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. Author manuscript; available in PMC: 2008 Apr 28.
Published in final edited form as: Carcinogenesis. 2008 Jan 3;29(2):333–341. doi: 10.1093/carcin/bgm284

Association of single-nucleotide polymorphisms in the cell cycle genes with breast cancer in the British population

Kristy E Driver 1,*, Honglin Song 1, Fabienne Lesueur 1, Shahana Ahmed 1, Nuno L Barbosa-Morais 1, Jonathan P Tyrer 1, Bruce AJ Ponder 1, Douglas F Easton 2, Paul DP Pharoah 1, Alison M Dunning, for the Studies in Epidemiology and Risks of Cancer Heredity (SEARCH) Team1,†
PMCID: PMC2346546  EMSID: UKMS1603  PMID: 18174243

Abstract

Using a large-scale case–control study, we examined whether common single-nucleotide polymorphisms (SNPs) within 13 genes involved in the cell cycle pathway are associated with breast cancer risk. Seventy-nine tag SNPs were used to evaluate 240 common SNPs found in the genes: CCND1, CCND2, CCND3, CCNE1, CDK2, CDK4, CDK6, CDKN1A, CDKNIB, CDKN2A/CDKN2B, CDKN2C and CDKN2D. These were genotyped in 2270 cases and 2280 controls from the Studies in Epidemiology and Risks of Cancer Heredity (SEARCH) study. Tag SNPs showing evidence of statistically significant differences between cases and controls (P < 0.1) were genotyped in a further 2200 cases and 2280 controls from the same population. This approach found evidence for breast cancer-associated SNPs in four of the cell cycle genes: the cyclin CCNE1 rs997669 had an odds ratio (OR) (GG/AA) of 1.18 [95% confidence interval (95% CI) 1.04–1.34] P = 0.003 and the cyclin-dependent kinase inhibitors—CDKN1A rs3176336: OR (TT/AA) = 1.25 (95% CI 1.11–1.42) P = 0.0026; CDKN1B rs34330: OR (TT/CC) = 1.22 (95% CI 1.02–1.47) P = 0.013 and the region of CDKN2A/2B rs3731239: OR (CC/TT) = 0.90 (95% CI 0.79–1.03) P = 0.013 and rs3218005 OR (GG/AA) = 1.55 (95% CI 1.02–2.37) P = 0.013 (P-values unadjusted for multiple testing). We were able to exclude the D-type cyclins, cyclin-dependent kinases, CDKN2C and CDKN2D from having any significantly associated risk with breast cancer in our study population. The combined effects of the cell cycle genes considered here provide evidence for a significant association with breast cancer risk in a global test (P-heterogeneity = 0.010, P-trend = 0.048). Further large-scale studies are needed to confirm these results.

Introduction

Breast cancer tends to run in families, and twin studies suggest that most of the excess familial risk is attributable to genetic factors (1), (reviewed in ref. 2). Some of this familial risk is explained by high-risk susceptibility genes, notably BRCA1 and BRCA2 (3). Linkage studies suggest that other high-risk genes are unlikely to exist, (2) and most of the familial aggregation is likely to be due to common lower risk alleles. Lower risk susceptibility alleles in ATM, CHEK2, BRIP1, PALB2 and CASP8 (4-11) have been identified, and more recently six new susceptibility loci have been detected through genome-wide studies (12-14). In total, the known susceptibility alleles only explain about 25% of the familial risk of the disease, strongly suggesting that further loci remain to be identified (2,15).

Failures in DNA repair and the subsequent uncontrolled proliferation of cells with damaged DNA both contribute to tumourigenesis (16). Since the cell cycle governs the proliferation and growth of cells, germ-line alterations in the cell cycle genes and their products could predispose to tumours. Here, we consider key genes involved in the cell cycle pathway and examine whether common alleles confer a risk of breast cancer.

The cell cycle pathway has been extensively reviewed (16-19). Germ-line and somatic mutations within the genes (20-22) and changes in expression of their protein products (23-26) have been reported in several cancers. The cell cycle can be broken down into distinct phases (27). This study focuses on the proteins involved in the G1 to S transition, which are concerned with surmounting the ‘restriction point’ (R) (Figure 1): a critical period within the cell cycle at which cells shift from senescence to becoming committed to DNA replication and cell growth (28). The retinoblastoma (RB) protein, a key regulator within the cell cycle, is at the centre of this phase. It is not considered here, as we have previously reported a comprehensive single-nucleotide polymorphism (SNP)-tagging approach on the RB1 gene, with two SNPs (rs2854344 and rs198580) showing a differential effect on breast cancer risk (29).

Fig. 1.

Fig. 1

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Proteins involved in the G1to S transition of the cell cycle to overcome the restriction point (R) (adapted from ref. 31). The genes examined in this study correspond to the proteins highlighted in grey. P, phosphorylation; −ve, inhibits CDK–cyclin complex; +ve, aids CDK–cyclin complex

Progression through the cell cycle is governed by the activation of cyclin-dependent kinases (CDKs), a group of serine/threonine kinases that form active heterotrimeric complexes when bound to their regulatory cyclin subunits (18). Sequential activation of CDK–cyclin complexes leads to the phosphorylation and inactivation of RB, allowing transcription of cell cycle genes by the E2F family of transcription factors (named for their ability to bind a sequence in the adenovirus E2 promoter) (30). This process is kept in check by cyclin-dependent kinase inhibitors (CKIs). These are known under various names, but are generally classified into two groups: The first group is known as inhibitors of kinase 4 (INK4), which include CDKN2A (INK4A/p16 and ARF/p14), CDKN2B (INK4B/p15), CDKN2C (INK4C/p18) and CDKN2D (INK4D/p19). These bind to both CDK4 and CDK6 to prevent their association with cyclin-D. The second group is commonly known as cyclin-dependent kinase inhibitory protein/kinase inhibitor protein (CIP/KIP): CDKN1A (WAF1/p21/CIP1) and CDKN1B (KIP1/p27), which form heterotrimeric complexes with the G1 to S transition CDKs. The CIP/KIP proteins do not affect cyclin binding. At low concentrations, they have been shown to improve complexing between CDK4/6 and Cyclin-D, but still inhibit CDK2–Cyclin-E (reviewed in ref. 31).

The purpose of this study was to ascertain whether common polymorphisms in the cell cycle genes are associated with breast cancer risk. We used a case–control study design and a comprehensive SNP tagging approach to capture the common variants and test them for association with breast cancer risk.

Materials and methods

Patients and controls

Cases were drawn from the SEARCH (breast) study, an ongoing population-based study, with cases ascertained through the Eastern Cancer Registration and Information Centre (formerly Eastern Cancer Registry). The study commenced in 1996 and to maximize recruitment in the initial phase all women diagnosed with invasive breast cancer below age 55 years since 1991 and still alive in 1996 (prevalent cases, median age 48 years) were invited to participate. This increased the number of younger cases where the genetic contribution to the disease is predicted to be higher. Between 1996 and the present, all women diagnosed below age 70 years (incident cases, median age 54 years) were eligible to take part. Sixty-four percent of eligible breast cancer patients returned a questionnaire and provided a blood sample for DNA analysis. Controls were randomly selected from the Norfolk component of European Prospective Investigation of Cancer (EPIC). EPIC is a prospective study of diet and cancer being carried out in nine European countries. The EPIC-Norfolk cohort comprises 25 000 individuals resident in Norfolk, East Anglia—the same region from which the cases have been recruited. Controls were not matched to cases, but were broadly similar in age at blood draw (aged 42–81 years, median age 63 years). Basic epidemiological details of the breast cancer cases and controls have previously been reported in ref. (32). The ethnic background of both cases and controls, as reported on the questionnaires, was similar with >98% being white. The study was approved by the Eastern Region Multicentre Research Ethics Committee, and all subjects provided written informed consent.

The total number of cases available for analysis was 4470, of whom 27% were prevalent cases. The samples were split into two sets in order to save DNA and reduce genotyping costs: the first set (set 1 n = 2270 cases and 2280 controls) was genotyped for all SNPs and the second set (set 2 n = 2200 cases and 2280 controls) was then tested for those SNPs that showed marginally significant associations in set 1 (P-heterogeneity or P-trend < 0.1). Cases were randomly selected for set 1 from the first 3500 recruited, with set 2 comprising the remainder of these plus the next 970 incident cases recruited. As the prevalent cases were recruited first, the proportion of these is higher in set 1 than set 2 (33% versus 20%). Median age at diagnosis was similar in both sets (51 and 52 years old, respectively). There were no significant differences in the morphology, histopathological grade or clinical stage of the cases by set or by prevalent/incident status.

Power

The statistical power of the study depends on the susceptibility allele frequency, the risks conferred and the genetic mode of action (dominant, recessive, co-dominant). The staged approach substantially reduces genotyping costs without significantly affecting statistical power—a comparison is shown in Supplementary Table I (supplementary data are available at Carcinogenesis Online). For example, assuming that the causative SNP is tagged with r2 = 0.8, a type I error rate of 0.0001 and genotyping success rate of 0.95, the staged study has 86% (full = 88%) power to detect a dominant allele with a minor allele frequency (MAF) of 0.05 that confers a relative risk of 1.5. Power to detect a dominant allele with MAF of 0.25 that confers a relative risk of 1.3 is 87% (full = 89%). Power to detect recessive alleles conferring the same relative risk is less—53% in the staged study (full = 60%) for an allele with MAF of 0.25 and risk 1.5 and 71% (full = 75%) for an allele with MAF 0.5 and risk 1.3.

Selection of tagging SNPs

Where possible, the tag SNPs were identified from data obtained from the National Institute of Environmental Health Sciences (NIEHS) Environmental Genome Project (EGP). The EGP is currently re-sequencing candidate genes for cancer across a panel of 90/95 individuals representative of USA ethnicities. The original panel (P1-PDR90) of 90 individuals consists of 24 European Americans, 24 African-Americans, 12 Mexican Americans, 6 Native Americans and 24 Asian-Americans, but the ethnic group identifiers are not available. Because it is known that there is greater genetic and haplotype diversity in individuals of African origin, we have identified and excluded 28 of the samples with the greatest African ancestry in this population by comparing the genotypes of the re-sequenced samples with genotypes for the same SNPs from the National Heart, Lung and Blood Institute Variation Discovery Resource Project African-American panel (http://pga.gs.washington.edu/finished_genes.html). Data from the remaining 62 individuals were used to identify tag SNPs. Ideally, samples from Native American, Hispanic American and Asian-American individuals should also be removed, but there is less genetic diversity between these groups and they cannot be excluded with any certainty.

When NIEHS re-sequencing data were not available, we used data from the International HapMap Project (http://www.hapmap.org, release #34/5 21-06-2005: last public release used in this study) to select tag SNPs. The HapMap Project has genotyped a large number of randomly selected tag SNPs in several populations including 30 parent–offspring trios collected by the Centre d'Etude du Polymorphisme Humain in 1980 from residents in Utah, USA with northern and western European ancestry (CEU population). Tag SNPs selected using HapMap data are thought to capture most of the known common variation with good efficiency (33).

The efficiency with which a SNP is tagged depends on the linkage disequilibrium (LD) between itself and the tag SNP as measured by the pairwise correlation coefficient (rp2). We aimed to define a set of tag SNPs where all known common variants (MAF ≥ 0.05) had an estimated rp2 > 0.8 with at least one tag SNP. Some SNPs are poorly correlated with other single SNPs but may be efficiently tagged by a haplotype defined by multiple SNPs (multimarker tags), reducing the number of tag SNPs needed. Therefore, we aimed for the correlation between each SNP and a haplotype of tagging SNPs to be rs2 > 0.8. Tag SNPs were selected using the programme Tagger (http://www.broad.mit.edu/mpg/tagger/), which combines the simplicity of pairwise methods with the potential efficiency of multimarker approaches (34).

Taqman genotyping

Genotyping was carried out using Taqman® according to the manufacturer's instructions. Primers and FAM/VIC-labelled probes were supplied directly by (Applied Biosystems, Warrington, UK) as Assays-by-Design™. All assays were carried out in 384-well plates. Each plate included negative controls (with no DNA) and positive controls duplicated on a separate quality control plate. Plates were read on the ABI Prism 7900 using the Sequence Detection Software (Applied Biosystems). Failed genotypes were not repeated. Assays in which the genotypes of duplicate samples did not show >98% concordance were discarded and replaced with alternative assays with the same tagging properties. Call rates for each assay were above 95%.

Statistical methods

For each SNP, deviation of genotype frequencies in controls from the Hardy–Weinberg equilibrium was assessed by a χ2 test with one degree of freedom (df). Genotype frequencies in cases and controls were compared by χ2 test for heterogeneity (2df) and test for trend (1df). Genotype distributions were also compared between prevalent and incident cases and between subjects in set 1 and set 2 with χ2 tests (2df). No statistically significant differences were found (data not shown), and therefore, the results were combined.

In addition to the univariate analyses, we carried out specific haplotype tests for those combinations of alleles (multimarker tags) that tagged specific SNPs. We also carried out a general comparison of common haplotype frequencies in each gene haplotype block utilizing the data from all the tag SNPs in that block. Haplotype blocks were defined such that the common haplotypes (>5% frequency) accounted for at least 80% of the haplotype diversity. Rare haplotypes (<5%) were pooled. For both specific multimarker tests and the general comparison of haplotype frequencies by haplotype block, haplotype frequencies and subject-specific expected haplotype indicators were calculated separately for each study using the programme TagSNPs (35). This implements an expectation substitution approach to account for haplotype uncertainty given unphased genotype data. Subjects missing >50% genotype data in each block were excluded from haplotype analysis. Haplotype-specific odds ratios (ORs) were estimated with their associated confidence intervals (CIs) using unconditional logistic regression. We also used unconditional logistic regression to test the association of specific tag SNP combinations (multimarkers), generated by the Tagger program, with breast cancer risk.

To obtain an overall test of whether there were more associations than would be expected under the global null hypothesis of no association, we used the admixture maximum likelihood (AML) test (36). In brief, the AML method formulates the alternative hypothesis in terms of the proportion (unknown) of SNPs that are associated (α) and the effect size when an association exists (η). The parameters α and η can be estimated by maximum pseudo-likelihood, assuming that the SNPs are independent and a likelihood ratio statistic derived. Although some SNPs are correlated, the significance of the test statistic can be assessed empirically by random permutation of cases and controls; in this study, we used 10 000 permutations.

Results

We genotyped 79 tag SNPs, representing 240 common SNPs in 13 genes within the cell cycle pathway. Details of the SNP tagging process are summarized in Table I. All tag SNPs were genotyped in set 1 (2270 cases and 2280 controls), and the genotype-specific risks are shown in Table II (genotype frequencies for cases and controls are shown in Supplementary Table II, supplementary data are available at Carcinogenesis Online). Five (6.3%) of the 79 genotype distributions in controls showed minor departure from Hardy–Weinberg (P < 0.05). These are included in the data set as visual inspection of clustering showed good quality genotyping. Fifteen tag SNPs with a P-value < 0.1 for either heterogeneity or trend test were eligible to be genotyped in set 2, consisting of a further 2200 cases and 2280 controls. The combined results (set 1 and set 2) are also shown in Table II highlighted in grey. Twelve common SNPs located in CCND2 and CDK6 were tagged by a combination of tag SNPs (multimarker tags) rather than an individual SNP tag. There were no significant associations between these multi-marker tags and breast cancer risk (Supplementary Table III, supplementary data are available at Carcinogenesis Online).

Table I. Details of tag SNPs selection for cell cycle genes.

Gene Size (kb) Data Percentage of
gene re-sequenced
by EGP		 No. of
common
variants		 No. of
tag SNPs
genotyped		 Additional
SNPs
genotypeda 		Common
variants
tagged
rp2 > 0.8		 Common
variants
tagged
rp2 > 0.5		 Common
variants
tagged
rs2 > 0.8		 LD
blocks
CCND1  16 EGP 100 13b  5 2 10 (76.9%) 10 (76.9%) 10 (76.9%) 1
CCND2  32 (40 kb file)c HapMap NA 30 14 0 19 (63.3%) 25 (83.3%) 23 (76.7%) 2
CCND3   9 EGP  88 13  7 0 12 (92.3%) 12 (92.3%) 12 (92.3%) 1
CCNE1  13.5 EGP  84 12d  3 1  5 (41.7%)  7 (58.3%)  5 (41.7%) 1
CDK2   7.6 EGP  98  6  2 0  2 (33.3%)  2 (33.3%)  2 (33.3%) 1
CDK4   6.7 EGP  96  5  2 0  4 (80%)  4 (80%)  4 (80%) 1
CDK6 220.8 HapMap NA 88 13 0 68 (77.3%) 83 (94.3%) 76 (86.4%) 3
CDKN1A  10.9 EGP  85 27  9e 1 17 (63.0%) 22 (81.5%) 17 (63.0%) 3
CDKN1B   5.7 (10 kb file)c HapMap 100  7  7 1  7 (100%)  7 (100%)  7 (100%) 1
CDKN2A (2B)f  28.8 (13.3) EGP  81 (94) 33 13 3 32 (97%) 32 (97%) 32 (97%) 1
CDKN2C   8.9 EGP/HapMap  87  3  2 0  3 (100%)  3 (100%)  3 (100%) 1
CDKN2D   4.1 EGP  63  3  2 0  3 (100%)  3 (100%)  3 (100%) 1
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NA, not applicable.

a

Additional SNPs genotyped that were not common in the data set used for tagging. These SNPs do not contribute to the estimates of tagging efficiency

b

One common SNP in EGP (rs3212867) was monomorphic in the CEU HapMap population.

c

File size actually used for tagging process.

d

Eleven SNPs in LD in EGP had a MAF of 0 in the CEU HapMap population, so were not considered in our population.

e

Assay for rs3176359 was too rare in our population (MAF 5 0.002).

f

Genes CDKN2A and CDKN2B were considered together as they lie in a single LD block on 9p21.

Table II. Breast Cancer risks associated with the tag SNPs examined in this study.

Gene and SNPs MAF OR (95% CI)
(heterozygotes
versus common
homozygotes)		 OR (95% CI)
(rare homozygotes
versus common
homozygotes)		 Heterogeneity,
P-value (2df)		 Trend test,
P-value (1 df)
CCND1
 rs602652 (07) 0.48 0.93 (0.81–1.07) 0.97 (0.83–1.15) 0.599 0.713
 rs3862792 (02)a 0.03 1.07 (0.83–1.39)  1.07 (0.07–17.14) 0.871 0.600
 rs603965 (03) 0.46 0.92 (0.80–1.06) 0.90 (0.76–1.07) 0.386 0.205
 rs3212879 (08) 0.46 1.03 (0.90–1.18) 1.09 (0.92–1.29) 0.612 0.329
 rs3212891 (10) 0.43 1.01 (0.88–1.15) 1.09 (0.92–1.29) 0.559 0.371
 rs678653 (04) 0.35 1.06 (0.97–1.16) 1.14 (0.99–1.30) 0.136 0.046
 rs7178 (01)a 0.07 0.99 (0.83–1.18) 1.10 (0.49–2.45) 0.969 0.994
CCND2
 rs3217795 (01) 0.10 1.01 (0.87–1.17) 1.21 (0.64–2.29) 0.833 0.752
 rs3217805 (02) 0.40 0.87 (0.77–1.00) 0.93 (0.77–1.11) 0.129 0.180
 rs3217820 (03) 0.38 1.05 (0.92–1.19) 0.90 (0.75–1.09) 0.255 0.573
 rs3217852 (09) 0.24 0.95 (0.84–1.08) 0.91 (0.71–1.17) 0.615 0.324
 rs3217862 (10) 0.16 1.01 (0.89–1.16) 1.28 (0.90–1.82) 0.379 0.355
 rs3217863 (11) 0.07 1.02 (0.86–1.20) 1.17 (0.45–3.04) 0.934 0.779
 rs3217869 (05) 0.38 1.14 (1.00–1.29) 1.08 (0.90–1.29) 0.141 0.183
 rs3217901 (18) 0.43 1.01 (0.89–1.15) 0.90 (0.76–1.07) 0.351 0.317
 rs3217906 (14) 0.26 0.97 (0.85–1.10) 0.93 (0.74–1.18) 0.781 0.483
 rs3217916 (15) 0.27 1.02 (0.90–1.15) 1.02 (0.81–1.29) 0.952 0.764
 rs3217925 (16) 0.25 1.04 (0.92–1.18) 1.00 (0.78–1.28) 0.796 0.678
 rs3217926 (06) 0.40 1.06 (0.94–1.21) 0.89 (0.74–1.06) 0.101 0.404
 rs3217933 (17) 0.26 1.00 (0.88–1.13) 0.91 (0.72–1.16) 0.751 0.620
 rs3217936 (07) 0.32 1.04 (0.92–1.18) 1.11 (0.91–1.35) 0.570 0.293
CCND3
 rs3218114 (08) 0.17 1.07 (0.94–1.22) 0.88 (0.60–1.27) 0.445 0.673
 rs3218110 (07) 0.25 1.08 (0.95–1.22) 0.99 (0.78–1.26) 0.488 0.488
 rs9529 (06) 0.28 0.90 (0.79–1.01) 0.98 (0.78–1.24) 0.206 0.239
 rs1051130 (03)b 0.45 0.93 (0.82–1.07) 0.92 (0.78–1.08) 0.507 0.282
 rs2479717 (02) 0.27 0.90 (0.80–1.02) 0.96 (0.76–1.22) 0.264 0.217
 rs3218092 (04) 0.17 1.02 (0.90–1.17) 0.87 (0.61–1.25) 0.690 0.914
 rs1410492 (01) 0.27 0.97 (0.86–1.10) 0.88 (0.70–1.11) 0.554 0.321
CCNE1
 rs997669 (02) 0.39 1.13 (1.03–1.24) 1.18 (1.04–1.34) 0.008 0.003
 rs3218036 (12) 0.32 1.09 (0.96–1.23)) 1.12 (0.92–1.36) 0.326 0.150
 rs3218038 (05) 0.04 0.96 (0.77–1.21) 103.75 (0.00–37209)   0.573 0.836
 rs3218076 (08)a 0.27 1.06 (0.94–1.20) 0.82 (0.65-1.05) 0.112 0.647
CDK2
 rs2069408 (02) 0.33 1.07 (0.94–1.21) 0.96 (0.79-1.17) 0.447 0.866
 rs1045435 (04) 0.08 0.99 (0.84–1.17) 1.30 (0.67–2.51) 0.731 0.820
CDK4
 rs2270777 (01) 0.43 1.14 (0.99–1.31) 1.08 (0.91–1.29) 0.169 0.233
 rs2069506 (02)b 0.34 1.01 (0.88–1.15) 0.86 (0.71–1.05) 0.299 0.285
CDK6
 rs8179 (09)b 0.20 1.13 (0.99–1.28) 0.95 (0.71–1.26) 0.156 0.294
 rs2285332 (12) 0.25 0.91 (0.80–1.03) 1.08 (0.84–1.37) 0.216 0.580
 rs42046 (08) 0.26 1.08 (0.96–1.22) 1.03 (0.82–1.31) 0.454 0.335
 rs3731348 (11) 0.05 1.16 (0.96–1.40) 0.79 (0.18–3.55) 0.292 0.157
 rs2237570 (13) 0.11 0.94 (0.85–1.05) 0.92 (0.64–1.31) 0.512 0.251
 rs3731343 (01) 0.47 0.94 (0.82–1.08) 0.94 (0.80–1.12) 0.665 0.469
 rs3757823 (02) 0.09 1.09 (0.98–1.22) 1.24 (0.76–2.02) 0.211 0.079
 rs2079147 (03) 0.46 0.99 (0.87–1.14) 1.04 (0.88–1.23) 0.801 0.639
 rs2282991 (10) 0.10 0.91 (0.81–1.02) 0.90 (0.58–1.40) 0.243 0.100
 rs4729049 (04) 0.09 1.00 (0.85–1.17) 0.73 (0.37–1.44) 0.655 0.703
 rs8 (05)b 0.21 0.92 (0.81–1.04) 1.20 (0.88–1.65) 0.168 0.719
 rs445 (06) 0.10 1.04 (0.89–1.21) 0.93 (0.54–1.62) 0.861 0.758
 rs992519 (07) 0.14 0.91 (0.80–1.05) 1.31 (0.84–2.02) 0.188 0.631
CDKN1A
 rs762624 (07) 0.27 1.02 (0.93–1.11) 1.22 (1.04–1.44) 0.051 0.063
 rs2395655 (06) 0.39 0.99 (0.87–1.12) 1.11 (0.93–1.32) 0.393 0.372
 rs3176326 (17) 0.21 0.98 (0.87–1.11) 1.13 (0.83–1.53) 0.676 0.811
 rs3176331 (11) 0.13 0.97 (0.84–1.12) 1.13 (0.70–1.81) 0.807 0.927
 rs3176336 (12) 0.39 1.01 (0.92–1.11) 1.25 (1.11–1.42) 0.00078 0.0026
 rs3176343 (14) 0.05 1.09 (0.89–1.32) 2.10 (0.72–6.16) 0.283 0.217
 rs1801270 (01) 0.06 1.08 (0.95–1.23) 2.08 (1.18–3.68) 0.021 0.040
 rs3176352 (02) 0.26 1.04 (0.95–1.14) 1.18 (1.00–1.40) 0.124 0.063
 rs1059234 (03) 0.06 1.09 (0.90–1.32) 3.28 (1.39–7.73) 0.011 0.039
 rs6457937 (04)a 0.09 1.07 (0.91–1.25) 1.56 (0.89–2.74) 0.224 0.151
CDKN1B
 rs3759216 (04) 0.46 0.86 (0.75–0.99) 0.96 (0.81–1.13) 0.084 0.417
 rs3759217 (05) 0.13 0.91 (0.79–1.05) 1.02 (0.61–1.70) 0.405 0.262
 rs34330 (01) 0.24 1.08 (0.99–1.18) 1.22 (1.02–1.47) 0.044 0.013
 rs2066827 (02) 0.24 0.98 (0.86–1.12) 0.95 (0.72–1.25) 0.908 0.668
 rs34329 (06) 0.31 0.89 (0.79–1.01) 1.12 (0.92–1.38) 0.043 0.934
 rs3093736 (07) 0.03 0.94 (0.73–1.21) 2.08 (0.52–8.33) 0.508 0.943
 rs7330 (03) a 0.40 1.04 (0.94–1.14) 1.10 (0.97–1.24) 0.354 0.155
 rs1420023 (08) 0.11 0.96 (0.82–1.12) 0.93 (0.56–1.56) 0.842 0.559
CDKN2A/2B
 rs3731257 (2A–13) 0.25 1.10 (0.97–1.25) 0.95 (0.74–1.20) 0.232 0.514
 rs3088440 (2A–11) 0.09 0.93 (0.79–1.10) 0.70 (0.36–1.35) 0.409 0.234
 rs11515 (2A–10) 0.14 1.03 (0.89–1.18) 1.44 (0.94–2.21) 0.227 0.246
 rs3731249 (2A–12)a 0.03 0.96 (0.74–1.23) 1.04 (0.15–7.41) 0.939 0.742
 rs3731239 (2A–09) 0.37 0.86 (0.79–0.94) 0.90 (0.79–1.03) 0.005 0.013
 rs4074785(2A–14)a 0.10 0.94 (0.80–1.10) 0.71 (0.38–1.35) 0.442 0.256
 rs3731222 (2A–01)ab 0.14 0.94 (0.82–1.08) 1.52 (0.96–2.39) 0.119 0.861
 rs3731211(2A–02)b 0.28 1.00 (0.92–1.09) 1.25 (1.07–1.47) 0.018 0.059
 rs3731197 (2A–15) 0.39 0.97 (0.85–1.10) 1.08 (0.90–1.29) 0.488 0.603
 rs3218020 (2A–03) 0.34 0.98 (0.86–1.11) 1.04 (0.86–1.26) 0.823 0.855
 rs2811712(2A–04) 0.10 1.06 (0.95–1.17) 1.64 (1.11–2.42) 0.030 0.045
 rs3218012(2B–04) 0.44 0.92 (0.81–1.05) 0.96 (0.81–1.14) 0.492 0.505
 rs3218009 (2B–03) 0.12 0.99 (0.86–1.14) 1.24 (0.74–2.08) 0.701 0.832
 rs3218005 (2A–05) 0.09 1.11 (0.99–1.24) 1.55 (1.02–2.37) 0.027 0.013
 rs3217992 (2A–06) 0.37 1.04 (0.91–1.18) 1.00 (0.84–1.21) 0.837 0.796
 rs1063192 (2B–02) 0.45 0.93 (0.81–1.06) 0.94 (0.80–1.12) 0.538 0.421
CDKN2C
 rs3176459 (02) 0.33 1.02 (0.90–1.16) 1.09 (0.89–1.32) 0.698 0.431
 rs12855 (01) 0.10 0.85 (0.73–0.99) 1.19 (0.63–2.24) 0.105 0.111
CDKN2D
 rs3218222 (02) 0.23 1.10 (0.98–1.25) 1.00 (0.78–1.30) 0.284 0.290
 rs1465701 (01) 0.05 0.98 (0.81–1.20) 1.46 (0.46–4.62) 0.795 0.957
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SNPs shaded grey were taken through into set 2. The results presented here refer to the combined data for set 1 and set 2.

a

Extra SNPs genotyped that were not part of the tagging set.

b

Controls are out of Hardy–Weinberg.

The AML method (36) using all the tag SNPs assessed in this study showed evidence for a significant association with breast cancer susceptibility, compared with the global null hypothesis (P-heterogeneity = 0.010, P-trend = 0.048). Most of this effect came from eight SNPs in four genes (CCNE1, CDKN1A, CDKN1B and CDKN2A/2B) that were associated with breast cancer risk at the P < 0.05 level for either the trend test or heterogeneity test. Three SNPs within CDKN1A conferred a significantly elevated risk of breast cancer. The most strongly associated variant was rs3176336 (CDKN1A-12), (P-heterogeneity = 0.00078, P-trend = 0.0026). This had a recessive mode of action with an OR for the rare homozygote of 1.25 (95% CI 1.11–1.42) compared with the common homozygote (TT versus AA). The other two associated SNPs, rs1801270 (CDKN1A-01) and rs1059234 (CDKN1A-03), are in strong LD (rp2 = 0.97). Stepwise logistic regression including both CDKN1A-01 and CDKN1A-03 demonstrated that the associations observed with these two tag SNPs could be explained by CDKN1A-01 alone. SNP rs1801270 (CDKN1A-01) confers an increased risk of breast cancer (P-heterogeneity = 0.020, P-trend = 0.040) with an apparent recessive mode of action—OR = 2.08 (95% CI 1.18–3.68) for the rare homozygotes versus the common homozygotes (AA versus CC). One SNP in the other CIP/KIP CKI gene (CDKN1B) was also associated with an increased breast cancer risk: rs34330 (CDKN1B-01) (P-trend = 0.013) in a dose-dependent manner [minor allele per allele OR = 1.09 (95% CI 1.02–1.17)]. rs3759216 (CDKN1B-04) and rs34329 (CDKN1B-06) were not taken through to set 2, as multiple logistic regression showed they were not independent of rs34330 (CDKN1B-01). SNP rs997669 (CCNE1-02) located in CCNE1 was also significantly associated with an increased breast cancer risk, OR (GG/AA) = 1.18 (95% CI 1.04–1.34) P-trend = 0.003.

Nominally significant associations with breast cancer risk were also found for four SNPs within the region of CDKN2A/2B. These two genes were considered as one region because they lie close together on chromosome 9 in a single LD block. The strongest association was observed with rs3731239 (CDKN2A-09), in which the minor allele conferred a protective effect [OR (CC/TT) = 0.90 (95% CI 0.79–1.03) P-trend = 0.013]. The minor alleles of three additional SNPs were associated with an increased risk of breast cancer: rs3731211 (CDKN2A-02), rs2811712 (CDKN2A-04) and rs3218005 (CDKN2A-05). rs2811712 (CDKN2A-04) and rs3218005 (CDKN2A-05) are in perfect LD (rp2 = 1.0). After adjustment for rs3731239 (CDKN2A-09), rs3218005 (CDKN2A-05) (or equivalently CDKN2A-04) was of borderline significance (P = 0.051), but CDKN2A-02 was no longer significant.

We found no evidence of association with breast cancer, at the P < 0.05 level, for any of the SNPs in CDKN2C, CDKN2D, CDK2, CDK4 and CDK6 (Table II), nor any evidence of an association with any of the multimarker tags (Supplementary Table III, supplementary data are available at Carcinogenesis Online), nor any difference in the haplotype frequencies in cases and controls for SNPs in these genes (Table III).

Table III. Haplotype analysis of all tag SNPs genotyped in set 1.

Genea Haplotype Frequency P-value OR 95% CI
CCND1 h1010000 0.343 0.256
07, 02, 03, 08, 10, 04, 01 h0001110 0.296 0.038 1.105 1.006–1.214
h0001100 0.098 0.163
h0000000 0.085 0.166
h1010001 0.070 0.629
Combined rareb 0.102
CCND2 h100000 0.3496 0.636
Block 1—03, 09, 10, 11, 05, 18 h010001 0.2203 0.198
h001010 0.1536 0.274
h000011 0.1113 0.467
Combined rare 0.698
CCND2 h000100 0.3653 0.740
Block 2—14, 15, 16, 06, 17, 07 h011001 0.2276 0.752
h100010 0.1925 0.272
Combined rareb 0.239
h000010 0.0443 0.050 1.234 1.000–1.523
CCND3 h0011100 0.2515 0.211
08, 07, 06, 03, 02, 04, 01 h0000001 0.2499 0.482
h0100000 0.2352 0.221
h1001010 0.1640 0.544
Combined rareb 0.731
h0000000 0.0362 0.048 1.254 1.002–1.568
CCNE1 h0000 0.3353 0.169
02, 12, 05, 08 h1100 0.3211 0.187
h0001 0.2291 0.719
h1000 0.0763 0.479
Combined rareb 0.990
CDK2 h00 0.6091 0.806
02, 04 h10 0.3078 0.903
h01 0.0601 0.858
Combined rareb 0.847
CDK4 h10 0.4318 0.209
01, 02 h01 0.3319 0.225
h00 0.2352 0.929
Combined rareb 0.796
CDK6 h000001 0.4634 0.552
Block 1—09, 12, 08, 11,13, 01 h101000 0.2041 0.304
h010010 0.0965 0.034 0.860 0.748–0.989
h010000 0.0921 0.654
h001000 0.0629 0.676
h010100 0.0554 0.142
Combined rareb 0.741
CDK6 h000 0.5303 0.662
Block 2—02, 03, 10 h010 0.2818 0.253
h110 0.0969 0.167
h011 0.0883 0.024 0.843 0.726–0.978
Combined rareb 0.239
CDK6 h0000 0.5197 0.708
Block 3—04, 05, 06, 07 h0100 0.2049 0.754
h0010 0.1037 0.742
h0001 0.0789 0.893
h1001 0.0602 0.480
Combined rareb 0.824
CDKN1A h0000 0.5874 0.198
Block 1—07, 06, 17, 11 h1110 0.2055 0.635
h0101 0.1279 0.954
h1100 0.0551 0.346
Combined rareb 0.092
CDKN1A h0000 0.5690 0.063
Block 2—12, 14, 01, 02 h1001 0.1708 0.454
h1000 0.1609 0.948
h1111 0.0502 0.289
Combined rareb 0.047 1.238 1.003–1.529
CDKN1A h00 0.8418 0.014 0.864 0.769–0.971
Block 3—03, 04 h01 0.0942 0.182
h10 0.0587 0.050 1.199 1.000–1.438
Combined rareb 0.361
CDKN1B h10000000 0.2731 0.552
04, 05, 01, 02, 06, 07, 03, 08 h01001010 0.0927 0.186
h00100010 0.0840 0.218
h10010000 0.0816 0.356
h00101010 0.0611 0.338
h00100000 0.0576 0.323
h00000001 0.0571 0.448
Combined rareb 0.557
CDKN2A2B h1000000001010010 0.1943 0.192
CDKN2A—13, 11, 10, 12, 09, h0000100000000001 0.1682 0.724
14, 01, 02, 15, 03, 04; h0000100000001001 0.1161 0.994
CDKN2B-04, CDKN2B-03, h0000001110000001 0.0911 0.886
CDKN2A-05, 06, CDKN2B-02 h0100010010010000 0.0828 0.175
h0010000110100100 0.0660 0.741
Combined rareb 0.120
CDKN2C h00 0.5685 0.857
02, 01 h10 0.3360 0.454
h01 0.0950 0.130
Combined rareb 0.915
CDKN2D h00 0.7629 0.331
02, 01 h10 0.1865 0.289
h11 0.0502 0.954
Combined rareb 0.601
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a

SNPs used for haplotype analysis are arranged in chromosome order.

b

Rare haplotypes (<5%) were pooled.

Borderline evidence for an association with breast cancer susceptibility was observed in the variants of the D type cyclins (CCND1, CCND2 and CCND3). SNP rs678653 (CCND1-04) was associated with a marginally increased risk [OR (CC/GG) = 1.14 (95% CI 0.99–1.30) P-trend = 0.046] in combined sets 1 and 2 (Table II). The individual tag SNPs for CCND2 and CCND3 did not show any association with breast cancer risk, but both genes have haplotypes of tag SNPs associated with an elevated risk (Table III), suggesting that there may be as yet, undiscovered genetic variants associated with risk in these two genes.

None of the tested SNPs were associated with age in the controls and age-adjusted results were similar to the unadjusted results (data not shown).

Discussion

We have found some evidence that common genetic variation in the cell cycle genes is associated with breast cancer using the global AML test (P = 0.010). Specific associations were identified for tag SNPs in CCNE1, CDKN1A, CDKN1B and the region CDKN2A/2B. In total, 8 of 87 (79 tag SNPs plus 8 additionally genotyped) SNPs (9.2%) showed a significant association with breast cancer at the P ≤ 0.05 threshold. This is approximately double the number of tag SNPs we would have expected to show an association by chance alone. However, none of the individual univariate associations were highly significant, so it is not possible to be certain at this stage whether any specific associations are true positives. The most significant association (with rs3176336 in CDKN1A) reached P = 0.0026, whereas thresholds of P < 10−4 for candidate gene studies and P < 10−7 for genome-wide studies have been suggested as appropriate to overcome the multiple testing problem (2,37). It is also possible that some of the observed effect is due to effects of population stratification and/or cryptic relatedness, leading to an inflation of the test statistics, although this effect is likely to be small. Using the genomic control method, we have previously shown that there was an inflation of approximately 6% in the median chi-squared test statistic for our study (38). The P-value for the most significantly associated SNP (rs3176336) is attenuated from P = 0.0026 to P = 0.0028 after adjusting for this.

The CCNE1 protein is a cyclin, which binds to CDK2, and is involved with surpassing the restriction point (39). The significantly associated tag SNP within CCNE1 is rs997669 (CCNE1-02), which is mapped to intron 4 of the gene. This SNP also tags rs3218069 (rp2 = 0.95) and rs3218070 (rp2 = 0.95) both located in intron 11. Neither of these regions appear to be conserved across species (data from http://www.genome.ucsc.edu). We have considered conservation across species for the common alleles as it is more likely that a SNP in a conserved area will have a functionally important effect (40). The lack of conservation in this case might mean that another, as yet undiscovered variant correlated with these directly increases risk of breast cancer. Eighty-four percent of the gene footprint has been sequenced by EGP, with only the repetitive sequences unscanned, indicating that almost all the common variants in this gene will already have been identified. The number of common variants quoted in Table I is lower than that suggested by EGP as 11 of their reportedly common polymorphisms, which are all strongly correlated with one another (rp2 > 0.8), do not appear to be polymorphic in our population. HapMap (http://www.hapmap.org) shows that these SNPs have a MAF of 0 in the CEU population and this is in agreement with our own findings. Although we excluded the subjects with the most African ancestry from the downloaded EGP data, we were unable to isolate samples of European ancestry. This does not detract from the significance of rs997669, but does suggest that further re-sequencing of more samples from individuals of western European ancestry may be needed to find the functional variant and possibly identify other common variants present that were too rare in the combined EGP populations for us to include.

The other three genes with significantly associated SNPs encode CKIs. CDKN2A is an INK4 CKI, which can bind to CDK4 and CDK6, to prevent their association with the catalytic subunit cyclin-D. This in turn prevents the phosphorylation of RB, which is the first significant event within G1 of the cell cycle (reviewed in ref. 31,41). The most significantly associated tag SNP in CDKN2A from our study is rs3731239 (CDKN2A-09). The SNP is located within intron 1, but does not tag any other SNPs with rp2 > 0.8, nor is this region highly conserved (conservation score of 0.002 from http://gvs.gs.washington.edu/GVS/). Other groups have also reported links with CDKN2A variants and breast cancer. A Polish case–control study (42) looked at the CDKN2A A148T variant, also known as rs3731249 (CDKN2A-12) (43). Carriers of this variant within their study appeared to have tumours with an excess of intraductal cancers (ductal carcinoma in situ) with microinvasion. We found no association with invasive breast cancer for this variant in our study population, and we specifically excluded ductal carcinoma in situ cases from our analysis, so we were not able to confirm or refute their finding.

CDKN1B and CDKN1A are from a family of CKIs called CIP/KIP inhibitors. Their main inhibitory effect is the prevention of RB phosphorylation by CDK2–Cyclin-E (31). One SNP within exon 1 of CDKN1B, rs34330 (CDKN1B-01), is significantly associated with an increased risk of breast cancer in this study [OR (TT/CC) = 1.22 (95% CI 1.02–1.47) P-trend = 0.013]. The SNP is a singleton (i.e. not correlated with any other known SNPs) and 100% of the gene has been sequenced by EGP. If this association is confirmed, it is likely that this is the functional variant.

The most significantly associated SNP of the study was rs3176336 (CDKN1A-12), which has an apparently recessive effect. This SNP tags rs3176333 (rp2 = 0.97) and rs3176337 (rp2 = 0.81), and all three are located in intron 1 of CDKN1A. Eighty-five percent of the gene has been re-sequenced by EGP with only the repetitive sequence unscanned, indicating that the majority of polymorphisms have been identified. The SNP rs3176333 is located in a highly conserved region (Figure 2), whereas rs3176336 and rs3176337 do not appear to be positioned in regions conserved across species. This provides some evidence that rs3176333 is more likely to be the functional variant as the common allele has been selectively maintained across species. The observed increase in breast cancer risk associated with these three SNPs could be related to how the different allelic forms affect pre-mRNA splicing: an important mechanism that controls eukaryotic gene expression (44). We used the splicing rainbow (http://www.ebi.ac.uk/asd-srv/wb.cgi?method = 8), which is a tool for the identification of binding sites of splicing factors, based mainly on position scoring matrices derived from SELEX data (45). The minor alleles of these three SNPs introduce additional binding sites for hnRNP I [also called polypyrimidine tract binding protein (PTB), one of the most common intronic splicing regulators] and some serine-arginine rich splicing factors (SR proteins) (namely SRp20, SC35, SRp40, ASF/SF2), which are common exonic splicing factors, but have also shown (for some genes) to bind and act at the intronic level (46,47). These alternative splicing changes brought about by the minor allele may alter the structure of the mRNAs and their encoded proteins, resulting in alterations in function, including binding, localization, stability, phosphorylation, etc. (44).

Fig. 2.

Fig. 2

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Conservation of SNPs tagged by rs3176336 (CDKN1A-12) across other species.

A Chinese population-based study by Ma et al. (48) reported associations with breast cancer for rs34330 (CDKN1B-01), consistent with our findings. A meta-analysis combining both sets of data showed the OR of the rare homozygote to be OR (TT/CC) = 1.29 (95% CI 1.03–1.62) P = 0.024. The Forest plot is shown in Figure 3. There was no significant heterogeneity between the two studies. Their results also supported the association observed with rs1801270 (CDKN1A-01) reported here. However, a larger study carried out by the Breast Cancer Association Consortium consisting of 18 290 cases and 22 670 controls (including the data used in this study) found no overall evidence for an association with breast cancer (P-trend = 0.28), but there was evidence of heterogeneity between the studies (P = 0.009) (5). In our own study, rs1801270 was not significantly associated with breast cancer risk after adjustment for rs3176336 (CDKN1A-12). Neither of these studies genotyped rs3176336, so it is likely that further genotyping of rs3176336 by other studies is needed to confirm an association that was missed with rs1801270 (CDKN1A-01).

Fig. 3.

Fig. 3

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Meta-analysis combining data from this study (SEARCH) and a Chinese case–control study (48), to examine the association of the rare homozygote of rs34330 (CDKN1B-01) with breast cancer risk (P = 0.024). Heterogeneity P = 0.663.

Recent improvements in technology have made genome-wide association studies possible, and three such studies have now been conducted in breast cancer (12-14). Despite this, detailed candidate gene studies such as this study are still informative because they provide more detailed coverage of all the known polymorphisms in these regions. For example, we genotyped 79 tag SNPs plus 8 additional SNPs in this study across the cell cycle genes, compared with 65 tag SNPs in ref. (12). In addition, stage 1 of this study is larger than any of the genome-wide association studies, providing greater power to detect modest effects.

In conclusion, we found evidence that common genetic variation in the cell cycle genes is associated with susceptibility to breast cancer. Although no one individual SNP has demonstrated a highly significant association with breast cancer, the results of the global test suggest that multiple common variants within the cell cycle genes may each confer a small effect. This would conform to the polygenic model for cancer susceptibility, which proposes that much of the inherited risk of cancer is due to multiple risk alleles, each conferring a low to moderate risk (49). Larger studies will be needed to confirm these associations.

Supplementary Material

Supp Tables I-III

Acknowledgements

We would like to thank the Eastern Cancer Registration and Information Centre and the SEARCH team for recruitment of cases; the EPIC-Norfolk Investigators for recruitment of controls; Don Conroy, Craig Luccarini, Helen Field and Danielle Shadforth for their technical support. B.A.J.P. is a Gibb Fellow of Cancer Research, UK, D.F.E is a Cancer Research UK Principal Research Fellow and P.D.P.P. is a Senior Clinical Research Fellow.

Funding

Cancer Research United Kingdom (C20/A1639, C20/A3084, C490/A4341).

Abbreviations

AML

admixture maximum likelihood

95% CI

95% confidence interval

CDK

cyclin-dependent kinase

CKI

cyclin-dependent kinase inhibitor

CIP/KIP

cyclin-dependent kinase inhibitory protein/kinase inhibitor protein

df

degree of freedom

EGP

Environmental Genome Project

EPIC

European Prospective Investigation of Cancer

INK4

inhibitors of kinase 4

LD

linkage disequilibrium

MAF

minor allele frequency

OR

odds ratio

RB

retinoblastoma

SNP

single-nucleotide polymorphism

Footnotes

Conflict of Interest Statement: None declared.

Supplementary material

Supplementary Tables I–III can be found at http://carcin.oxfordjournals.org/

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Supplementary Materials

Supp Tables I-III
NIHMS1603-supplement-Supp_Tables_I-.doc (272KB, doc)

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