilibrium and the two founder mutation-containing haplotypes. SNP number refers to numbering in column 1 of Table 1; only the 70 with MAF ≥ 0.05 in Ashkenazi Jews are shown in B-D. A) Scale representation of the ~650 kb region studied, indicating the BRCA1 gene, founder mutations, and genome sequence gap of unknown true size. B) LDSelect-Comp output showing a total of 22 bins for Ashkenazi Jews, with 17 "singleton" bins indicated by solid dots on a single row. C) Haploview output showing three block structures and related ht-SNPs (indicated with up arrowheads). D) Haplotypes estimated for 85 unrelated Ashkenazi Jews using SNPHAP as implemented in HapScope. The block boundaries were calculated in Haploview and overlaid on this figure. All haplotypes with an estimated frequency of at least 1% are displayed (h1 to h11), with individual frequencies and sums indicated to the right of the blocks. The common allele is designated "1" and the minor allele "2". The 185delAG and 5382insC containing haplotypes, determined from the family based genotypes, are indicated with gray (haplotype 2) and blue background (haplotype 1), respectively. Black arrows indicate the relative position of these two founder mutations.

African-Americans presented the least LD of all populations, with the presence of more distinct blocks within the region (Fig. 1). Maps for all five populations shared a break-point that maps approximately 20 kb downstream of the BRCA1 gene, between SNP95 (rs2298862) and SNP96 (rs443759). Among non-African groups, only Mexican-Americans exhibited an additional break point within the 288 kb block structure that encompasses BRCA1. The 3'end of the entire region showed less extensive LD but a similar pattern across all the groups. Only one htSNP (SNP3 – rs17599948) was found to be completely shared across populations, which is not unexpected since htSNPs often are population-specific [28] (Fig. 1).

When the bin-based approach was used, we found that bins were largely shared across different ethnic groups (Fig. 2). The differences across populations were related to the number of bins as well as the number and position of TagSNPs. As expected, African-Americans were the most diverse group, containing the highest number of bins (34), followed by Ashkenazi Jews (22), CEPH (19), Mexican-Americans and Chinese-American (14). This contrasts with 28 htSNPs in African-Americans, and 16, 13, 25 and 18 among Ashkenazi Jews, CEPH, Mexican- Americans and Chinese-Americans, respectively. Three TagSNPs were shared by all populations (SNP3 – rs17599948, SNP41 – rs8176166, and SNP67 – rs3092994), showing average MAF of 0.193, 0.183 and 0.335, respectively (Fig. 2). Mexican-Americans showed two disjoint bins of highly-correlated SNPs, rather than one extended bin structure as evidenced in Ashkenazim, CEPH and Chinese-Americans. The disjoint occurred between positions 38,471,400 (SNP65 – rs8176242) and 38,469,351 (SNP67 – rs3092994), mapping between introns 17 and 18 of BRCA1 (Fig. 2F). Interestingly, our results resemble what others have observed [32] in Native- Americans, namely an historical recombination event between introns 15 and 18. All five populations showed a large bin spanning ~288 kb encompassing SNP1 (rs13119) through SNP95 (rs2298862) (Fig. 2A–F), which represented the same extended region found in the block analysis. This large bin had 0.278 average MAF across populations and included BRCA1 coding polymorphisms L771L_(TTG>CTG), P871L_(CCG>CTG), 1183R_(AAA>AGA) and S1436S_(TCT>TCC) (Fig. 2).

The maps of linkage disequilibrium in LD units (LDU) corresponded well with the two previous approaches of assessing disequilibrium. The four major breakpoints that were observed in Fig. 1 and 2, when haplotype blocks and bin structures were inferred, coincided with the same major steps in the LDU analysis (Fig. 4). In addition, we were able to observe two small steps in Fig. 4 for the Mexican-Americans, which were not observed in any other population. The first step occurred between SNP65 (rs8176242, intron 17) and SNP69 (rs8176265, intron 19). The corresponding site of possible recombination could be observed as a split at the main bin structure for Mexican-Americans in Fig. 2F. The second step was found between SNP90 (rs8176323) and SNP91 (rs7223952), downstream of the gene. A close correspondence was evidenced as a breakdown in LD around the same position in Fig. 1.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2156-8-68/MediaObjects/12863_2006_Article_547_Fig4_HTML.jpg
Figure 4

LDU maps. Comparison of LDU maps across the ~650 kb region containing the BRCA1 gene for 5 populations. Top. Scale representation of the ~650 kb region studied, indicating the BRCA1 gene, founder mutations, and genome sequence gap of unknown true size. Bottom. LDU maps for five populations.

185delAG and 5382insC haplotype reconstruction

We were particularly interested in fine mapping the BRCA1 locus to identify possible gene variants or haplotypes associated with the two founder mutations in Ashkenazi Jews. Therefore, 82 intragenic and 30 flanking SNPs were tested against the two founder mutations with the Pearson's correlation (r) coefficient (Table 2). Although in strong LD with the majority of markers as measured by D' (Table 2), the highest pair-wise r2 for 185delAG was 0.04, owing to its relatively low frequency, with a common SNP (SNP96, rs443759) that mapped outside the large BRCA1-containing block, approximately 110 kb downstream of the gene. Regarding 5382insC, there was one highly-significant association (r2 = 1.0) with SNP8 (rs8176072) (Table 2). This is a rare SNP that was present only in 5382insC mutation carriers, and 5382insC was not correlated above 0.03 with any other SNP in the region.
Table 2

Pair-wise correlation coefficients between the two founder BRCA1 mutations and all other SNPs among Ashkenazi Jews

    

Ashkenazim (n = 85)

Correlation (r 2) with

D' with

D' with

SNP name

rs number

SNP number

SNP description

mafa

Hetb

HW P-value

185delAG

5382insC

185delAG

5382insC

C_9270420

13119

1

NBR1: UTR

0.424

0.52

0.580

0.011

0.001

1.000

1.000

C_9270421

748620

2

NBR1: UTR

0.440

0.52

0.640

0.016

0.000

1.000

1.000

C_9270454

17599948

3

NBR1: intron

0.211

0.35

0.649

0.016

0.001

1.000

1.000

C_95201

11653231

4

NBR1: intron

0.435

0.51

0.703

0.014

0.001

1.000

1.000

C_3178692

9908805

5

LBRCA1: UTR

0.000

    

1.000

1.000

C_11631183

2175957

6

NBR2: intron

0.447

0.52

0.665

0.013

0.001

1.000

1.000

BR1_000340

3092986

7

NBR2: intron

0.051

0.10

0.636

0.001

0.000

1.000

1.000

BR1_000392

8176072

8

NBR2:intron

0.006 c

0.01

0.957

0.000

1.000

1.000

1.000

BR1_000571

8176074

9

NBR2:UTR

0.000

    

1.000

1.000

C_2615164

3765640

10

BRCA1:UTR

0.440

0.52

0.640

0.015

0.000

1.000

1.000

M_185delAG

NAd

11

BRCA1:exon 2

0.006

0.01

0.957

NA

0.000

NA

1.000

BR1_007918

8176090

12

BRCA1: intron

0.000

    

1.000

1.000

BR1_010574

1800062

13

BRCA1: P_K38K_(AAG>AAA)

0.000

    

1.000

1.000

BR1_010796

8176101

14

BRCA1: intron

0.000

    

1.000

1.000

C_2615171

8176103

15

BRCA1: intron

0.463

0.56

0.291

0.014

0.000

1.000

1.000

BR1_011340

8176104

16

BRCA1: intron

0.024

0.05

0.824

0.001

0.000

1.000

1.000

C_2615172

8176109

17

BRCA1: intron

0.441

0.53

0.497

0.011

0.000

1.000

0.000

BR1_016775

8065872

18

BRCA1: intron

0.000

    

1.000

1.000

BR1_017105

8176120

19

BRCA1: intron

0.424

0.49

0.913

0.015

0.001

1.000

1.000

BR1_018573

799914

20

BRCA1: intron

0.000

    

1.000

1.000

BR1_019408

799913

21

BRCA1: intron

0.032

0.06

0.772

0.001

0.000

1.000

1.000

BR1_019904

8176128

22

BRCA1: intron

0.000

    

1.000

1.000

BR1_020896

8176133

23

BRCA1: intron

0.422

0.51

0.738

0.012

0.000

1.000

1.000

C_2615180

799912

24

BRCA1: intron

0.472

0.52

0.701

0.011

0.000

1.000

0.000

BR1_026422

799923

25

BRCA1: intron

0.232

0.44

0.031

0.003

0.000

1.000

1.000

BR1_029258

8176145

26

BRCA1: intron

0.400

0.59

0.038

0.021

0.001

1.000

0.000

BR1_029448

8176146

27

BRCA1: intron

0.031

0.06

0.774

0.001

0.000

1.000

1.000

BR1_030748

7503154

28

BRCA1: intron

0.415

0.51

0.618

0.012

0.001

1.000

1.000

BR1_031875

1799950

29

BRCA1: P_Q356R_(CAG>CGG)

0.077

0.15

0.442

0.002

0.000

1.000

1.000

BR1_032885

4986850

30

BRCA1: P_D693N_(GAC>AAC)

0.133

0.19

0.096

0.001

0.000

0.000

1.000

BR1_033119

16940

31

BRCA1: P_L771L_(TTG>CTG)

0.422

0.53

0.429

0.012

0.001

1.000

1.000

BR1_033420

799917

32

BRCA1:P_P871L_(CCG>CTG)

0.429

0.51

0.766

0.015

0.001

1.000

1.000

BR1_003927

4986852

33

BRCA1: P_S1040N_(AGC>AAC)

0.012

0.02

0.912

0.000

0.000

1.000

1.000

BR1_034226

2227945

34

BRCA1: P_S1140G_(AGT>GGT)

0.000

    

1.000

1.000

BR1_034356

16942

35

BRCA1: P_K1183R_(AAA>AGA)

0.433

0.50

0.868

0.015

0.001

1.000

1.000

C_7530109

799916

36

BRCA1: intron

0.438

0.53

0.482

0.015

0.000

1.000

1.000

BR1_035507

2070833

37

BRCA1: intron

0.031

0.04

0.001

0.001

0.000

1.000

1.000

BR1_036077

2070834

38

BRCA1: intron

0.435

0.52

0.626

0.014

0.001

1.000

1.000

BR1_036793

8176158

39

BRCA1: intron

0.418

0.54

0.393

0.012

0.000

1.000

1.000

BR1_036859

8176160

40

BRCA1: intron

0.435

0.52

0.626

0.014

0.001

1.000

1.000

BR1_038085

8176166

41

BRCA1: intron

0.196

0.35

0.391

0.018

0.000

0.388

1.000

BR1_040350

8176174

42

BRCA1: intron

0.000

    

1.000

1.000

C_3178696

3950989

43

BRCA1: intron

0.441

0.52

0.566

0.007

0.000

1.000

1.000

BR1_040669

8176175

44

BRCA1: intron

0.006

0.01

0.956

0.000

0.000

1.000

1.000

BR1_041288

8176177

45

BRCA1: intron

0.000

    

1.000

1.000

BR1_041721

8176178

46

BRCA1: intron

0.000

    

1.000

1.000

C_3178676

1060915

47

BRCA1: P_S1436S_(TCT>TCC)

0.380

0.56

0.105

0.018

0.001

1.000

0.000

C_3178677

3737559

48

BRCA1: intron

0.124

0.20

0.481

0.001

0.001

0.462

1.000

BR1_045154

8176187

49

BRCA1: intron

0.000

    

1.000

1.000

BR1_045505

8176188

50

BRCA1: intron

0.000

    

1.000

1.000

BR1_0046019

6416927

51

BRCA1: intron

0.000

    

1.000

1.000

BR1_047826

8176198

52

BRCA1: intron

0.422

0.53

0.429

0.016

0.001

1.000

1.000

BR1_0477839

8176199

53

BRCA1: intron

0.341

0.42

0.594

0.016

0.000

1.000

1.000

BR1_048551

4239147

54

BRCA1: intron

0.440

0.52

0.640

0.011

0.001

1.000

1.000

BR1_050244

8176206

55

BRCA1: intron

0.000

    

1.000

1.000

C_11621042

2236762

56

BRCA1: intron

0.435

0.52

0.626

0.016

0.001

1.000

1.000

C_2615208

1799966

57

BRCA1: intron

0.456

0.54

0.456

0.008

0.000

1.000

0.185

BR1_055669

3092987

58

BRCA1: intron

0.416

0.52

0.545

0.013

0.002

1.000

1.000

BR1_056796

8176225

59

BRCA1: intron

0.000

    

1.000

1.000

BR1_058369

8176232

60

BRCA1: intron

0.013

0.03

0.936

0.000

0.000

1.000

1.000

BR1_058614

8176234

61

BRCA1: intron

0.437

0.54

0.345

0.016

0.000

1.000

1.000

BR1_058834

8176235

62

BRCA1: intron

0.339

0.44

0.872

0.017

0.000

1.000

1.000

BR1_059589

8176236

63

BRCA1: intron

0.000

    

1.000

1.000

BR1_060022

8176240

64

BRCA1: intron

0.000

    

1.000

1.000

BR1_060520

8176242

65

BRCA1: intron

0.433

0.52

0.538

0.011

0.001

1.000

1.000

BR1_061014

8176245

66

BRCA1: intron

0.000

    

1.000

1.000

C_2615220

3092994

67

BRCA1: intron

0.434

0.53

0.471

0.012

0.000

1.000

0.000

BR1_062588

8176259

68

BRCA1: intron

0.000

    

1.000

1.000

BR1_0064398

8176265

69

BRCA1: intron

0.417

0.52

0.477

0.012

0.001

1.000

1.000

BR1_064501

2187603

70

BRCA1: intron

0.422

0.53

0.429

0.009

0.001

1.000

1.000

BR1_066741

8176273

71

BRCA1: intron

0.424

0.52

0.580

0.011

0.001

1.000

1.000

BR1_067978

8176278

72

BRCA1: intron

0.000

    

1.000

1.000

BR1_068063

8066171

73

BRCA1: intron

0.000

    

1.000

1.000

M_5382insC

NA

74

BRCA1:exon 20

0.006

0.01

0.957

0.000

NA

1.000

NA

C_2615230

8176289

75

BRCA1: intron

0.441

0.52

0.566

0.014

0.000

1.000

1.000

BR1_073023

8176293

76

BRCA1: intron

0.000

    

1.000

1.000

BR1_074008

4793192

77

BRCA1: intron

0.435

0.52

0.626

0.016

0.001

1.000

1.000

BR1_074807

8176296

78

BRCA1: intron

0.435

0.51

0.703

0.015

0.000

1.000

1.000

C_2615238

3092988

79

BRCA1: intron

0.424

0.52

0.580

0.015

0.001

1.000

1.000

BR1_076933

8176303

80

BRCA1: intron

0.000

    

1.000

1.000

BR1_077034

8176305

81

BRCA1: intron

0.094

0.16

0.753

0.002

0.001

1.000

1.000

BR1_077328

8176307

82

BRCA1: intron

0.000

    

1.000

1.000

BR1_079220

8068463

83

BRCA1: intron

0.000

    

1.000

1.000

BR1_079396

8176313

84

BRCA1: intron

0.006

0.01

0.957

0.000

0.000

1.000

1.000

BR1_080570

8176316

85

BRCA1: intron

0.000

    

1.000

1.000

BR1_081125

8176318

86

BRCA1: UTR

0.435

0.49

0.963

0.009

0.001

1.000

1.000

BR1_081990

12516

87

BRCA1: UTR

0.435

0.52

0.626

0.014

0.001

1.000

1.000

BR1_082035

8176320

88

BRCA1: UTR

0.006

0.01

0.957

0.000

0.000

1.000

1.000

BR1_082199

8176321

89

Intergenic

0.000

    

1.000

1.000

BR1_082687

8176323

90

Intergenic

0.429

0.51

0.766

0.015

0.001

1.000

1.000

C_11621012

7223952

91

Intergenic

0.441

0.52

0.566

0.014

0.000

1.000

1.000

C_3178665

9911630

92

Intergenic

0.422

0.53

0.429

0.016

0.001

1.000

1.000

C_2615245

11460963

93

Intergenic

0.441

0.53

0.497

0.014

0.001

1.000

1.000

C_3178699

2298861

94

ARHN: locus

0.441

0.55

0.309

0.011

0.001

0.000

1.000

C_3178698

2298862

95

ARHN: locus

0.447

0.54

0.383

0.011

0.001

1.000

1.000

C_2287905

443759

96

IFI35:intron

0.229

0.41

0.129

0.040

0.002

1.000

1.000

C_11617231

11871636

97

RPL27: intron

0.235

0.40

0.304

0.036

0.002

1.000

1.000

C_1588447

2271539

98

RPL27: intron

0.388

0.47

0.932

0.023

0.003

1.000

1.000

C_765227

690971

99

MGC2744: intron

0.000

    

1.000

1.000

C_1588417

528854

100

MGC2744: intergenic

0.106

0.16

0.230

0.000

0.001

1.000

1.000

C_1588405

323495

101

G6PC: intergenic

0.316

0.37

0.182

0.004

0.000

0.205

1.000

C_3256885

2593595

102

G6PC: intron

0.124

0.15

0.007

0.000

0.001

1.000

1.000

C_3256881

324075

103

Intergenic

0.171

0.29

0.717

0.000

0.000

0.266

1.000

C_15883310

2290041

104

PRKWNK4: mis-sense

0.012

0.02

0.913

0.005

0.000

1.000

1.000

C_2160077

4321242

105

RAMP2: intron

0.480

0.58

0.158

0.003

0.000

1.000

1.000

C_1075621

752313

106

EZH1: intergenic

0.363

0.40

0.314

0.005

0.000

1.000

1.000

C_3256867

7359598

107

EZH1: intergenic

0.386

0.57

0.073

0.005

0.001

1.000

0.000

C_15959277

2271027

108

EZH1: intron

0.000

    

1.000

1.000

C_1441435

7214055

109

CNTNAP1: UTR

0.149

0.30

0.109

0.001

0.001

1.000

1.000

C_1441436

9766

110

CNTNAP1: UTR

0.353

0.52

0.219

0.007

0.000

0.173

1.000

C_7529639

1553469

111

CNTNAP1: silent

0.065

0.11

0.248

0.002

0.000

0.463

1.000

C_1125369

2271029

112

CNTNAP1: silent

0.405

0.62

0.009

0.000

0.001

0.040

1.000

C_1441438

3760384

113

CNTNAP1: intron

0.359

0.58

0.020

0.005

0.000

1.000

1.000

C_1441444

2292749

114

TUBG2: intron

0.217

0.39

0.219

0.006

0.001

1.000

1.000

a maf = Minor allele frequency

bHet = Heterozygosity

cPopulation private SNPs are shown underlined

dNA= Not Applicable

Haplotypes were estimated for the set of all SNPs with MAF ≥ 0.05. Haplotypes for the founder mutation containing chromosomes were unambiguously determined in at least one family across the entire region studied. The185delAG and 5382insC mutations occurred on the two most common haplotypes, representing 15% (haplotype 2) and 29% (haplotype 1) of the chromosomes, respectively, among Ashkenazi Jews (Fig. 3D). In the haplotype analyses, the 185delAG mutation occurred on a chromosome with the minor allele at most loci, and the 5382insC on a chromosome with the major allele at most loci (Fig. 3D). This pattern constitutes what has been previously described as "yin yang haplotypes", in which two high-frequency haplotypes have different alleles at most SNP sites [33].

Discussion

The primary objective of this study was to address the question of whether we could identify the Ashkenazi BRCA1 founder mutation 185delAG in a typical case-control association study, using anonymous genetic markers. The answer is no. The impact on the required sample size (S) needed if one studies a marker in LD with the true disease allele is related to the inverse of the square of their correlation coefficient (r), as in S = 1/r2 [34]. Almost none of the SNPs had a high correlation coefficient with either founder mutation (Table 2). Since most markers were more common than the founder mutations, this result is not surprising. However, our SNP selection strategy did not exclude low frequency SNPs. In fact, one of the SNPs identified in 3 of the 90 Polymorphism Discovery Resource subjects [35] was perfectly correlated with 5382insC. However, we did not observe this SNP in any of the four non-Ashkenazi reference populations. Based on our results, the sample size required to detect the 185delAG mutation in a breast cancer case-control study conducted in Ashkenazi women that did not directly test for the mutation, would be at least 25 times larger than one that measured the mutation directly, requiring on the order of 62,000 subjects. Based on pair-wise measurements, we conclude that it would have been extremely difficult to have mapped the two founder mutations using the case-control association methodology using common SNPs.

Association studies may also compare combinations of SNPs, or haplotypes, between cases and controls, and the founder mutations might have been discoverable if they occurred on uncommon haplotypes. Using relatively common SNPs (MAF ≥ 5%), like those on whole-genome SNP platforms, we found that the two mutations were present on haplotypes representing a polar pattern, termed yin-yang haplotypes [33]. These two haplotypes accounted together for a large percentage of the total chromosomes studied, independent of the population, ranging from 64% for the Chinese-Americans to 43% for the CEPH.

It is highly unlikely that the founder mutations could have been discovered owing to a difference in haplotype frequency between cases and controls largely because they occur on the two most common haplotypes. For example, consider a case-control study of Ashkenazi Jews with 500 cases and 500 controls. Among controls (1000 chromosomes), the distribution of BRCA1-containing haplotypes would be roughly as in Fig. 3D (i.e., there would have been 288 chromosomes with haplotype 1 and 153 with haplotype 2). Among cases, assuming 1% carried 5382insC and 4% carried 185delAG, there would be 5 additional haplotype 1 (total = 293) and 20 additional haplotype 2 (total = 173) chromosomes. These case-control contrasts, 293 vs. 288 (OR 1.02) and 173 vs. 153, would require extremely large sample sizes of over 30,000 subjects to detect either mutation with 80% statistical power. Conversely, in the more advantageous situation in which the 185delAG mutation by chance occurred on a rare haplotype (for example, haplotype 8), there would have been 32 such chromosomes in cases vs. 12 in controls requiring approximately 4,800 subjects for the same statistical power.

The BRCA1 locus is well known to have significant LD [29, 30]. Nonetheless, we found a marked differentiation between African-Americans and non-African Americans in the haplotype block analysis. Compared with African-Americans, the non-African American populations had less haplotype diversity and more extensive LD (Fig. 1). The increased number of crossovers along the entire region for African-Americans probably reflects older evolutionary events. Our data conform to previous findings [27], describing higher haplotype diversity as well as less extensive LD in the Yoruban and African American samples than in European and Asian populations. When SNP "bins" derived from pair-wise measurements of LD were compared, we found a greater extent of LD boundaries being shared across the five different ethnical groups (Fig. 2). Ashkenazi Jews and the CEPH population had highly similar patterns of LD, independently of the type of analysis used to generate the LD structures (haplotype, or pair-wise bin methods) (Fig. 1 and Fig. 2). Overall, haplotype blocks and bins showed similar patterns, probably owing to the strong LD present overall in this region. The LDU analysis showed a remarkable overall similarity with the two previous methods that were used to analyze LD (Fig. 4). There were basically four major breakdowns in LD downstream to BRCA1 that were largely shared across populations. Nevertheless, African-Americans presented more recombination events than the other four populations, consistent with the smaller block sizes showed in Fig. 1.

Conclusion

In summary, our detailed analyses of 114 polymorphic SNPs in a 646 kb region around BRCA1 in Ashkenazi Jews and other populations confirmed a high level of linkage disequilibrium across nearly the entire region. In addition to 85 unrelated Ashkenazi Jews, we over-sampled carriers of the founder mutations 185delAG and 5382insC and their relatives to more precisely calculate correlations with other markers and to molecularly determine the mutation associated haplotypes (these subjects were not included in allele frequency estimates). This allowed us to assess the likelihood of discovering the founder mutations by virtue of their association with individual SNPs or haplotypes that one would assay in a breast cancer case-control study in Ashkenazi Jews. We did not observe a high correlation coefficient between any individual SNP likely to be included in a genome-wide anonymous scan and either founder mutation. Our findings suggest that a study at least 25X larger (60,000 subjects or more) would have been required if the mutations were not tested for directly. The two founder mutations occur on the two most common haplotypes, representing over 40% of the chromosomes, also suggesting that a haplotype-based analysis would not have been successful at detecting either of the underlying mutations. These results are influenced heavily by the relative rarity of the founder mutations, as reflected by high values for Lewontin's D' measures of LD but low correlation coefficients. Our results suggest caution in using genome-wide association studies with common SNPs for detecting uncommon, disease-causing mutations.

Methods

Subjects

Independent subjects included 85 unrelated Ashkenazi Jews, 60 European-Americans (Utah) from the CEPH (The Centre d'Etude du Polymorphisme Humain) family collection, and 48 each from African-Americans, Chinese-Americans and Mexican-Americans (Human Diversity Collection, Coriell Cell Repository, Camden, NJ). The 30 children of the 60 Utah CEPH subjects were also assayed to test for Mendelian errors. In addition, six unrelated BRCA1:185delAG and three unrelated BRCA1:5382insC founder mutation carriers and their relatives [36], identified through the National Cancer Institute's Cancer Family Registry, were included in the study in order to establish mutation-associated haplotypes from family data. Mutation-associated haplotypes were inferred through inspection of genotypes for all available first-degree relatives of mutation carriers. The Ashkenazi Jewish samples were obtained from anonymous control subjects from the National Laboratory for the Genetics of Israeli Populations at Tel-Aviv University [37].

Marker selection and genotyping

The 90 kb BRCA1 locus was previously re-sequenced in 90 individuals representing five major US ethnic/population groups from the Polymorphism Discovery Resource (PDR-90) [35], by the University of Washington as part of the Environmental Genome Project (EGP) [38]. Samples consisted of 24 European-, 24 African- 24 Asian-, 12 Mexican-, and six Native-Americans. The geographic origin of individual donors, however, is masked and may not be used to assign allele frequencies to specific sub-populations. Most of the 301 variants identified were SNPs. Genotyping all 301 variants at this locus in the current study was not necessary since many are highly correlated. We developed the following strategy to identify a reduced set of variants that still captured much of the diversity of the region. Using the EGP data on all 299 biallelic single nucleotide substitutions (i.e., no lower minor allele frequency cutoff), and using custom software, we calculated all pair-wise correlations (r2) and created "clusters", defined as groups of SNPs that were perfectly correlated with all others in the cluster. Our method is similar to LDSelect 1.0 [28] except that it required that all pair-wise correlations of SNPs in a cluster be 1.0 (i.e., complete LD). LDSelect is typically used with a threshold value of r2 of 0.8, and SNPs are clustered into "LD bins" if their pair-wise r2 is at or above this threshold value with at least one other SNP (but not all) in the bin. Using an r2 of 1.0 resulted in more clusters than a lower r2 threshold, increasing the number of SNPs assayed in this study.

Taqman 5'-nuclease assays were developed through Applied Biosystems (Foster City, CA) Assay-by-Design service after first filtering for repetitive, non-unique, and low-complexity sequence. We developed assays for all "singleton" SNPs (those that did not have pair-wise r2 values of 1.0 with another SNP). For the 59 clusters with two or more SNPs, we chose one SNP from each cluster of two, three and four SNPs, and for 9 clusters of five or more SNPs, we chose one fourth of them for assay development. In addition, we selected all (n = 43) commercially available Assay-on-Demand assays (Applied Biosystems, Foster City, CA) that mapped within approximately 200 kb upstream and 400 kb downstream of the BRCA1 locus. This SNP set represented almost all known variants (or ones highly correlated) at this locus.

Of the 143 resulting assays, three were excluded due to technical problems (poor clustering or more than one Mendelian error), and 28 were not polymorphic in our complete sample set, leaving 112 polymorphic SNPs in addition to the two founder mutations. There were 82 BRCA1 intragenic SNPs (approximately one SNP per 1 kb) and 30 SNPs that mapped to the region outside BRCA1 (approximately one SNP per 20 kb). The allelic discrimination assays were performed in 5 microliter reactions in 384-well plates according to manufacturer's recommendations. Data were analyzed with the allelic discrimination SDS 2.1 software on an ABI 7900HT (Applied Biosystems, Foster City, CA), with manual determination of genotype clusters [see Additional file 2].

Statistical analysis

Allelic frequency and chi-square goodness-of-fit tests for Hardy-Weinberg equilibrium (HWE) were calculated using SAS/Genetics 9.1 (SAS Institute, Inc., Cary, North Carolina). To assess the correlation between the two founder mutations and all other SNPs, we over-sampled mutation carriers and calculated a weighted Pearson's correlation coefficient using SAS 9.1. We also tested association by use of Tagger [39] operates in either pairwise or aggressive mode, and we used both approaches to examine association. Heterozygosity levels, as well as the variation in gene frequencies between populations by means of their FST (Wright's F-statistics) were calculated using POPGENE 1.31 [40].

Haplotypes and their frequencies were inferred from genotypes across the entire region for each population separately, using the software package SNPHAP 1.3 [41], as implemented in Hapscope [42], for loci with minor allele frequencies (MAF) > 5%. SNPHAP uses the expectation-maximization algorithm to calculate maximum likelihood estimates of haplotype frequencies from unphased genotype data.

In order to compare LD patterns across different populations, we employed two different analyses, the first based on partitioning SNPs into haplotype blocks [27] using Haploview [43] and the second based on "bins" of correlated SNPs not constrained to be adjacent to each other [28]. The binning method used a modified version of LDSelect 1.0 that calculates composite LD measures [44], without assuming that loci are in Hardy-Weinberg equilibrium. We used an r2 threshold of 0.8 for binning SNPs, and filtered SNPs with population-specific MAF ≤ 0.05.

LDSelect identifies tagSNPs, representing those SNPs in a bin that have r2 values at or above the threshold with all other SNPs in a bin. Only one tagSNP in each bin needs to be assayed to capture the majority of the SNP diversity. The block method employed by Haploview groups adjacent SNPs in strong LD, defined as those with one-sided upper 95% confidence bound on D' >0.98 and the lower bound >0.7. In this method, haplotype tag SNPs (htSNPs) represents the set of SNPs that must be assayed in each block to capture all haplotypes at 1% frequency in the population.

LD maps were constructed from genotype data with the software LDMAP [45]. LD maps are scaled in linkage disequilibrium units (LDU) and show (when plotted against the physical map) a pattern of plateaus (reflecting regions of low haplotype diversity and low recombination) and steps (representing regions of historical recombination events).

We genotyped related individuals from families segregating 185delAG and 5382insC founder mutations in order to reconstruct their haplotypes. The 185delAG- and 5382insC-containing haplotypes were unambiguously determined from analyzing the genotypes of all available family members. The frequencies of these mutation-containing haplotypes were determined from SNPHAP analyses of the five populations separately. Block boundaries were defined based on Haploview analyses and overlaid upon the SNPHAP results.

We estimated the required number of subjects to have 80% statistical power to identify the 185delAG mutation if tested directly in a case-control study to be approximately 2492 using EpiInfo 4.0 [46], assuming equal numbers of cases and controls, alpha of 0.0001, and heterozygous carrier frequencies of 0.6% for controls and 3.3% for cases.

Declarations

Acknowledgements

This research was supported in part by the Intramural Research Program of the NIH, Center for Cancer Research and the Division of Cancer Epidemiology and Genetics, National Cancer Institute, U.S. Department of Health and Human Services.

Authors’ Affiliations

(1)
Laboratory of Population Genetics, National Cancer Institute
(2)
Department of Biological Sciences, Florida International University, University Park
(3)
Clinical Genetics Branch, National Cancer Institute
(4)
Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center
(5)
Gastroenterology Section, Department of Medicine, University of Chicago
(6)
Human Genetics Division, University of Southampton, Southampton General Hospital

References

  1. Miki Y, Swensen J, Shattuck-Eidens D, Futreal PA, Harshman K, Tavtigian S, Liu Q, Cochran C, Bennett LM, Ding W, Bell R, Rosenthal J, Hussey C, Tran T, McClure M, Frye C, Hattier T, Phrlps R, Haugen-Strano A, Katcher H, Yakumo K, Gholami Z, Shaffer D, Stone D, Bayer S, Wray C, Bogden R, Dayanath P, Ward J, Tonin P, et al: A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science. 1994, 266 (5182): 66-71. 10.1126/science.7545954.View ArticlePubMedGoogle Scholar
  2. Wooster R, Bignell G, Lancaster J, Swift S, Seal S, Mangion J, Collins N, Gregory S, Gumbs C, Micklem G: Identification of the breast cancer susceptibility gene BRCA2. Nature. 1995, 378 (6559): 789-792. 10.1038/378789a0.View ArticlePubMedGoogle Scholar
  3. Szabo CI, King MC: Population genetics of BRCA1 and BRCA2. Am J Hum Genet. 1997, 60 (5): 1013-1020.PubMed CentralPubMedGoogle Scholar
  4. Tonin P, Weber B, Offit K, Couch F, Rebbeck TR, Neuhausen S, Godwin AK, Daly M, Wagner-Costalos J, Berman D, Grana G, Fox E, Kane MF, Kolodner RD, Krainer M, Haber DA, Struewing JP, Warner E, Rosen B, Lerman C, Peshkin B, Norton L, Serova O, Foulkes WD, Lynch HT, Lenoir GM, Narod SA, Garber JE: Frequency of recurrent BRCA1 and BRCA2 mutations in Ashkenazi Jewish breast cancer families. Nat Med. 1996, 2 (11): 1179-1183. 10.1038/nm1196-1179.View ArticlePubMedGoogle Scholar
  5. Antoniou A, Pharoah PD, Narod S, Risch HA, Eyfjord JE, Hopper JL, Loman N, Olsson H, Johannsson O, Borg A, Pasini B, Radice P, Manoukian S, Eccles DM, Tang N, Olah E, Anton-Culver H, Warner E, Lubinski J, Gronwald J, Gorski B, Tulinius H, Thorlacius S, Eerola H, Nevanlinna H, Syrjakoski K, Kallioniemi OP, Thompson D, Evans C, Peto J, Lalloo F, Evans DG, Easton DF: Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case Series unselected for family history: a combined analysis of 22 studies. Am J Hum Genet. 2003, 72 (5): 1117-1130. 10.1086/375033.PubMed CentralView ArticlePubMedGoogle Scholar
  6. Easton D, McGuffog L, Thompson D, Dunning A, Tee L, Baynes C, Healey C, Pharoah P, Ponder B, Seal S, Barfoot R, Sodha N, Eeles R, Stratton M, Rahman N, Peto J, Spurdle AB, Chen X, Chenevix-Trench G, Hopper JL, Giles GG, McCredie MRE, Holli KSK, Kallioniemi O, Eerola H, Vahteristo P, Blomqvist C, Nevanlinna H, Kataja V, Mannermaa A, et al: CHEK2*1100delC and susceptibility to breast cancer: a collaborative analysis involving 10,860 breast cancer cases and 9,065 controls from 10 studies. Am J Hum Genet. 2004, 74 (6): 1175-1182. 10.1086/421251.View ArticleGoogle Scholar
  7. Renwick A, Thompson D, Seal S, Kelly P, Chagtai T, Ahmed M, North B, Jayatilake H, Barfoot R, Spanova K, McGuffog L, Evans DG, Eccles D, Easton DF, Stratton MR, Rahman N: ATM mutations that cause ataxia-telangiectasia are breast cancer susceptibility alleles. Nat Genet. 2006, 38 (8): 873-875. 10.1038/ng1837.View ArticlePubMedGoogle Scholar
  8. Seal S, Thompson D, Renwick A, Elliott A, Kelly P, Barfoot R, Chagtai T, Jayatilake H, Ahmed M, Spanova K, North B, McGuffog L, Evans DG, Eccles D, Easton DF, Stratton MR, Rahman N: Truncating mutations in the Fanconi anemia J gene BRIP1 are low-penetrance breast cancer susceptibility alleles. Nat Genet. 2006, 38 (11): 1239-1241. 10.1038/ng1902.View ArticlePubMedGoogle Scholar
  9. Rahman N, Seal S, Thompson D, Kelly P, Renwick A, Elliott A, Reid S, Spanova K, Barfoot R, Chagtai T, Jayatilake H, McGuffog L, Hanks S, Evans DG, Eccles D, Easton DF, Stratton MR: PALB2, which encodes a BRCA2-interacting protein, is a breast cancer susceptibility gene. Nat Genet. 2007, 39 (2): 165-167. 10.1038/ng1959.PubMed CentralView ArticlePubMedGoogle Scholar
  10. Cox A, Dunning AM, Garcia-Closas M, Balasubramanian S, Reed MW, Pooley KA, Scollen S, Baynes C, Ponder BA, Chanock S, Lissowska J, Brinton L, Peplonska B, Southey MC, Hopper JL, McCredie MR, Giles GG, Fletcher O, Johnson N, dos Santos Silva I, Gibson L, Bojesen SE, Nordestgaard BG, Axelsson CK, Torres D, Hamann U, Justenhoven C, Brauch H, Chang-Claude J, Kropp S, et al: A common coding variant in CASP8 is associated with breast cancer risk. Nat Genet. 2007, 39 (3): 352-358. 10.1038/ng1981.View ArticlePubMedGoogle Scholar
  11. Hemminki K, Granstrom C, Czene K: Attributable risks for familial breast cancer by proband status and morphology: a nationwide epidemiologic study from Sweden. Int J Cancer. 2002, 100 (2): 214-219. 10.1002/ijc.10467.View ArticlePubMedGoogle Scholar
  12. Hemminki K, Czene K: Attributable risks of familial cancer from the Family-Cancer Database. Cancer Epidemiol Biomarkers Prev. 2002, 11 (12): 1638-1644.PubMedGoogle Scholar
  13. Lichtenstein P, Holm NV, Verkasalo PK, Iliadou A, Kaprio J, Koskenvuo M, Pukkala E, Skytthe A, Hemminki K: Environmental and heritable factors in the causation of cancer – analyses of cohorts of twins from Sweden, Denmark, and Finland. N Engl J Med. 2000, 343 (2): 78-85. 10.1056/NEJM200007133430201.View ArticlePubMedGoogle Scholar
  14. Risch N: The genetic epidemiology of cancer: Interpreting family and twin studies and their implications for molecular genetic approaches. Cancer Epidemiol Biomarkers Prev. 2001, 10 (7): 733-741.PubMedGoogle Scholar
  15. Peto J, Collins N, Barfoot R, Seal S, Warren W, Rahman N, Easton DF, Evans C, Deacon J, Stratton MR: Prevalence of BRCA1 and BRCA2 gene mutations in patients with early-onset breast cancer. J Natl Cancer Inst. 1999, 91 (11): 943-949. 10.1093/jnci/91.11.943.View ArticlePubMedGoogle Scholar
  16. Antoniou AC, Easton DF: Polygenic inheritance of breast cancer: Implications for design of association studies. Genet Epidemiol. 2003, 25 (3): 190-202. 10.1002/gepi.10261.View ArticlePubMedGoogle Scholar
  17. Altshuler D, Brooks LD, Chakravarti A, Collins FS, Daly MJ, Donnelly P: A haplotype map of the human genome. Nature. 2005, 437 (7063): 1299-1320. 10.1038/nature04226.View ArticleGoogle Scholar
  18. Risch N, Merikangas K: The future of genetic studies of complex human diseases. Science. 1996, 273 (5281): 1516-1517. 10.1126/science.273.5281.1516.View ArticlePubMedGoogle Scholar
  19. Pharoah PD, Dunning AM, Ponder BA, Easton DF: Association studies for finding cancer-susceptibility genetic variants. Nat Rev Cancer. 2004, 4 (11): 850-860. 10.1038/nrc1476.View ArticlePubMedGoogle Scholar
  20. Hirschhorn JN, Daly MJ: Genome-wide association studies for common diseases and complex traits. Nat Rev Genet. 2005, 6 (2): 95-108. 10.1038/nrg1521.View ArticlePubMedGoogle Scholar
  21. Easton DF, Pooley KA, Dunning AM, Pharoah PD, Thompson D, Ballinger DG, Struewing JP, Morrison J, Field H, Luben R, Wareham N, Ahmed S, Healey CS, Bowman R, Meyer KB, Haiman CA, Kolonel LK, Henderson BE, Le Marchand L, Brennan P, Sangrajrang S, Gaborieau V, Odefrey F, Shen CY, Wu PE, Wang HC, Eccles D, Evans DG, Peto J, Fletcher O, et al: Genome-wide association study identifies novel breast cancer susceptibility loci. Nature. 2007, 447 (7148): 1087-1093. 10.1038/nature05887.PubMed CentralView ArticlePubMedGoogle Scholar
  22. The Wellcome Trust Case Control Consortium: Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007, 447 (7145): 661-678. 10.1038/nature05911.PubMed CentralView ArticleGoogle Scholar
  23. Pharoah PD, Antoniou A, Bobrow M, Zimmern RL, Easton DF, Ponder BA: Polygenic susceptibility to breast cancer and implications for prevention. Nat Genet. 2002, 31 (1): 33-36. 10.1038/ng853.View ArticlePubMedGoogle Scholar
  24. Antoniou AC, Pharoah PD, McMullan G, Day NE, Stratton MR, Peto J, Ponder BJ, Easton DF: A comprehensive model for familial breast cancer incorporating BRCA1, BRCA2 and other genes. Br J Cancer. 2002, 86 (1): 76-83. 10.1038/sj.bjc.6600008.PubMed CentralView ArticlePubMedGoogle Scholar
  25. Struewing JP, Brody LC, Erdos MR, Kase RG, Giambarresi TR, Smith SA, Collins FS, Tucker MA: Detection of eight BRCA1 mutations in 10 breast/ovarian cancer families, including 1 family with male breast cancer. Am J Hum Genet. 1995, 57 (1): 1-7.PubMed CentralView ArticlePubMedGoogle Scholar
  26. Struewing JP, Abeliovich D, Peretz T, Avishai N, Kaback MM, Collins FS, Brody LC: The carrier frequency of the BRCA1 185delAG mutation is approximately 1 percent in Ashkenazi Jewish individuals. Nat Genet. 1995, 11 (2): 198-200. 10.1038/ng1095-198.View ArticlePubMedGoogle Scholar
  27. Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B, Higgins J, DeFelice M, Lochner A, Faggart M, Liu-Cordero SN, Rotimi C, Adeyemo A, Cooper R, Ward R, Lander ES, Daly MJ, Altshuler D: The structure of haplotype blocks in the human genome. Science. 2002, 296 (5576): 2225-2229. 10.1126/science.1069424.View ArticlePubMedGoogle Scholar
  28. Carlson CS, Eberle MA, Rieder MJ, Yi Q, Kruglyak L, Nickerson DA: Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. Am J Hum Genet. 2004, 74 (1): 106-120. 10.1086/381000.PubMed CentralView ArticlePubMedGoogle Scholar
  29. Bonnen PE, Wang PJ, Kimmel M, Chakraborty R, Nelson DL: Haplotype and linkage disequilibrium architecture for human cancer-associated genes. Genome Res. 2002, 12 (12): 1846-1853. 10.1101/gr.483802.PubMed CentralView ArticlePubMedGoogle Scholar
  30. Liu XD, Barker DF: Evidence for effective suppression of recombination in the chromosome 17q21 segment spanning RNU2-BRCA1. Am J Hum Genet. 1999, 64 (5): 1427-1439. 10.1086/302358.PubMed CentralView ArticlePubMedGoogle Scholar
  31. Freedman ML, Penney KL, Stram DO, Riley S, McKean-Cowdin R, Le Marchand L, Altshuler D, Haiman CA: A haplotype-based case-control study of BRCA1 and sporadic breast cancer risk. Cancer Res. 2005, 65 (16): 7516-7522. 10.1158/0008-5472.CAN-05-0132.View ArticlePubMedGoogle Scholar
  32. Kidd JR, Speed WC, Pakstis AJ, Kidd KK: A 100 kb block encompassing BRCA1. Am J Hum Genet. 2003, 73 (suppl 5): 173-Google Scholar
  33. Zhang J, Rowe WL, Clark AG, Buetow KH: Genomewide distribution of high-frequency, completely mismatching SNP haplotype pairs observed to be common across human populations. Am J Hum Genet. 2003, 73 (5): 1073-1081. 10.1086/379154.PubMed CentralView ArticlePubMedGoogle Scholar
  34. Pritchard JK, Przeworski M: Linkage disequilibrium in humans: models and data. Am J Hum Genet. 2001, 69 (1): 1-14. 10.1086/321275.PubMed CentralView ArticlePubMedGoogle Scholar
  35. Collins FS, Brooks LD, Chakravarti A: A DNA polymorphism discovery resource for research on human genetic variation. Genome Res. 1998, 8 (12): 1229-1231.PubMedGoogle Scholar
  36. Kramer JL, Velazquez IA, Chen BE, Rosenberg PS, Struewing JP, Greene MH: Prophylactic oophorectomy reduces breast cancer penetrance during prospective, long-term follow-up of BRCA1 mutation carriers. J Clin Oncol. 2005, 23 (34): 8629-8635. 10.1200/JCO.2005.02.9199.View ArticlePubMedGoogle Scholar
  37. National Laboratory for the Genetics of Israeli Populations at Tel-Aviv University. [http://www.tau.ac.il/medicine/NLGIP/nlgip.htm]
  38. Environmental Genome Project, University of Washington. [http://egp.gs.washington.edu/data/brca1/]
  39. de Bakker PI, Yelensky R, Pe'er I, Gabriel SB, Daly MJ, Altshuler D: Efficiency and power in genetic association studies. Nat Genet. 2005, 37 (11): 1217-1223. 10.1038/ng1669.View ArticlePubMedGoogle Scholar
  40. POPGENE: the user-friendly shareware for population genetic analysis. [http://www.ualberta.ca/~fyeh/index.htm]
  41. SNPHAP: a program for estimating frequencies of large haplotypes of SNPs. [http://www-gene.cimr.cam.ac.uk/clayton/software/snphap.txt]
  42. Zhang J, Rowe WL, Struewing JP, Buetow KH: HapScope: a software system for automated and visual analysis of functionally annotated haplotypes. Nucleic Acids Res. 2002, 30 (23): 5213-5221. 10.1093/nar/gkf654.PubMed CentralView ArticlePubMedGoogle Scholar
  43. Barrett JC, Fry B, Maller J, Daly MJ: Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005, 21 (2): 263-265. 10.1093/bioinformatics/bth457.View ArticlePubMedGoogle Scholar
  44. Weir BS: Inferences about linkage disequilibrium. Biometrics. 1979, 35 (1): 235-254. 10.2307/2529947.View ArticlePubMedGoogle Scholar
  45. Maniatis N, Collins A, Xu CF, McCarthy LC, Hewett DR, Tapper W, Ennis S, Ke X, Morton NE: The first linkage disequilibrium (LD) maps: delineation of hot and cold blocks by diplotype analysis. Proc Natl Acad Sci USA. 2002, 99 (4): 2228-2233. 10.1073/pnas.042680999.PubMed CentralView ArticlePubMedGoogle Scholar
  46. EpiInfo Version 3.4.1. [http://www.cdc.gov/epiinfo/]

Copyright

© Pereira et al; licensee BioMed Central Ltd. 2007

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement