Human Molecular Genetics, 2002, Vol. 11, No. 12 1399-1407
© 2002 Oxford University Press
Variants in DNA double-strand break repair genes and breast cancer susceptibility
1Cancer Research UK Department of Oncology, 2Cancer Research UK Genetic Epidemiology Unit and 3Department of Public Health, University of Cambridge, Strangeways Research Laboratory, Worts Causeway, Cambridge CB1 8RN, UK and 4Frauenklinik der Technischen Universität München, Ismaningerstrasse 22, 81675 München, Germany
Received January 21, 2002; Accepted April 9, 2002
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSELECTRONIC DATABASESREFERENCES |
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We performed genetic association studies in a population-based breast cancer case–control study analysing polymorphisms in genes involved in homologous recombination (NBS1, RAD52, RAD51, XRCC2 and XRCC3) and non-homologous end-joining (KU70/80 and LIG4). These DNA double-strand break repair genes are candidates for breast cancer susceptibility. Genotype results were available for up to 2205 cases and 1826 controls. In the homologous recombination (HR) pathway, genotype frequencies differed between cases and controls for two polymorphisms in XRCC3; T241M (P=0.015) and IVS5 A>G at nt 17893 (P=0.008). Homozygous carriers of M241 were associated with an increased risk [odds ratio (OR) MM versus TT=1.3 (95% confidence interval (CI) 1.1–1.6)], while the rare allele of IVS5A>G was associated with a dominant protective effect [OR AG versus AA=0.8 (0.7–0.9)]. The association of a rare variant in XRCC2 (R188H) was marginally significant [P=0.07; OR HH versus RR=2.6 (1.0–6.7)]. In the non-homologous end-joining (NHEJ) pathway, a polymorphism in LIG4 (T>C at nt 1977) was associated with a decrease in breast cancer risk [P=0.09; OR CC versus TT=0.7 (0.4–1.0)]. No significant association was found for 12 other polymorphisms in the other genes studied. For XRCC3, we found evidence for four common haplotypes and four rarer ones that appear to have arisen by recombination. Two haplotypes, AGC and GGC, were associated with non-significant reductions in breast cancer risk, and the rare GAT haplotype was associated with a significantly increased risk. These data provide some evidence that variants in XRCC2 and LIG4 alter breast cancer risk, together with stronger evidence that variants of XRCC3 are associated with risk. If these results can be confirmed, understanding the functional basis should improve our understanding of the role of DNA repair in breast carcinogenesis.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSELECTRONIC DATABASESREFERENCES |
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Fewer than 2% of all breast cancer cases are due to germline mutations in the two major susceptibility genes BRCA1 and BRCA2 (1). Furthermore, these genes are thought to account for less than 20% of the excess familial risk of breast cancer (1,2), suggesting that other breast cancer susceptibility genes remain to be identified. A recent segregation analysis suggests that the remaining familial aggregation may be largely ‘polygenic’, i.e. due to a large number of alleles each contributing a small effect (3).
BRCA1 and BRCA2 are important in DNA double-strand break (DSB) repair, as are three other breast cancer susceptibility genes (ATM, TP53 and CHK2) (4). DNA DSBs are the most detrimental form of DNA damage, and are frequently induced by carcinogens such as ionizing radiation. They lead to chromosomal breakage and rearrangement – events that may result in apoptosis or tumorigenesis. Several human cancer predisposition syndromes, such as ataxia telangiectasia (AT) and Nijmegen breakage syndrome (NBS), are characterized by chromosome instability and sensitivity to DSB-causative agents.
Eukaryotic cells have developed two pathways to repair DNA DSBs (Fig. 1); the homologous recombination (HR) and the non-homologous end-joining (NHEJ) pathways. In humans, the initial step in both pathways is the recognition and signalling of DNA DSBs by a protein complex containing NBS1, MRE11 and RAD50 (5). In the NHEJ pathway, KU70 and KU80 then bind the DSB, followed by recruitment and activation of DNA-protein kinase (DNA-PK). XRCC4 and ligase IV (LIG4) are recruited by the DNA-PK holoenzyme, activated by DNA-PK-mediated phosphorylation, and then repair the break. In HR, repair involves a strand exchange reaction catalysed by RAD51 and facilitated by RAD52 through direct interaction. RAD54, a DNA-dependent ATPase, also interacts directly with RAD51 and stimulates its activity. The RAD51-related proteins RAD51B-D, XRCC2 and XRCC3 are also involved in HR, and there is a direct interaction between XRCC3 and RAD51 (4,6). Furthermore BRCA2 interacts directly with RAD51 and indirectly with BRCA1 (7). ATM (mutated in patients with AT) regulates BRCA1 and NBS1 by phosphorylation (8–10).
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Thus, common variants in genes involved in DNA DSB repair are good candidates for low-penetrance breast cancer susceptibility. The aim of this study was to test the hypothesis that variants in genes involved in DNA DSB repair confer increased susceptibility to breast cancer. In order to do this, we identified single-nucleotide polymorphisms (SNPs) in the coding sequence, intron–exon boundaries, and 5' and 3' untranslated regions (UTRs) of each gene. SNPs with a rare allele frequency greater than 5% were then tested for association in a large case–control study.
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RESULTS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSELECTRONIC DATABASESREFERENCES |
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Identification of candidate SNPs
We considered 13 genes identified as being involved in the DNA DSB repair pathway: NBS1, MRE11, RAD50, KU70, KU80, DNA-PK, LIG4, XRCC4, RAD52, RAD51, RAD54, XRCC2 and XRCC3. Experimental SNP searches were undertaken for nine of these, XRCC2, RAD54, MRE11, RAD51, RAD52, KU70, KU80, XRCC4 and LIG4, as follows. The genomic DNA sequence was available for XRCC2 (GenBank accession no. 003109) and the gene was screened by denaturing high-pressure liquid chromatography (DHPLC) and subsequent sequencing. Two common polymorphisms were found; 5'-UTR 4234 G>C and R188H (31479 G>A). A further SNP, 3'-UTR 41796 C>T, was identified from the SNP consortium database. Three-quarters of the genomic sequence of the RAD54 gene was identified by aligning the available cDNA sequence (GenBank X97795) to genomic sequence by BLAST search. This was analysed by single-strand conformation polymorphism (SSCP). One fragment in which Matsuda et al. (11) had previously described a missense mutation (G325R) in a single breast cancer patient was additionally analysed by DHPLC. No common SNPs were detected in RAD54.
When we started the study, cDNA sequence alone was available for MRE11 (GenBank NM_005590), RAD51 (GenBank D14134), RAD52 (GenBank L33262), KU70 (GenBank J04607), KU80 (GenBank NM_021141), XRCC4 (GenBank U40622) and LIG4 (GenBank NM_002312). These genes were screened for SNPs by SSCP in a set of 16 cDNA samples followed by sequencing and frequency assessment in 48 genomic DNA samples. Variants with a frequency of over 0.05 were found in KU70 (G593G G>T), RAD51 (5'-UTR 135 G>C and 5'-UTR 172 G>T), RAD52 (3'-UTR 2259 C>T) and LIG4 (D501D T>C).
No useful SNPs were found in RAD54, MRE11, KU80 and XRCC4. Of the remaining four genes, multiple common SNPs have been published for NBS1 (L34L, E185Q, D399D and P672P) and XRCC3 (5'-UTR 4541, IVS5 17893 and T241M), and consequently we did not conduct our own search. A SNP search of RAD50 had been conducted previously (H. Mohrenweiser, personal communication); four SNPs had been identified (T191L, Q826X, R884H and R1239Q). However, none of these had a frequency greater than 0.05 in a set of 72 samples from healthy ethnically diverse individuals. For the DNA-PK gene, only a cDNA sequence was available (GenBank U47077). This is a huge gene spanning over 4129 codons, and a comprehensive SNP search was beyond the scope of this study.
Genotypic-specific risks
We genotyped our case–control series for the 15 SNPs encompassing seven genes (Tables 1 and 2). The genotype frequencies were similar in both the prevalent (ABC R) and incident (ABC P) cases for all the polymorphisms examined. None of the genotype distributions for the controls differed significantly from those expected under Hardy–Weinberg equilibrium.
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Seven polymorphisms in NBS1, KU70 and RAD51 showed no significant difference between cases and a first set of controls (Table 1). All genotype-specific odds ratios (OR) were close to 1.0, with upper 95% confidence limits of 1.6 or less. The only exception was for the rare homozygote risk for RAD51 5'-UTR 135 G>C, but this was not significant [OR=2.5, 95% (confidence interval) CI 0.6–10.9]. These polymorphisms were not considered further.
Table 2 shows the results for those polymorphisms that were genotyped in the cases and two sets of controls. No significant differences in genotype frequencies between the two control series were found, apart from XRCC2 5'-UTR 4234 G>C (EPIC 1 versus EPIC 2; P=0.001). Since the two control series were drawn from available EPIC samples, this difference is almost certainly due to chance. For the risk estimates, data from the two control series were combined.
There was no significant difference in genotype frequency between cases and controls for polymorphisms in RAD52 (3'-UTR C>T at nt 2259), XRCC3 (5'-UTR A>G at nt 4541) and XRCC2 (5'-UTR G>C at nt 4234 and 3'-UTR C>T at nt 41657). We observed statistically significant differences between cases and controls for two polymorphisms in the XRCC3 gene (IVS5 17893 A>G and T241M C>T). Another marginally significant difference was found in the XRCC2 gene (R188H G>A). Both of these genes act in the HR pathway. A fourth significant result was for LIG4 D501D, which is part of the NHEJ pathway.
The XRCC3 IVS5 17893 G-allele had an apparently dominant protective effect (
2=9.8, 2 df, P=0.008): both heterozygote and homozygote G-carriers had a significantly decreased risk of developing breast cancer compared with homozygote AA-carriers (homozygote OR=0.8, 95% CI 0.7–0.9; heterozygote OR=0.8, 95% CI 0.6–1.0). In contrast, the XRCC3 T241M (18067 C>T) amino acid substitution was associated with increased risk (2=8.4, 2 df, P=0.015). Homozygote individuals for the methionine (T-allele) had a significantly increased risk compared with those homozygous for the threonine (C-allele) (OR=1.3, 95% CI 1.1–1.6, P=0.006), and the risk to heterozygotes was also marginally increased (OR=1.1, 95% CI 1.0–1.3, P=0.05). The distribution of genotypes was significantly different in cases aged less than 50 years compared with those aged 50 and over (P=0.003), suggesting that the genotypic-specific risks vary with age. In women aged less than 50, the risk to MM homozygotes was 1.4 (1.1–1.8), compared with 1.2 (0.9–1.5) in women aged over 50. In contrast, the risk to heterozygote women aged less than 50 was 1.0 (0.8–1.2), compared with 1.3 (1.1–1.5) in older women.
There was some evidence of an increased risk in XRCC2 R188H rare homozygotes (OR=2.6, 95% CI 1.0–6.7). However, there was no evidence of an increased risk in heterozygotes (OR=0.9, 95% CI 0.8–1.1), and the overall comparison of genotype frequencies was non-significant. The genotype distribution in cases did not vary with age (P=0.57). Homozygotes for the LIG4 D501D (at nt 1977 T>C) C-allele were associated with a significantly decreased breast cancer risk (OR=0.7; 95% CI 0.4–1.0, P=0.04, 1 df), whilst the risk to heterozygote carriers did not vary from unity (OR 0.9, 95% CI 0.8–1.1). Again the overall effect was not significant and the genotype distribution in cases did not vary with age (P=0.95).
Haplotype risks
For four of the genes studied (NBS1, RAD51, XRCC2 and XRCC3), we have genotyped more than one SNP. We estimated haplotype frequencies in cases and controls and haplotype-specific odds ratios. We estimated non-zero frequencies for 8 of 16 possible haplotypes in NBS1, 4 of 4 in RAD51, 4 of 9 in XRCC2 and 8 of 9 in XRCC3 (Table 3). For NBS1, RAD51 and XRCC2, there were no significant differences in the estimated haplotypes frequencies between cases and controls. For XRCC3, we found evidence for four common haplotypes and four rarer ones that appear to have arisen by recombination. Two of the haplotypes were associated with non-significant reductions in breast cancer risks: AGC (OR=0.9, 95% CI 0.7–1.1) and GGC (OR=0.4, 95% CI 0.2–1.3). These risks are consistent with the fact that these two haplotypes together carry over 98% of the IVS5 G-alleles, which we have already shown to be associated with a reduction in risk. The rare GAT haplotype was associated with a significantly increased risk (OR=4.2, 95% CI 1.7–10). We observed this haplotype homozygously in three cases but no controls, and it was carried heterozygously in an estimated 38.2 cases and 9.7 controls.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSELECTRONIC DATABASESREFERENCES |
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For each gene in the HR and NHEJ pathways, we have attempted to identify at least one (and preferably more) SNPs in the coding sequence, intron–exon boundaries, and 5' and 3'-UTRs with an allele frequency greater than 0.05. SNPs that alter the amino acid sequence of the gene were of most interest, since these clearly have the potential to be functional. However, we also examined other non-coding SNPs of suitable frequency. Our rationale was that any potentially functional variants, even if not directly assayed, are likely to be in linkage disequilibrium (LD) with the typed SNPs. Where we have been able to identify more than one SNP per gene, we have used them to generate haplotypes. The power to detect an association by LD is dependent on the closeness in frequency between the functional variant and the observed marker allele or haplotype in LD with it. Generating haplotypes with multiple polymorphisms reduces the possibility that a true association will be missed.
We have estimated the breast cancer risks associated with 15 polymorphisms in seven genes using a case–control study design. Statistically significant results were found for two polymorphisms in XRCC3 (IVS5 and T241M), with some suggestion of an association for XRCC2 (R188H) and LIG4 (D501D). These variants have not previously been associated with breast cancer risk, but XRCC3 T241M has been shown to be associated with risk of malignant melanoma (12). Although the estimated relative risks were quite modest, we observed a stronger effect by considering XRCC3 haplotypes, with one haplotype (GAT) associated with a 4-fold risk over other haplotypes. If confirmed, this haplotype would account for 3.6% of the excess familial risk of breast cancer and would be associated with a population-attributable fraction of 2%.
The possibility of false positives (type I statistical errors) must be considered. We have performed 15 separate tests of significance, and we might therefore expect one significant result at the P=0.05 level. For XRCC2 and LIG4, there was no significant difference in the overall genotype frequency between cases and controls (P=0.07 and 0.09 respectively; Table 2). The rare homozygote risks for these polymorphisms were both significantly different from unity, in a one-degree-of-freedom test, but both were based on a small number of rare homozygotes and the significance level associated with each was marginal. Thus, these results require confirmation in other population samples. The observed associations for the two polymorphisms in XRCC3 are more highly significant (P=0.008 and 0.015) but may still be type I errors.
There is little evidence that any of these polymorphisms alter protein function. The T241M polymorphism in XRCC3 changes the amino acid from a neutral hydrophilic residue with a hydroxyl group to a hydrophobic one with a methyl sulfur group. This may result in a substantial change in protein structure and function, but no functional studies to confirm this have yet been published. The XRCC3 IVS5 A>G polymorphism is non-coding, the XRCC2 R188H polymorphism is a conservative amino acid change that does not lie in a known functional domain (13), while the LIG4 polymorphism (D501D) does not change the amino acid sequence. Moreover, the effect observed for the XRCC3 GAT haplotype was stronger than for any individual polymorphisms. These observations suggest that the association is likely to be due to another functional variant (or variants) in LD with the polymorphisms tested.
Despite the fact that we have used a variety of techniques to identify other polymorphisms in these genes, further work is still required to identify other functional variants in them. Our physical searches for SNPs mostly focused on the coding regions of genes and used SSCP, which has a detection rate of about 60% for single base substitutions. We did not attempt to identify polymorphisms in regulatory regions and promotors outside the cDNA sequence. We purposely chose not to investigate rare variants (frequency <0.05), because even our large case–control study would lack the power to demonstrate a moderate increase in risk.
It is possible that the risks observed for some polymorphisms are the result of gene–gene interaction. We have not attempted to assess such effects, because the number of individuals that are double rare homozygotes for any two of the at-risk polymorphisms is small, and the estimate of an interaction effect will be unreliable. Very large sample sizes are needed for this type of analysis.
This study has enabled us to exclude the possibility that all the common variants we could identify in NBS1, KU70, RAD51 and RAD52 increase the risk of breast cancer by a factor greater than 1.4. However, this does not rule out the possibility that other rare variants are more strongly associated with risk. In addition, it provides preliminary evidence that variants in XRCC2 increase and those in LIG4 decrease breast cancer risk, together with stronger evidence that variants of XRCC3 are associated with risk. If these results can be confirmed, then understanding the functional basis should improve our understanding of the role of DNA repair in breast carcinogenesis.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSELECTRONIC DATABASESREFERENCES |
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Identification of SNPs from the NHEJ and HR pathways
Candidate genes were derived from the published protein-interaction pathways for repairing DNA double strand breaks shown in Figure 1 (4,6). SNPs within genes of interest were initially identified through literature searches, SNP databases (for the URL addresses, see the electronic databases given at the end of this paper) and personal communication. If the rare allele frequency of the SNP was not given by the source, we obtained an estimate by genotyping a randomly selected set of 48 genomic DNA samples from UK Caucasian breast cancer patients.
If no suitable SNP was identified through the electronic searches, a physical search for gene variants was carried out by SSCP or DHPLC using standard protocols. Where genomic sequences were available from GenBank, all known exons and their intron- boundaries were analysed in the set of 48 samples described above. Where only cDNA sequences were available, the complete coding sequence as well as 3'- and 5'-UTRs were analysed in a set of cDNAs. This cDNA set was generated from lymphocyte cell lines of 16 breast cancer patients selected from our case–control study. In order to maximize the amount of RNA from DNA repair genes, the cell lines were arrested in S phase, and RNA was extracted and reverse-transcribed to cDNA using standard protocols. Unusual SSCP bands or DHPLC patterns, indicating the existence of a suitable SNP, were directly sequenced on an ABI PRISM 377 Sequencer (Applied Biosystems) according to manufacturer's instructions. When variants were found in cDNA, the surrounding genomic sequence was identified by a BLAST search of sequence data generated by the Sanger Centre (http://www.sanger.ac.uk) or by a global BLAST search (http://www.ncbi.nlm.nih.gov/BLAST), and allele frequencies were obtained using the set of 48 genomic DNA samples. In order to have good power to detect small relative risks, we restricted our attention to SNPs with a fre-quency greater than 5%. For dominant alleles with a frequency greater than 5%, our sample size provided at least 90% power to detect a relative risk of 1.5 significant at the 1% level.
Patients and controls
Cases were drawn from the Anglian Breast Cancer Study. This is an ongoing population-based study, with cases ascertained through the East Anglian Cancer Registry. All patients diagnosed below age 55 years since 1991 and still alive in 1996 (prevalent cases, ABC R, median age at diagnosis 48 years), together with all those under 65 years diagnosed between 1996 and the present (incident cases, ABC P, median age 52 years), were eligible to take part. Of all the potential study participants, 21% were ineligible either because they had died or because their general practitioner deemed them unfit to take part. To date, 67% of eligible patients have taken part in the study (14). All study participants completed an epidemiological questionnaire and provided a blood sample for DNA analysis. This study is approved by the relevant local research ethics committees. At the time of our analysis, samples from up to 2592 patients were available for genotyping.
Female controls (n=2016) were randomly selected from the Norfolk component of the European Prospective Investigation of Cancer (EPIC–Norfolk), a prospective study of diet and cancer being carried out in the same population from which the cases have been drawn. This cohort comprises about 25 000 individuals resident in Norfolk (East Anglia), aged 45–74 years (15). There is no evidence for population stratification in this cohort (16). The ethnic background of both cases and controls is similar, with over 99% being white Anglo-Saxon.
Genotyping
We genotyped all patient and control samples for the listed polymorphisms (Table 4) using a 5' exonuclease assay (Taqman) and the ABI PRISM 7700 sequence detection system (Applied Biosystems). TaqMan primers and probes were designed using Primer Express Oligo Design Software v1.0 (Applied Biosystems). Assays (15 µl) were carried out on 20 ng genomic DNA according to manufacturer's instructions. Amplifications were carried out on MJ Tetrad thermal cyclers (GRI) at the annealing temperatures given in Table 4. Plates were read post-PCR on the ABI PRISM 7700 sequence detector using the Allelic Discrimination Sequence Detection software (Applied Biosystems). For the software to discriminate the genotypes, we included eight no-template controls (H2O) and eight positive controls (allele-specific) in each 96-well test plate, for all SNPs. Positive controls were artificial DNA templates made by annealing together long oligonucleotides that span each SNP.
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For each SNP, TaqMan genotyping was initially carried out on all cases (ABC P and ABC R) and a subset of 960 controls (EPIC 1). Where there was evidence of a difference in genotype frequencies between cases and controls (defined by a difference in the frequency of any genotype significant at the 5% level), a more precise estimate of risk was obtained by genotyping an additional 1056 controls (EPIC 2).
Statistical methods
For each polymorphism, deviation of the genotype frequencies in the controls from those expected under Hardy–Weinberg equilibrium was assessed using the standard 2-test. Genotype frequencies in cases and controls were compared by 2-tests. The genotypic-specific risks were estimated as odds ratios (ORs) with associated 95% intervals (CIs) by unconditional logistic regression. Floated CIs were also estimated by treating ORs as floating absolute risks (17). Unless otherwise stated, the P-values presented are for the overall differences in genotype distribution between cases and controls, and have two degrees of freedom. Haplotype frequencies and hence ORs were estimated with the ‘Estimating Haplotypes’ (EH) program (18). Confidence limits for haplotype-specific ORs ratios were derived from the observed information matrix, which was derived using the standard formula for the information matrix for parameter estimates derived via the EH algorithm (19).
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ACKNOWLEDGEMENTS |
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The authors thank the EPIC management team (Sheila Bingham, Nicholas Day, Kay-Tee Khaw and Suzy Oakes) as well as the ABC study team (Victoria Basham, Jane Gregory, Patricia Harrington, Carol Houghton, Karen Redman and Joanna Ward). We thank Steve Jackson and Harvey Mohrenweiser for suggesting candidate genes and SNPs. This work was funded by the Cancer Research UK (CRUK). B.A.J.P. is a CRUK Gibb Fellow, P.P. is a CRUK Senior Clinical Research Fellow and D.F.E. is a (CRUK) Principal Research Fellow. B.K. was funded by the Deutsche Krebshilfe and A.A. was funded in part by the Academy of Finland.
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ELECTRONIC DATABASES |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSELECTRONIC DATABASESREFERENCES |
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CGAP–GAI-identified variation in genes at http://lpg.nci.gov/GAI/
HGbase at www.hgbase.interactiva.de
Nijmegen Breakage Syndrome at www.vmresearch.org/nbspoly.txt
NCBI SNP database at www.ncbi.nlm.nih.gov/SNP
Sanger Centre database at www.sanger.ac.uk
SNP Consortium at http://snp.cshl.org/data/
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FOOTNOTES |
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* To whom correspondence should be addressed. Tel:+44 1223 740166; Fax:+44 1223 411609; Email: paul.pharoah{at}srl.cam.ac.uk
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REFERENCES |
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19 Louis, T.A. (1982) Finding the observed information matrix when using the EM algorithm. J. R. Stat. Soc. B, 44, 226–233.
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Z. Zhang, J. Wan, X. Jin, T. Jin, H. Shen, D. Lu, and Z. Xia Genetic Polymorphisms in XRCC1, APE1, ADPRT, XRCC2, and XRCC3 and Risk of Chronic Benzene Poisoning in a Chinese Occupational Population Cancer Epidemiol. Biomarkers Prev., November 1, 2005; 14(11): 2614 - 2619. [Abstract] [Full Text] [PDF]
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R. C. Millikan, J. S. Player, A. R. deCotret, C.-K. Tse, and T. Keku Polymorphisms in DNA Repair Genes, Medical Exposure to Ionizing Radiation, and Breast Cancer Risk Cancer Epidemiol. Biomarkers Prev., October 1, 2005; 14(10): 2326 - 2334. [Abstract] [Full Text] [PDF]
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G. MacPherson, C. S. Healey, M. D. Teare, S. P. Balasubramanian, M. W. R. Reed, P. D. P. Pharoah, B. A. J. Ponder, M. Meuth, N. P. Bhattacharyya, and A. Cox Association of a Common Variant of the CASP8 Gene With Reduced Risk of Breast Cancer J Natl Cancer Inst, December 15, 2004; 96(24): 1866 - 1869. [Abstract] [Full Text] [PDF]
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G. J. Tranah, E. Giovannucci, J. Ma, C. Fuchs, S. E. Hankinson, and D. J. Hunter XRCC2 and XRCC3 Polymorphisms Are Not Associated with Risk of Colorectal Adenoma Cancer Epidemiol. Biomarkers Prev., June 1, 2004; 13(6): 1090 - 1091. [Full Text] [PDF]
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S. Savas, D. Y. Kim, M. F. Ahmad, M. Shariff, and H. Ozcelik Identifying Functional Genetic Variants in DNA Repair Pathway Using Protein Conservation Analysis Cancer Epidemiol. Biomarkers Prev., May 1, 2004; 13(5): 801 - 807. [Abstract] [Full Text] [PDF]
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J. C. Figueiredo, J. A. Knight, L. Briollais, I. L. Andrulis, and H. Ozcelik Polymorphisms XRCC1-R399Q and XRCC3-T241M and the Risk of Breast Cancer at the Ontario Site of the Breast Cancer Family Registry Cancer Epidemiol. Biomarkers Prev., April 1, 2004; 13(4): 583 - 591. [Abstract] [Full Text] [PDF]
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S. Wacholder, S. Chanock, M. Garcia-Closas, L. El ghormli, and N. Rothman Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies J Natl Cancer Inst, March 17, 2004; 96(6): 434 - 442. [Abstract] [Full Text] [PDF]
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J. Han, S. E. Hankinson, H. Ranu, I. De Vivo, and D. J. Hunter Polymorphisms in DNA double-strand break repair genes and breast cancer risk in the Nurses' Health Study Carcinogenesis, February 1, 2004; 25(2): 189 - 195. [Abstract] [Full Text] [PDF]
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B. Deans, C. S. Griffin, P. O'Regan, M. Jasin, and J. Thacker Homologous Recombination Deficiency Leads to Profound Genetic Instability in Cells Derived from Xrcc2-Knockout Mice Cancer Res., December 1, 2003; 63(23): 8181 - 8187. [Abstract] [Full Text] [PDF]
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T. R. Smith, E. A. Levine, N. D. Perrier, M. S. Miller, R. I. Freimanis, K. Lohman, L. D. Case, J. Xu, H. W. Mohrenweiser, and J. J. Hu DNA-Repair Genetic Polymorphisms and Breast Cancer Risk Cancer Epidemiol. Biomarkers Prev., November 1, 2003; 12(11): 1200 - 1204. [Abstract] [Full Text] [PDF]
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P. P. Medina, S. A. Ahrendt, M. Pollan, P. Fernandez, D. Sidransky, and M. Sanchez-Cespedes Screening of Homologous Recombination Gene Polymorphisms in Lung Cancer Patients Reveals an Association of the NBS1-185Gln Variant and p53 Gene Mutations Cancer Epidemiol. Biomarkers Prev., August 1, 2003; 12(8): 699 - 704. [Abstract] [Full Text] [PDF]
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G. Matullo, M. Peluso, S. Polidoro, S. Guarrera, A. Munnia, V. Krogh, G. Masala, F. Berrino, S. Panico, R. Tumino, et al. Combination of DNA Repair Gene Single Nucleotide Polymorphisms and Increased Levels of DNA Adducts in a Population-based Study Cancer Epidemiol. Biomarkers Prev., July 1, 2003; 12(7): 674 - 677. [Abstract] [Full Text] [PDF]
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N. R. Jacobsen, B. A. Nexo, A. Olsen, K. Overvad, H. Wallin, A. Tjonneland, and U. Vogel No Association between the DNA Repair Gene XRCC3 T241M Polymorphism and Risk of Skin Cancer and Breast Cancer Cancer Epidemiol. Biomarkers Prev., June 1, 2003; 12(6): 584 - 585. [Full Text] [PDF]
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Y.-P. Fu, J.-C. Yu, T.-C. Cheng, M. A. Lou, G.-C. Hsu, C.-Y. Wu, S.-T. Chen, H.-S. Wu, P.-E. Wu, and C.-Y. Shen Breast Cancer Risk Associated with Genotypic Polymorphism of the Nonhomologous End-Joining Genes: A Multigenic Study on Cancer Susceptibility Cancer Res., May 15, 2003; 63(10): 2440 - 2446. [Abstract] [Full Text] [PDF]
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S. Rafii, A. Lindblom, M. Reed, M. Meuth, and A. Cox A naturally occurring mutation in an ATP-binding domain of the recombination repair gene XRCC3 ablates its function without causing cancer susceptibility Hum. Mol. Genet., April 15, 2003; 12(8): 915 - 923. [Abstract] [Full Text] [PDF]
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U. Vogel, B. A. Nexo, A. Olsen, B. Thomsen, N. R. Jacobsen, H. Wallin, K. Overvad, and A. Tjonneland No Association Between OGG1 Ser326Cys Polymorphism and Breast Cancer Risk Cancer Epidemiol. Biomarkers Prev., February 1, 2003; 12(2): 170 - 171. [Full Text] [PDF]
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E. L. Goode, C. M. Ulrich, and J. D. Potter Polymorphisms in DNA Repair Genes and Associations with Cancer Risk Cancer Epidemiol. Biomarkers Prev., December 1, 2002; 11(12): 1513 - 1530. [Abstract] [Full Text] [PDF]
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