© The Author 2006. Published by Oxford University Press.
ARTICLE |
Commonly Studied Single-Nucleotide Polymorphisms and Breast Cancer: Results From the Breast Cancer Association Consortium
The Breast Cancer Association Consortium
See "Appendix" for the names and affiliations of the authors
Correspondence to: Paul Pharoah, MB, PhD, Strangeways Research Laboratory, Worts Causeway, Cambridge CB1 8RN, U.K. (e-mail: paul1{at}srl.cam.ac.uk).
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ABSTRACT |
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 TopNotes Abstract IntroductionMethodsResultsDiscussionAppendixReferences |
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Background: The Breast Cancer Association Consortium (BCAC) is an international collaboration that was established to provide large sample sizes for examining genetic associations. We conducted combined analyses on all single-nucleotide polymorphisms (SNPs) whose associations with breast cancer have been investigated by at least three participating groups. Methods: Data from up to 12 studies were pooled for each SNP (ADH1C I350V, AURKA F31I, BRCA2 N372H, CASP8 D302H, ERCC2 D312N, IGFBP3 –202 c>a, LIG4 D501D, PGR V660L, SOD2 V16A, TGFB1 L10P, TP53 R72P, XRCC1 R399Q, XRCC2 R188H, XRCC3 T241M, XRCC3 5' UTR, and XRCC3 IVS7-14). Genotype frequencies in case and control subjects were compared, and genotype-specific odds ratios for the risk of breast cancer in heterozygotes and homozygotes for the rare allele compared with homozygotes for the common allele were estimated with logistic regression. Statistical tests were two-sided. Results: The total number of subjects for analysis of each SNP ranged from 12 013 to 31 595. For five SNPs—CASP8 D302H, IGFBP3 –202 c>a, PGR V660L, SOD2 V16A, and TGFB1 L10P—the associations with breast cancer were of borderline statistical significance (P = .016, .060, .047, .056, and .0088 respectively). The remaining 11 SNPs were not associated with breast cancer risk; genotype-specific odds ratios were close to unity. There was some evidence for between-study heterogeneity (P<.05) for four of the 11 SNPs (ADH1C I350V, ERCC2 D312N, XRCC1 R399Q, and XRCC3 IVS5-14). Conclusion: Pooling data within a large consortium has helped to clarify associations of SNPs with breast cancer. In the future, consortia such as the BCAC will be important in the analysis of rare polymorphisms and gene x gene or gene x environment interactions, for which individual studies have low power to identify associations, and in the validation of associations identified from genome-wide association studies.
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INTRODUCTION |
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Breast cancer, like most common cancers, tends to cluster in families, with the disease being approximately twofold more common in women whose first-degree relatives have breast cancer than in women without such a family history (1). The higher rate of most breast cancers in monozygotic twins of case patients than in dizygotic twins or siblings suggests that most familial clustering is the result of inherited genetic factors rather than lifestyle or environmental factors (2). Some of this clustering can be explained by mutations in specific genes that confer high risk of disease. However, such susceptibility alleles are rare in the population. For example, highly penetrant variants in the breast cancer susceptibility genes BRCA1 and BRCA2 account for less than 20% of the total genetic risk of breast cancer and other, rarer high-penetrance genes such as TP53 and PTEN account for less than 5% of the risk (3). It is likely that much of the unexplained familial risk is due to alleles of low to moderate penetrance.
The genetic association study is a powerful tool for identifying such alleles. During the past 10 years, such studies have been used widely in the search for breast cancer susceptibility alleles, and most of these studies have focused on putative functional variants in genes that are candidates for influencing breast cancer risk because of their known biologic functions. Nevertheless, few definitive common susceptibility alleles have emerged, and most associations reported in the literature have not been confirmed by subsequent studies (4,5). This situation probably reflects the false-positive nature of most initial reports (type I errors) and is probably exacerbated by publication bias. The levels of statistical significance that are appropriate in other contexts (P = .05 or P = .01) can be highly misleading in genetic association studies. Because the number of possible genetic polymorphisms is very large and the prior probability that any polymorphism will be associated with disease is low, most polymorphisms that achieve a modest level of statistical significance will be false positives. The false-positive rate can be reduced by using more stringent levels of statistical significance (6). However, most studies lack the power to detect moderate relative risks at stringent levels of statistical significance. In addition, nonreplication may occur because of a lack of adequate statistical power in the replication study, resulting in false negatives (type II errors). The major lesson from these observations is that large samples sizes are needed to detect and confirm, at appropriate levels of statistical significance, genetic variants that are associated with modest increases in risk (7,8). The ability to identify such genetic variants can be further improved by careful selection of both candidate gene and candidate polymorphism (6).
One approach to conducting more powerful association studies is through collaboration between existing groups. The Breast Cancer Association Consortium (BCAC) has been established to conduct such collaborative studies in breast cancer. The consortium currently includes more than 20 collaborative groups, with a combined sample size in excess of 30 000 case subjects and 30 000 control subjects. The aim of the current study was to conduct combined analyses on all single-nucleotide polymorphisms (SNPs) that had been investigated in at least three different studies by members of the consortium and for which genotype had been determined in at least 10 000 subjects. A total of 16 SNPs fulfilled these criteria: CASP8 D302H (rs1042485), IGFBP3 –202 c>g (rs2854744), PGR V660L (rs1042438), SOD2 V16A (rs1799725), TGFB1 L10P (rs1982073), ADH1C I350V (rs698), AURKA F31I (rs2273535), BRCA2 N372H (rs144848), ERCC2 D312N (rs1799793), LIG4 D501D (rs1805386), TP53 R72P (rs1042522), XRCC1 R399Q (rs25487), XRCC2 R188H (rs3218536), XRCC3 T241M (rs861539), XRCC3 5' UTR (rs1799794), XRCC3 IVS7-14 (rs1799796). More than half of the data used in this analysis have not previously been published.
The I350V polymorphism in alcohol dehydrogenase type 3 (ADH1C) affects the kinetics of alcohol oxidation, with the valine allele being associated with more rapid oxidation. Because alcohol consumption may increase the risk of breast cancer by increasing exposure to carcinogenic metabolites (9) or by influencing the levels of reproductive steroid hormones that play a critical role in breast carcinogenesis (10), this polymorphism could influence the effect of alcohol consumption on risk of breast cancer. Two previous studies have reported on this variant in breast cancer, although both studies included fewer than 500 breast cancer patients. Freudenheim et al. (11) analyzed data from the New York Diet Study and found a reduced disease risk in premenopausal women who carry the rare allele but no association with breast cancer risk in postmenopausal women. Hines et al. (12) reported no association at any age using data from the Nurses Health Study.
AURKA (STK6/STK15) encodes AURORA-A, a serine/threonine kinase that is involved in mitotic chromosomal segregation. The F31I polymorphism is associated with increased aneuploidy in colon tumors and cell transformation in vitro (13). A recent meta-analysis (14) of 15 studies of breast, colon, ovarian, prostate, lung, esophageal, and nonmelanoma skin cancer showed a statistically significantly increased risk associated with the I-allele for colon cancer, breast cancer, and all cancers combined. The breast cancer studies in that analysis provided a total of approximately 5700 participants. Other studies have reported an increased risk for esophageal cancer associated with the isoleucine allele (15,16).
Carriers of rare, mostly protein-truncating mutations in the BRCA2 gene are strongly predisposed to developing breast and several other cancers. The only common (minor allele frequency >.05) nonsynonymous SNP in BRCA2 is N372H. Homozygous carriers of the histidine allele were first reported to have a 30% increase in breast cancer risk in a pooled analysis from five case–control studies conducted among Northern European Caucasian populations (17), with the strongest association observed in young women (<45 years of age). This finding was later confirmed in an Australian population (18); again, the strongest association was confined to young women (<40 years old). Studies conducted in the United States (19–21) have not confirmed these initial reports.
The CASP8 gene encodes caspase 8, one of the initiator caspases that transduce apoptotic signals from the death receptors at the cell surface. A U.K. study of approximately 6000 subjects reported that the rare allele of the nonsynonymous SNP D302H was associated with a reduced risk of breast cancer in a dose-dependent manner (22). This finding was subsequently confirmed in a German study of 355 patients with familial breast cancer and 1000 control subjects (23).
The ERCC2 gene encodes a protein that is involved in transcription-coupled nucleotide excision repair and is an integral member of the basal transcription factor BTF2–TFIIH complex. The D312N polymorphism occurs in a highly conserved helicase domain, and the aspartic acid allele is associated with a diminished apoptotic response (24). Several studies have examined the role of this variant in breast cancer risk, with different results (25–29). The studies varied considerably in the size and ethnic composition of the population and in the study design. Although the largest study—of 3634 case subjects and 3340 control subjects from the United Kingdom, Germany, and Australia—found no association between the ERCC2 D312N SNP and risk of breast cancer (29), a population-based study from Germany and a hospital-based study from Finland found an increased risk of breast cancer associated with the major (i.e., aspartic acid containing) allele (26,27).
Insulin-like growth factor–binding proteins (IGFBPs) regulate the biologic activity of insulin-like growth factor 1, and serum IGFBP3 levels have been shown to be associated with breast cancer risk (30). Although several studies have shown that the –202 c>a promoter polymorphism in IGFBP3 is associated with altered levels of IGFBP3 (31–35), the single report of a positive association with breast cancer (36) has not been confirmed in other studies (31–34,37,38).
A complex of ligase IV (LIG4) and XRCC4 acts in the nonhomologous end–joining DNA repair pathway to carry out the ligation step in the repair of double-stranded DNA breaks. The silent t1977c (D501D) polymorphism in LIG4 has been investigated as a risk factor for breast cancer in two studies (39,40) in predominantly white European populations. Neither study found a statistically significant main effect, although Han et al. (40) reported an increased risk associated with the rare allele in carriers with a first-degree family history of breast cancer.
Progesterone is a key steroid sex hormone in female sexual development and reproductive activity, and its physiologic actions are mediated by the progesterone receptor, encoded by the PGR gene. One study has reported an increased risk of breast cancer associated with the PGR V660L polymorphism (41), but this finding was not confirmed in other studies (42–45).
SOD2 encodes manganese superoxide dismutase, an important component of the cellular antioxidant defense system. Three studies have reported a positive association of the A16V SNP with breast cancer (46–48), but most studies have found no association (49–55).
Transforming growth factor 
(TGF), which is encoded by TGFB1, suppresses tumor initiation (56) but can also promote tumor progression when the antiproliferative effect of the TGF signaling pathway has been overridden by other oncogenic mutations (57). The proline allele of the L10P polymorphism has been associated with increased secretion of TGF (58), but studies of its role in breast cancer susceptibility have yielded inconsistent results (58–60).
The TP53 tumor suppressor protein regulates the cellular stress response by controlling the induction of apoptosis or growth arrest at cell cycle checkpoints. The TP53 R72P polymorphism is located in a proline-rich region of the gene that is homologous to an SH3-binding domain and is required for the growth suppression activity of p53 (61). After the initial report of a statistically significantly increased risk of breast cancer in women homozygous for the proline allele (62), numerous studies have examined a possible role of this polymorphism in breast cancer risk. Of those studies that included at least 100 patients, none replicated the initial findings (63–66), and several studies (67,68) even observed increased risks of breast cancer in women homozygous for the arginine allele. The two largest studies, each including more than 1000 subjects, however, reported no association of this polymorphism with breast cancer risk (69,70).
XRCC1 interacts with DNA polymerase , DNA ligase III, PARP-1, and APE1 in the final ligation stage of the base excision DNA repair pathway. The R399Q polymorphism of XRCC1 occurs within its BRCT-I domain, which interacts directly with PARP-1. The first study to investigate an association between the XRCC1 R399Q polymorphism and breast cancer (71) found an association between breast cancer and the rare glutamine allele in African Americans but not in whites. Since then, 12 additional studies have examined the association of this polymorphism with breast cancer risk (27,72–82). Of these, one found a statistically significant association with homozygosity for the rare allele (72) and one found an association with being a carrier of this allele (80). The other 10 studies found no main effect of the rare allele, although two studies reported statistically significant interactions between the allele and other risk factors with breast cancer risk, one with "ever smoking" (81) and one with having a family history of breast cancer (78).
The XRCC2 protein is involved in homologous recombination repair. The R188H polymorphism affects the efficiency of DNA repair. The rare variant has been shown to be associated with an increased risk of breast cancer in some studies (39,83) but not in others (21,40,84).
The XRCC3 protein forms part of a complex involved in the repair of double-stranded DNA breaks by homologous recombination. A modest association between the homozygous variant genotype of the T241M allele of XRCC3 and breast cancer risk was first reported in a study in the United Kingdom (39); however, most subsequent studies in Caucasian populations (27,40,74,75,78,84,85) have not confirmed this association. A recent meta-analysis of published data suggested a very small increase in risk among women homozygous for the methionine allele (21). Associations with other SNPs in XRCC3 have been inconsistent. Although homozygosity for the IVS5-14a>g was associated with a statistically significant decrease in breast cancer risk in U.K. and Polish studies (21,39), two other studies, both from the United States (21,40), did not confirm this finding. The XRCC3 Ex2+2 a>g (5' UTR) polymorphism was not statistically significantly associated with breast cancer risk in previously published studies (21,39,40). Previous analyses of haplotypes inferred from these three XRCC3 polymorphisms suggested an increase in breast cancer risk associated with the rare gat haplotype (21,39).
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METHODS |
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TopNotesAbstractIntroductionMethodsResultsDiscussionAppendixReferences |
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Studies
Eighteen studies have contributed data to these analyses. A summary of the individual studies is given in Tables 1 and 2. All but two of the studies include predominantly white European populations. Eleven of the studies used population-based case ascertainment, whereas seven ascertained cases through hospital-based series. Four studies restricted ascertainment to women aged 70 years or less.
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Statistical Methods
Deviations of the genotype frequencies in the control subjects from those expected under Hardy–Weinberg equilibrium were assessed by chi-square tests (1 df), for each study separately. The main test of the null hypothesis of no association was a likelihood ratio test (2 df) comparing a logistic regression model that included terms for genotype and study with the model that include a term for study only. We tested for heterogeneity between study strata by comparing logistic regression models with and without a genotype x study interaction term by using a likelihood ratio test. Genotype-specific risks under a fixed-effects model for each SNP were estimated as odds ratios for the heterozygote and rare homozygote genotypes with the common homozygote as the referent category by using unconditional logistic regression. We also estimated a per-allele risk under a multiplicative codominant genetic model by fitting the number of rare alleles carried as a continuous covariate. We also estimated summary genotype-specific risks under a random-effects model by using the method of DerSimonian and Laird (86).
Testing for multiple potential gene x gene or gene x environment interaction in the absence of statistically significant main effects is of doubtful value, mainly because there are many more possible interactions than main effects and so there is a much more serious issue of multiple testing (87). We therefore tested for an interaction with age only by using only control subjects or only case subjects. This approach is more powerful than standard case–control methods for detecting interaction (88). Subjects were divided into four age categories—less than 40 years, 40–49 years, 50–59 years, and more than 60 years—according to age at diagnosis for case patients and age at interview for control subjects. Two dummy variables were created, one for heterozygotes and one for the rare homozygotes, each with values 1, 2, 3, and 4 for these four age categories, respectively. Poisson regression and a likelihood ratio test were then used to compare models with and without the two interaction terms treated as continuous variables. We estimated age-specific risks by comparing the genotype distribution of case subjects within each age category with that among control subjects at all ages.
Genotype data for three XRCC3 SNPs were available from five studies. Haplotype frequencies and subject-specific expected haplotype indicators were calculated separately for each study with TagSNPs software (89), which implements an expectation-substitution approach to account for haplotype uncertainty given unphased genotype data. Subjects missing data for two of the three SNPs were excluded. We considered haplotypes with a frequency of more than 2% in at least one study to be "common." Rare haplotypes were pooled. We used unconditional logistic regression to test the global null hypothesis of no association between haplotype and breast cancer by comparing a model including terms for study and haplotype (assuming multiplicative effects for each common haplotype) with a model that included a term for study only. Haplotype-specific odds ratios were also estimated with their associated 95% confidence intervals. We also tested for multilocus effects by using the logistic regression procedure suggested by Cordell and Clayton (90). This method includes a main effect term for each SNP in the logistic regression model rather than modeling the full haplotype effect. A likelihood ratio test was used to test this model for statitistical significance. All statistical tests were two-sided.
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RESULTS |
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For five of the 16 SNPs (CASP8 D302H, IGFBP3 –202 c>a, PGR V660L, SOD2 V16A, and TGFB1 L10P), there was some evidence of an association with breast cancer risk. The total sample size (i.e., total number of case and control subjects) ranged from 12 059 for CASP8 D302H (three studies) to 17 190 for SOD2 V16A (five studies). The results based on the analyses of pooled data for these SNPs are shown in Table 3. However, no association reached anywhere near the level of statistical significance that has been suggested as appropriate for genetic association studies (i.e., P<10–5–10–7) (91). The most statistically significant association, with a P = .0088, was seen for TGFB1 L10P. Therefore, these five SNPs will be genotyped in other BCAC studies, and further analyses will be carried out when the complete dataset becomes available.
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The remaining 11 SNPs were not associated with breast cancer risk (Table 4). The total sample size ranged from 12 013 for ERCC2 D312N, which was genotyped in seven studies, to 31 595 for BRCA2 N372H, which was genotyped in 12 studies. These sample sizes are larger than those in any previously published breast cancer association study, with the exception of a combined analysis of CHEK2*1100delc(92); they are also considerably larger than those of any previous study of the polymorphisms included in this analysis. Genotype frequencies in case and control subjects for the individual studies are shown in Supplementary Table 1 (available at: http://jncicancerspectrum.oxfordjournals.org/jnci/content/vol98/issue19), and genotype-specific risks for each SNP by study are shown in Supplementary Table 2 (available at: http://jncicancerspectrum.oxfordjournals.org/jnci/content/vol98/issue19). Figure 1 shows the study-specific and genotype-specific risks. The genotype-specific risks for the combined analyses were all close to unity. The 95% confidence intervals for the odds ratio for heterozygotes compared with common homozygotes were narrow, with lower limits no less than 0.85 and upper limits of 1.11 or less. Estimates of the odds ratios for the rare homozygotes compared with common homozygotes were less precise, with lower 95% confidence limits no less than 0.69 and upper limits of 1.30 at most.
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There was strong evidence of between-study heterogeneity for one SNP, ERCC2 D312N (P<.001), and marginal evidence of between-study heterogeneity for three SNPs—ADH1C I350V (P = .045), XRCC1 R399Q (P = .049), and XRCC3 IVS5-14 (P = .011). In the presence of heterogeneity, data pooling under a fixed-effects model may not be appropriate. We therefore also estimated the genotype-specific risks using a random-effects model, but the summary estimates were similar to those estimated under the fixed-effects models (Table 4).
There was a statistically significant association between ADH1C I350V genotype frequencies and age in the control subjects, with the minor allele frequency falling with age (gradient = –0.45 for heterozygote frequency and –0.13 for homozygote frequency, P<.001). However, no association of genotype with age was evident in case subjects, and the age-specific odds ratios were all similar to one another (Table 5). Similarly, the minor allele frequency for AURKA F31I fell with age in the controls ( = –0.074 in heterozygotes and –0.11 in homozygotes, P = .045). Again, however, there was no association with genotype in case subjects and no clear age dependency for the age-specific risk estimates. For BRCA2 N372H, there was a statistically significant association between genotype frequency and age that was restricted to the case subjects ( = –0.004 in heterozygotes and –0.081 in homozygotes, P = .023). This association was reflected in a decline in the age-specific relative risk for rare-allele homozygotes from 1.30 (95% CI = 0.96 to 1.76) in women younger than 40 years to 1.02 (95% CI = 0.86 to 1.21) in those more than 60 years of age. No association with age was seen in case or control subjects for any of the remaining SNPs.
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Of the eight possible XRCC3 haplotypes, only four were common (i.e., frequency > 2%; Supplementary Table 3, available at: http://jncicancerspectrum.oxfordjournals.org/jnci/content/vol98/issue19). There was a statistically significant difference in haplotype frequencies between case and control subjects (global test P = .014), but this difference was due almost entirely to a difference in the frequency of the rare haplotypes. In particular, the gat (rs1799794/rs1799796/rs861539 rare/common/rare) haplotype was associated with an odds ratio of 2.38 (95% CI = 1.17 to 4.83) per copy of the haplotype carried. However, the estimated frequency of this haplotype in control subjects, based on unphased genotype data, was only 0.06%–0.14% in the five studies. Moreover, there were no differences among case and control subjects in the frequencies of the common haplotypes. In addition, the test for multilocus effects using the logistic regression approach of Cordell and Clayton (90) was not statistically significant (P = .13).
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DISCUSSION |
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TopNotesAbstractIntroductionMethodsResultsDiscussionAppendixReferences |
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Here we pooled genotype data for 16 SNPs that have commonly been studied in breast cancer from 3 to 12 case–control studies from BCAC participants in Europe, the United States, Australia, and Asia. For five SNPs, CASP8 D302H, IGFBP3 –202 c>a, PGR V660L, SOD2 V16A, and TGFB1 L10P, there was some evidence for an association with breast cancer risk, although none of the associations showed the level of statistical significance that has been suggested to be appropriate in genetic association studies. Even a simple Bonferroni correction allowing for 16 independent hypothesis tests would require a P value of .0031 to be equivalent to a conventional P value of .05, whereas the most statistically significant finding, for TGFB1 L10P, had P = .0088. Thus, genotyping of these SNPs by additional BCAC studies is in progress. The inclusion of these additional studies is likely to double the sample size and thus provide adequate power to definitive confirmation or refutation of the observed associations.
The remaining 11 SNPs, ADH1C I350V, AURKA F31I, BRCA2 N372H, ERCC2 D312N, LIG4 D501D, PGR V660L, TP53 R72P, XRCC1 R399Q, XRCC2 R188H, XRCC3 T241M, XRCC3 5' UTR a>g, and XRCC3 IVS5-14, were not associated with risk of breast cancer. All these SNPs except LIG4 D501D and XRCC3 5' UTR have been previously reported to be associated with a modest relative risk of breast cancer (>1.3) at a nominal level of statistical significance of .05—in some instances by individual studies within the consortium. In each case, however, the association was not confirmed in this much larger dataset. For example, homozygotes for the histidine allele of the BRCA2 N372H polymorphism were reported in two studies (17,18) to have a relative risk of breast cancer of approximately 1.3–1.4. In this study—with genotype data for this polymorphism on more than 15 000 case subjects and 15 000 control subjects—the upper 95% confidence limit was only 1.12 for HH homozygotes and 1.05 for heterozygotes.
All 11 SNPs can be excluded, with a reasonable degree of certainty, from having more than a small dominant effect on breast cancer risk because for all of them the upper confidence limit for the relative risk in heterozygous carriers of the rare allele compared with common-allele homozygotes was less than 1.12. Similarly, we can exclude moderate recessive effects because the upper confidence limit of relative risk in rare homozygotes was less than 1.30 for all SNPs. However, much larger sample sizes would be needed to detect recessive alleles with effects of this magnitude or less. For example, a total sample size of 93 000 would be required to detect a recessive allele of frequency 0.1 that confers a relative risk of 1.25 with 90% power at a type I error rate of 5%.
Four SNPs (ADH1C I350V, ERCC2 D312N, XRCC1 R399Q, and XRCC3 IVS5-14) showed some evidence for among-study heterogeneity in estimated relative risks. However, the evidence was marked for only ERCC2 D312N, for which one group (26) found an association that was not present in the other BCAC studies included here, although a similar association has been reported in a Finnish study (27).
In principle, heterogeneity could be caused by many factors. One possible factor is differences in the underlying population. However, all but two of the 18 studies selected case subjects from Western, predominantly white, populations, all with similar breast cancer incidence rates. The other two studies were from Thailand and Korea, which have different patterns of lifestyle risk factors, genetic backgrounds, and breast cancer risks from those in Western countries. However, although the allele frequencies for several SNPs differed in the Asian and Western studies, as expected, the estimated relative risks were not substantially different (Supplementary Table 1, available at: http://jncicancerspectrum.oxfordjournals.org/jnci/content/vol98/issue19). The Asian studies were, therefore, unlikely to be an important cause of the heterogeneity, except for XRCC1 R399Q, for which both Asian studies showed some risk increase in homozygotes for the rare allele that was not apparent overall.
Heterogeneity could also have resulted from the fact that each study used a different approach to select case and control subjects. However, all the studies selected control subjects from the general population of interest, and it seems unlikely that the selection procedure would have a strong relationship with genotype at any of these loci. There were also some differences in the selection of case subjects in that most studies used some age criterion and some studies selected case subjects based on family history or bilateral disease. In particular, some studies were strongly weighted toward young age at diagnosis, and case selection based on age could induce heterogeneity if age-specific associations were present. But age-specific associations cannot explain the observed heterogeneity because there were no strong age-specific effects for the four SNPs.
Finally, some heterogeneity could have been introduced by the inclusion of the initial study reporting the association in our analysis. Indeed, after excluding the first published studies of XRCC1 R399Q (72), there was no evidence for heterogeneity among the other studies of this SNP (data not shown). However, the tests for between-study heterogeneity for ADH1C I350V, ERCC2 D312N, and XRCC3 IVS5-14 remained statistically significant after excluding the first of the studies to have been published (data not shown).
Thus, we do not have a clear explanation for the statistical heterogeneity that was present for four of the SNPs. Although some variation in risk could be real, it seems unlikely that there are associations in one population but not others. Instead, we think it is most likely that any observed heterogeneity is due to some unexplained artifact and that the average effect across all studies, represented by the combined analyses (i.e., null), is the best reflection of the underlying reality.
We did not test for gene x gene or gene x environment interactions because of the issue of multiple testing. Furthermore, the biases inherent in case–control studies limit their usefulness in the study of gene x environment interactions. Nevertheless, it is possible that these polymorphisms alter risk in subgroups of the population that have been exposed to specific environmental and lifestyle factors, for example, if an association of ADH1C I350V with breast cancer risk were limited to women with high alcohol consumption. However, where no main effect has been detected, such subgroup effects must be small, the at-risk subgroup must represent a small proportion of the population under study, or there must be true crossover effects (i.e., genotype associations of different directions among subgroups), which are unlikely. Consequently, the power to detect interaction will be minimal. Despite these problems, we did investigate possible age-specific effects, although the results of these analyses illustrate the limitations of subgroup analysis in the absence of a main effect. We found some evidence for an interaction of AURKA F31I and BRCA2 N372H with age, with a suggestion of an increased risk in women less than 40 years of age. However, the tests for interaction were only marginally statistically significant, and the estimated odds ratios for the youngest age group (1.26 and 1.30, respectively) were small and not statistically significantly different from unity, despite the very large sample size. Thus, an even larger sample size would be required to determine whether there is any association between genotypes at these loci and breast cancer risk at a young age.
The analyses based on the BCAC dataset represent most of the available data of which we are aware for these SNPs. Because we have obtained data on all SNPs typed by consortium groups and have selected SNPs objectively for this analysis based on the available genotyping, we have minimized bias due to selective reporting of interesting results. For these reasons, we believe that these results are an accurate reflection of the true associations between these SNPs and breast cancer risk. This study illustrates the value of large consortia for clarifying risks associated with complex diseases. It has focused on common SNPs that, in some cases, have been reported previously to be associated with increased risks of breast cancer. However, consortia such as BCAC will also be important in the analysis of rare polymorphisms and gene x gene and gene x environment interactions, for which individual studies have poor power to identify associations, and in the validation of associations identified from genome-wide association studies.
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APPENDIX |
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TopNotesAbstractIntroductionMethodsResultsDiscussionAppendixReferences |
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Australian Breast Cancer Family Study: Georgia Chenevix-Trench, Queensland Institute for Medical Research; John L. Hopper, University of Melbourne; Amanda B. Spurdle, Queensland Institute for Medical Research; Xiaoqing Chen, Queensland Institute for Medical Research.
British Breast Cancer Study: Olivia Fletcher, Breakthrough Research Centre, Institute of Cancer Research; Nichola Johnson, Breakthrough Research Centre, Institute of Cancer Research; Claire Palles, Breakthrough Research Centre, Institute of Cancer Research; Julian Peto, Cancer Research United Kingdom (CRUK) Genetics and Epidemiology Group, London School of Hygiene & Tropical Medicine & Institute of Cancer Research; Isabel dos Santos Silva, CR-UK Genetics and Epidemiology Group, London School of Hygiene & Tropical Medicine & Institute of Cancer Research.
Copenhagen Breast Cancer Study: Stig E. Bojesen, Department of Clinical Biochemistry, Herlev University Hospital; Christen K. Axelsson, Department of Breast Surgery, Herlev University Hospital; Børge G. Nordestgaard, Department of Clinical Biochemistry, Herlev University Hospital.
GENICA: Ute Hamann, Deutsches Krebsforschungszentrum, Heidelberg; Muhammad U. Rashid, Deutsches Krebsforschungszentrum, Heidelberg; Christina Justenhoven, Dr Margarete Fischer-Bosch-Institute of Clinical Pharmacology (IKP), Stuttgart; Hiltrud Brauch, Dr Margaret Fischer-Bosch-Institute of Clinical Pharmacology (IKP), Stuttgart; Yon Ko, Johanniter-Krankenhaus, Bonn; Beate Pesch, Berufsgenossenschaftliches Forschungsinstitut für Arbeitsmedizin, Bochum.
Hannover Bilateral Breast Cancer Study: Thilo Dörk, Department of Gynecology, Hannover Medical School; Sandra Beussel, Department of Gynecology, Hannover Medical School; Katrin Gerriets, Department of Radiation Oncology, Hannover Medical School; Michael Bremer, Department of Radiation Oncology, Hannover Medical School.
Genetic Epidemiologic Study of Breast Cancer by Age 50: Jenny Chang-Claude, German Cancer Research Center, Heidelberg; Shan Wang-Gohrke, University of Ulm.
Helsinki Breast Cancer Study: Heli Nevanlinna, Department of Obstetrics and Gynecology, Helsinki University Central Hospital; Johanna Tommiska, Department of Obstetrics and Gynecology, Helsinki University Central Hospital; Rainer Fagerholm, Department of Obstetrics and Gynecology, Helsinki University Central Hospital; Carl Blomqvist, Department of Oncology, Helsinki University Central Hospital.
International Agency for Research on Cancer Breast Cancer Study in Thailand: David Hughes, International Agency for Research into Cancer, Lyon; Fabrice Odefrey, International Agency for Research into Cancer, Lyon; Valerie Gaborieau, International Agency for Research into Cancer, Lyon; Paul Brennan, International Agency for Research into Cancer, Lyon; Suleeporn Sangrajrang, Thai National Institute of Cancer, Bangkok.
Kuopio Breast Cancer Project: Arto Mannermaa, Department of Clinical Pathology and Forensic Medicine, University of Kuopio; Vesa Kataja, Department of Oncology, University Hospital and University of Kuopio; Veli-Matti Kosma, Department of Clinical Pathology and Forensic Sciences, University Hospital and University of Kuopio.
Mayo Clinic Breast Cancer Study: Fergus J. Couch, Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, Rochester, MN; Ellen L. Goode, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN; Janet Olson, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN; Thomas A. Sellers, H. Lee Moffitt Cancer Center, Tampa, FL.
National Cancer Institute Breast Cancer Study in Poland: Montserrat Garcia-Closas, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Rockville, MD; Jolanta Lissowska, M. Sklodowska-Curie Institute of Oncology and Cancer Center, Warsaw; Stephen Chanock, Core Genotyping Facility, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Gaithersburg, MD; Beata Peplonska, Nofer Institute of Occupational Medicine, Lodz.
US Three-State Breast Cancer Study: Montserrat Garcia-Closas, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Rockville, MD; Kathleen M. Egan, Vanderbilt University Medical Center, Nashville, TN; Polly A. Newcomb, University of Wisconsin, Madison, WI; Linda Titus-Ernstoff, Dartmouth Medical School, Norris Cotton Cancer Center, Lebanon, NH.
Studies of Epidemiology and Risk Factors in Cancer Heredity: Paul Pharoah, Department of Oncology, University of Cambridge; Douglas Easton, Department of Public Health and Primary Care, University of Cambridge; Alison Dunning, Department of Oncology, University of Cambridge; Bruce Ponder, Department of Oncology, University of Cambridge.
Seoul Breast Cancer Study: Daehee Kang, Seoul National University; Dong-Young Noh, Seoul National University; Keun-Young Yoo, Seoul National University; Sei Hyun Ahn, Ulsan University.
Sheffield Breast Cancer Study: Angela Cox, University of Sheffield; Malcolm W. R. Reed, University of Sheffield; Sabapathy P. Balasubramanian, University of Sheffield; Saeed Rafii, University of Sheffield; Gordon MacPherson, University of Sheffield; Wei-Yu Lin, University of Sheffield.
Spanish National Cancer Centre Breast Cancer Study: Javier Benitez, Human Cancer Genetics Programme, Spanish National Cancer Centre, Madrid; Roger Milne, National Genotyping Centre (CeGen), Human Cancer Genetics Programme, Spanish National Cancer Centre, Madrid.
US Radiologic Technologists Study: Alice J. Sigurdson, Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics; Lutecia H. Mateus Pereira, Laboratory of Population Genetics, Center for Cancer Research; Michele M. Doody, Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics; and Jeffery P. Struewing, Laboratory of Population Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services.
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The Australian Breast Cancer Family Study was funded by the National Health and Medical Research Council (NHMRC), the Victorian Health Promotion Foundation, the New South Wales Cancer Council, and as part of the Breast Cancer Family Registry funded by the US National Cancer Institute (NCI) under RFA # CA-95-003. The content of this article does not necessarily reflect the views or policies of the NCI or any of the collaborating centers in the Cancer Family Registry, and the mention of trade names, commercial products, or organizations does not imply endorsement by the US government or the Cancer Family Registry centers. The genotyping and analysis were supported by grants from the NHMRC and the Queensland Cancer Fund. A. B. Spurdle is funded by an NHMRC Career Development Award, and G. Chenevia-Trench and J. L. Hopper are NHMRC Senior and Senior Principal Research Fellows, respectively. The British Breast Cancer Study and the Mammography Oestrogens and Growth Factors Study are funded by Cancer Research United Kingdom (CRUK) and Breakthrough Breast Cancer. The Copenhagen Breast Cancer Study was supported by the Danish Medical Research Council, Chief Physician Johan Boserup and Lise Boserup's Fund, and Copenhagen County. The genetic epidemiologic study of bilateral breast cancer in Germany was supported by an intramural grant from Hannover Medical School. We gratefully acknowledge the support of Professor Johann Hinrich Karstens and Professor Christof Sohn. The GENICA study was supported by the Federal Ministry of Education and Research (BMBF) Germany grants 01KW9975/5, 01KW9976/8, 01KW9977/0, and 01KW0114. Genotyping analyses were supported by Deutsches Krebsforschungszentrum, Heidelberg, and the Robert Bosch Foundation of Medical Research, Stuttgart, Germany. The Genetic Epidemiologic Study of Breast Cancer by Age 50 in Germany was supported by the Deutsche Krebshilfe (Project number 70492). The genotyping was funded by the Deutsche Forschungsgemeinschaft (DFG RU476/2) and the Medical Faculty of the University of Ulm (P.589 and P.685). The Helsinki study was supported by the Helsinki University Central Hospital Research Fund, Academy of Finland, Finnish Cancer Society, and Sigrid Juselius Foundation. The Kuopio Breast Cancer Project was supported by the Finnish Ministry of Education and the Kuopio University Hospital EVO funding. The genotyping for the Madrid study was partially funded by the Genome Spain Foundation. The SEARCH study was funded by CRUK, and D. F. Easton is a Principal Fellow and P. D. Pharoah is a Senior Clinical Research Fellow of CRUK. The Sheffield study was funded by the Breast Cancer Campaign and Yorkshire Cancer Research. We are grateful to Lydia Hulme for help with genotyping. The US Radiologic Technologists Study, the US Three-State Study, and the NCI–Polish studies were funded in part by the Intramural Research Program of the NCI, Division of Cancer Epidemiology and Genetics, and the Center for Cancer Research. The US Three-State Study was also supported by RO1 CA67264, CA47147 (P. A. Newcomb); RO1 CA67338, CA69664 (L. Titus-Ernstoff), and RO1 CA47305 (K. M. Egan). We thank Christine Ambrosone, Jo Freudenheim, Peter Shields, and Chi-Chen Hong for access to data from the Western New York Diet Study. The study sponsors had no role in the design, analysis, writing, or decision to publish the study.
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