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. 2007 Oct 4;8:68. doi: 10.1186/1471-2156-8-68

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 Pereira 1, Marbin A Pineda 1, William H Rowe 1, Libia R Fonseca 2, Mark H Greene 3, Kenneth Offit 4, Nathan A Ellis 5, Jinghui Zhang 1, Andrew Collins 6, Jeffery P Struewing 1,✉
PMCID: PMC2093936  PMID: 17916242

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 [5-9], 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 [18-20]. 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 0.006f 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
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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 [29-31]. 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).

Figure 1.

Figure 1

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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.

Figure 2.

Figure 2

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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.

Figure 3.

Figure 3

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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.

Figure 4.

Figure 4

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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 0.006c 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
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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.

Competing interests

The author(s) declares that there are no competing interests.

Authors' contributions

LMP performed laboratory and statistical analysis and drafted the manuscript. MAP and LRF performed laboratory analysis. WHR and JZ wrote custom software and performed statistical analysis. MHG provided biomaterials and critically revised the manuscript. KO and NAE participated in the study design and analysis. AC provided design assistance and performed statistical analysis. JPS conceived and designed the study and performed statistical analysis. All authors read and approved the final manuscript.

Supplementary Material

Additional file 1

Summary of F-statistics and heterozygosity for all loci. Heterozygosity levels, as well as the variation in gene frequencies between populations by means of their FST (Wright's F-statistics).

Click here for file (33.3KB, pdf)
Additional file 2

Genotypes for all 114 polymorphic SNPs in 5 populations. Raw genotypes. Populations: (1) CEPH (includes related individuals), (2) African Americans, (3) Chinese Americans, (4) Mexican Americans and (5) Ashkenazi Jews.

Click here for file (120.8KB, txt)

Acknowledgments

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.

Contributor Information

Lutécia H Mateus Pereira, Email: luteciap@hotmail.com.

Marbin A Pineda, Email: pinedama@mail.nih.gov.

William H Rowe, Email: rowew@mail.nih.gov.

Libia R Fonseca, Email: lcaba003@fiu.edu.

Mark H Greene, Email: greenem@mail.nih.gov.

Kenneth Offit, Email: offitk@mskcc.org.

Nathan A Ellis, Email: naellis@uchicago.edu.

Jinghui Zhang, Email: jinghuiz@mail.nih.gov.

Andrew Collins, Email: A.R.Collins@soton.ac.uk.

Jeffery P Struewing, Email: struewij@mail.nih.gov.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Additional file 1

Summary of F-statistics and heterozygosity for all loci. Heterozygosity levels, as well as the variation in gene frequencies between populations by means of their FST (Wright's F-statistics).

Click here for file (33.3KB, pdf)
Additional file 2

Genotypes for all 114 polymorphic SNPs in 5 populations. Raw genotypes. Populations: (1) CEPH (includes related individuals), (2) African Americans, (3) Chinese Americans, (4) Mexican Americans and (5) Ashkenazi Jews.

Click here for file (120.8KB, txt)

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