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  • Research article
  • Open Access

The association of copy number variation and percent mammographic density

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BMC Research Notes20158:297

  • Received: 16 September 2014
  • Accepted: 22 May 2015
  • Published:



Percent mammographic density (PD) estimates the proportion of stromal, fat, and epithelial breast tissues on the mammogram image. Adjusted for age and body mass index (BMI), PD is one of the strongest risk factors for breast cancer [1]. Inherited factors are hypothesized to explain between 30 and 60% of the variance in this trait [25]. However, previously identified common genetic variants account for less than 6% of the variance in PD, leaving much of the genetic contribution to this trait unexplained. We performed the first study to examine whether germline copy number variation (CNV) are associated with PD. Two genome-wide association studies (GWAS) of percent density conducted on the Illumina 660W-Quad were used to identify and replicate the association between candidate CNVs and PD: the Minnesota Breast Cancer Family Study (MBCFS) and controls from the Mayo Venous Thromboembolism (Mayo VTE) Case–Control Study, with 585 and 328 women, respectively. Linear models were utilized to examine the association of each probe with PD, adjusted for age, menopausal status and BMI. Segmentation was subsequently performed on the probe-level test statistics to identify candidate CNV regions that were associated with PD.


Sixty-one probes from five chromosomal regions [3q26.1 (2 regions), 8q24.22, 11p15.3, and 17q22] were significantly associated with PD in MBCFS (p-values <0.0001). A CNV at 3q26.1 showed the greatest evidence for association with PD; a region without any known SNPs. Conversely, the CNV at 17q22 was largely due to the association between SNPs and PD in the region. SNPs in the 8q24.22 region have been shown to be associated with risk of many cancers; however, SNPs in this region were not responsible for the observed CNV association. While we were unable to replicate the associations with PD, two of the five CNVs (3q26.1 and 11p15.3) were also observed in the Mayo VTE controls.


CNVs may help to explain some of the variability in PD that is currently unexplained by SNPs. While we were able to replicate the existence of two CNVs across the two GWAS studies, we were unable to replicate the associations with PD. Even so, the proximity of the identified CNV regions to loci known to be associated with breast cancer risk suggests further investigation and potentially shared genetic mechanisms underlying the PD and breast cancer association.


  • Breast density
  • Mammographic density
  • Genetics
  • Copy number variation


Percent mammographic density (PD) is an estimate of the proportion of stromal and epithelial breast tissues on the mammogram image. Adjusted for age and body mass index (BMI), PD is one of the strongest risk factors for breast cancer, and women in the highest quartile of density have a 3- to 5-fold increased risk compared to women in the lowest quartile [1]. Twin and family studies have shown that PD is highly heritable and that inherited factors are estimated to explain between 30 and 60% of the variance in this trait [25]. To date, several genetic loci or single nucleotide polymorphisms (SNPs) have been identified to be associated with percent density using genome-wide association studies (GWAS), including a novel locus on chromosome 12q24 and established breast cancer loci, ZNF365, ESR1, LSP1 and RAD51L1, suggesting shared heritability between PD and breast cancer [68]. However, together these SNPs are estimated to account for less than 6% of the variance in PD, leaving much of the genetic contribution to this trait unexplained.

Like SNPs, the deletion or amplification of segments of DNA, known as copy number variation (CNV), are common in the germline and have been implicated in the risk of diseases including neuroblastoma, cataracts, and cancer [913]. In fact, CNVs are estimated to account for 13% of the human genome [9, 14, 15]. While the mechanisms underlying the development of CNVs remain generally unknown, it has been shown that CNVs are frequently located near telomeres, centromeres, and proximal duplicated regions [9, 16, 17]. Furthermore, rare germline genomic duplications and deletions have been shown to disrupt high-penetrance tumor suppressor genes, such as the BRCA1 and BRCA2 genes, in breast cancer patients, and have been demonstrated to aggregate within families [1820]. Several recent publications have linked germline copy number variation (CNV) in other regions of the genome, including both inter- and intra-genic regions, with risk or recurrence of breast cancer [2124].

As PD has been shown to be highly heritable, we hypothesized that some of the variance not explained by associated SNPs could be due to germline CNVs. CNVs have been shown to have adequate coverage on current SNP arrays, at least for large and intermediate size CNVs (CNVs >5 kb) [17], and the size of identified deletions and amplifications in most of the prior studies with cancer ranged from intermediate (4 kb) to large (2 Mb). Therefore, using data from two independent GWAS studies, we performed the first study to examine whether CNVs are associated with PD.



Two independent studies contributed copy number and PD phenotype information. The protocol was approved by the Mayo Clinic Institutional Review Board. The first stage utilized 595 women of white European ancestry with GWAS and PD data from the Minnesota Breast Cancer Family Study (MBCFS) [6, 25, 26]. Briefly, females from 89 multigenerational families ascertained through a breast cancer proband diagnosed between 1944 and 1952 and who provided the location and consent to retrieve their mammograms were recruited to a family study of breast density. Among the 737 age-eligible women (over age 40) we retrieved the mammograms of 658 (89%). Of these, 595 women had DNA available for GWAS analyses [6].

The replication stage consisted of 336 women who were female controls within the Mayo Venous Thromboembolism Case–Control Study (Mayo VTE) [6, 27]. Clinic-based controls were prospectively selected from persons undergoing outpatient general medical examinations from 2004 to 2009 who had no previous diagnosis of VTE or superficial vein thrombosis, active cancer, antiphospholipid antibody syndrome, rheumatologic or other autoimmune disorder, or prior bone marrow or liver transplant.

Both populations were genotyped on the Illumina 660W-Quad genotyping platform, which provided information on 657,172 autosomal probes for the evaluation of CNVs.

For both studies, the mammogram closest to enrollment date was obtained and digitized on either a Lumiscan 75 scanner (MBCFS) or Array 2905HD Laser Film Digitizer (Mayo VTE). PD was estimated by the same programmer (FFW) using a computer-assisted thresholding program Cumulus [28]. For MBCFS, percent density from the mediolateral oblique and craniocaudal views were averaged and used as the primary phenotype and for Mayo VTE, only the left craniocaudal view was used. We have previously shown concordance of density from both breast sides and views [4]. Although both studies had high intrareader reliability (>0.9 for both), we acknowledge the lower PD in the Mayo VTE population that is partly due to the increased age and BMI of the women relative to MBCFS, but also due to drift in the PD measure with time. There were 5 years between evaluations of PD for these two studies. However, these two studies both identified significant associations with a SNP at chromosome 12 [6] and show similar associations with clinical characteristics (data not shown).

Statistical analysis

Log R ratio (LRR) data were extracted from the two GWAS using Genome Studio. The LRR data for each probe were median normalized per plate so that the distributions of LRR values were similar across all plates [29, 30]. PennCNV was used to extract quality-control metrics and samples were removed from further analysis if the standard deviation of the LRR >0.35, the B-Allele Frequency (BAF) drift was >0.0015, the wave factor was >0.05, or the number of CNV intervals was >500 [31].

The primary goal of the analysis was to identify copy number regions associated with PD. To do so, we first utilized the probe level data and performed probe-specific tests using linear mixed effects models for MBCFS (to account for the family design) and linear models for Mayo VTE (Flow diagram, Additional file 1) [32]. The square-root of PD was the dependent variable and probe-specific LRR values, age, inverse of body mass index (BMI) and menopausal status were included as independent variables. Second, we identified candidate copy number regions of interest by applying circular binary segmentation (CBS) to the absolute value of the probe-specific test statistics [33]. The absolute values of the probe-specific test statistics were averaged within each segment. Segments defined by three or more probes with a mean test statistic greater than one were considered for further analysis. Third, we conservatively expanded the segments by including six times the initial number of probes in the CBS identified segment both prior to the start of the segment and after the end of the segment. For example, if a segment contained ten probes, then the expanded region would add 60 probes to the start and an additional 60 probes to the end of the segment for a total size of 130 probes. Empirically this appeared to be a sufficient expanded region so as to not impact the identification of the segment of interest. Fourth, we applied permutation tests to each expanded region using 10,000 iterations. For each iteration the phenotype was permuted, the probe-specific association models (mixed effect model for MBCFS and linear model for Mayo VTE) were run, and the CBS algorithm was applied. The probe-specific test statistics for all probes within the CBS identified region were averaged and the region was assigned the mean value. For identified regions that included a significant SNP, the above modeling process was repeated including the most significant SNP and the LRR value.

P values were computed from the permutation tests and were based on how many times the observed, non-permuted test statistic, exceeded all of the permuted runs (p value = N/10,001). If there were no permuted observations greater than the observed value, then the probe was assigned the value 1/10,001. Any given probe was considered to be significantly associated with breast density if the permuted P < 1/10,000. For the replication analysis, a significance level of 0.05 was used.

Recent projections suggest that CNVs may account for 13% of the human genome and their occurrences have been cataloged in public databases such as the Toronto Database of Genomic Variants and the Genome Structural Variation Consortium CNV discovery project [9, 14, 15]. As a secondary analysis, we evaluated whether any of our primary identified regions were in this database. We then used the validation-calling algorithm within PennCNV, which is designed to call CNVs in known common CNV regions by using all the probes within a defined region and identifying the most likely copy number (0, 1, 2, 3 and 4) [31]. Association analyses (mixed effect model for MBCFS and linear model for Mayo VTE) were run to test the association of common CNVs with PD.

Each SNP (coded as 0, 1, or 2) was also evaluated using a linear model (linear mixed effects model for MBCFS) where the square-root of PD was the dependent variable and SNP, age, inverse of body mass index (BMI) and menopausal status were included as independent variables.


The evaluation of the log R ratio (LRR) standard deviation, B-allele frequency (BAF) drift and wave factor for the 595 members of the Minnesota Breast Cancer Family Study (MBCFS) resulted in exclusion of ten subjects (Additional file 2). Thus, 585 patients were analyzed to identify associations between CNV and PD, adjusted for age, BMI and menopausal status. PD was lower in the Mayo VTE population, partly due to the slightly older age and higher BMI, but also due to drift in the PD measure can occur with time (Table 1).
Table 1

Characteristics of subjects used in the discovery and replication phases



Mayo VTE


Family study

Case–control study





Age, mean (SD) (years)

57.2 (11.6)

61.0 (12.7)

BMI, mean (SD) (kg/m2)

27.1 (5.7)

28.4 (6.1)

Pre-menopausal (%)



Percent density, mean (SD)

26.6 (15.91)

14.6 (13.62)

Mammogram view

Average of CC and MLO


PD measurement software



Digitizer Software


Array 2905

MBCFS Mayo Breast Cancer Family Study and Mayo VTE venous thromboembolism Case–Control Study. CC Craniocaudal and MLO mediolateral oblique.

Analysis of the 657,172 autosomal chromosome probes from the Illumina 660W Quad identified five regions on four chromosomes [3q26.1 (contained two regions), 8q24.22, 11p15.3, and 17q22] to be significantly associated with PD after adjusting for age, menopausal status, and BMI in MBCFS (Table 2). Candidate regions identified in the initial data reduction analysis step are shown in Additional file 3. Figure 1 shows the probe-level p-values, SNP p-values, recombination rates, and neighboring genes for each of the four chromosomes. Figure 2a–d shows the LRR values for each of the four chromosomes. The two 3q26.1 regions consisted of a total of 30 probes that were significantly associated with PD in MBCFS and were clustered in a region without SNPs on the Illumina 660W platform (Figure 1a). Our algorithm detected two deleted CNV regions at 3q26.1 that were associated with PD in MBCFS. These two regions were separated by a segment defined by four probes that contains both insertions and deletions (Figure 2a). The 8q24.22 region consisted of 6 probes that were significantly associated with PD in MBCFS (Figure 1b). This region contains two genes: HHLA1 and OC90. Upon closer examination of the LRR values, it appears that a single probe (SNP) was driving the association results (Figure 2b). However, while there are SNPs in this region, none were significantly associated with PD (Figure 1b). The 11p15.3 region consisted of 17 probes that were significantly associated with PD in MBCFS (Figure 1c). These 17 probes defined a deleted region on 11p15.3 (Figure 2c). And, while there were SNPs in this region, none were significantly associated with PD (Figure 1c). The 17q22 region consisted of three probes that were significantly associated with PD in MBCFS (Figure 1d). In contrast to the other regions, the 17q22 region had SNPs that were significantly associated with PD; the most significant SNP was rs12936458 (p value = 0.00023). After adjusting for rs12936458, the CNV probes were no longer associated with PD.
Table 2

CNV Regions identified in discovery phase (MBCFS) and evaluated in replication cohort (Mayo VTE)


Start position

End position

Number significant probesa

MBCFS (discovery)

Mayo VTE (replication)


























MBCFS Mayo Breast Cancer Family Study and Mayo VTE Mayo Venous Thromboembolism Case–Control Study.

aSignificant associations defined as p < 0.0001 for discovery phase and p < 0.05 for replication.

Figure 1
Figure 1

Candidate CNVs and SNP associations with PD in the Mayo Breast Cancer Family Study (MBCFS) for a 3q26.1 [2 regions], b 8q24.22, c 11p15.3 and d 17q22. For identifying candidate CNVs that are associated with PD, we performed probe-specific tests and subsequently performed segmentation on the test-statistics. P values were computed from permutation tests and were based on how many times the observed test-statistic, exceeded the permutation test statistics (using 10,000 permutations). Red circles denote CNV probes, black dots denote SNPs, the blue line denotes recombination rate, green lines denote genes, and the grey shaded areas denote the CNV region that was observed in MBCFS.

Figure 2
Figure 2

LRR values from the Mayo Breast Cancer Family Study (MBCFS) for a 3q26.1 [2 regions], b 8q24.22, c 11p15.3, and d 17q22. Each row represents a sample and each column represents a probe.

The Mayo VTE controls were used for replication. Similar quality-control exclusions were made for the Mayo VTE controls, resulting in 328 individuals being analyzed (Table 1; Additional file 2). As demonstrated by Figure 3, the Mayo VTE controls showed similar CNVs for the two regions at 3q26.1 (Figure 3a) and the region at 11p15.3 (Figure 3c) as was observed in MBCFS. Alternative versions of these figures for both studies are shown in Additional files 4, 5, 6 and 7. While the CNVs were observed across the two datasets, we did not observe an association with PD in MBCFS. Specifically, only one probe at 3q26.1 showed a significant association with PD in the Mayo VTE controls (p < 0.0001 Table 2; Additional file 8); however, the coefficient was of the opposite direction (Additional file 9).
Figure 3
Figure 3

LRR values from the Mayo VTE control samples for a 3q26.1 (2 regions), b 8q24.22, c 11p15.3, and d 17q22. Each row represents a sample and each column represents a probe.

As mentioned previously, the two 3q26.1 regions are in an area on the Illumina 660W Quad array that only contained CNV probes (there were no SNPs in this region; Figure 1a), suggesting that this is a known CNV region. This was confirmed by the absolute, inter-valued copy number estimates for 450 HapMap samples obtained from the Genome Structural Variation Consortium CNV discovery project, where chromosome 3 (163,994,833–164,109,307) is listed [9]. This particular region showed 54 (30%) CEU HapMap samples with deletions (18 with zero copies, 36 with one copy) and 60 (34%) samples with insertions. Thus, using PennCNV thirty-four probes in this region were used to force a CNV call in both the MBCFS and Mayo VTE samples. Within MBCFS there were 181 (31%) samples with deletions (54 with zero copies, 127 with one copy) and 13 (2%) samples with insertions. The number of deletions was similar to those detected in the HapMap samples; however, the insertions were significantly less frequent in the MBCFS cohort. Comparison of samples with a deletion versus those without a deletion in MBCFS was significantly associated with PD (p = 0.005). The Mayo VTE control samples were used for replication: 10 (3%) subjects had deletions (three with zero copies, seven with one copy) and no samples had an insertion. Thus, the Mayo VTE controls did have similar number of deletions and insertions and we did not observe a significant association with PD.


Prior reports have demonstrated that PD is a heritable trait; however, to date, only a small percentage of PD variation is explained by SNPs. To determine whether CNVs account for some of the remaining variability we conducted the first analysis of CNV and PD using two previously-published GWAS datasets. In the discovery set (MBCFS) we identified five candidate regions that were significantly associated with PD: 3q26.1 [2 regions], 8q24.22, 11p15.3 and 17q22.

A CNV at 3q26.1 (163,698,399 to 163,718,292 in GRCh36/hg18; near the one we identified in the present study) has been previously reported to be associated with breast cancer risk in a Japanese population, though the start and end positions are slightly different [23]. We observed two CNVs at this region in both MBCFS and Mayo VTE control samples. However, only a single probe in the 3q26.1 region was observed to be associated in the Mayo VTE replication samples and the effect was in the opposite direction. This discrepancy could be due to the fact that the two datasets had different study designs (MBCFS is a family study and the Mayo VTE controls were obtained from a case–control study) and/or because the PD distributions are different across the two studies. Because the CNV region replicates and because an association with breast cancer risk has been previously reported [23], we suggest that further investigation needs to be undertaken to further try to replicate the result observed in MBCFS. Notably, neither the CNV region identified in the Japanese study or our study mapped to a gene [34, 35]. Lastly, it is important to note that the 3q26.1 regions would not be detected via GWAS analysis using the Illumina 660 W-Quad because there are no known SNPs in this region.

The 8q24 region has been shown to be associated with risk of many cancers, including breast, [3642]. While our algorithm detected a CNV at 8q24.22 that was associated with PD in the MBCFS samples, we were not able to replicate the results in Mayo VTE. Furthermore, upon further evaluation of the data, it appears that the results are largely being driven by a single probe. Even so, because the 8q24 region has been previously reported, we suggest that further investigation should be undertaken to replicate these results.

Our algorithm identified a CNV at 11p15.3 in MBCFS, which was validated in the Mayo VTE samples. However, we only observed a significant association with PD in the MBCFS samples. To our knowledge, this region has not been identified previously.

A CNV region at 17q22 was identified to be significantly associated with PD in MBCFS. However, we determined that the association between this region and PD was driven by known SNPs in the region. Particularly, after adjusting for the most significant SNP in this region (rs12936458) the CNV was no longer associated with PD. Even so, the 17q22-23 region has previously been implicated in breast cancer risk, and also has been shown to have copy number abnormalities and amplification in breast cancer cell lines and tumors [4345]. Additionally, CNVs significantly associated with breast cancer risk have been previously reported in the adjacent 17q21 region, at BRCA1 [18, 20]. Therefore, additional examination of 17q21-23 may elucidate the role of CNVs in breast cancer pathogenesis.

Our analysis approach involved performing statistical association tests on the probe-level data and subsequently performing segmentation on the probe-level test statistics to identify candidate regions that are associated with PD [46]. Our approach is different from the majority of CNV analyses where candidate CNVs are first identified for each sample, the candidate CNVS are subsequently grouped together to create consensus regions across all samples, and lastly, the consensus regions are tested for associations with the trait of interest [4749]. There are at least two problematic aspects of this approach. First, there are numerous CNV detection algorithms available and unfortunately, the consensus of these algorithms is very low [40]. Second, it is not a trivial task to define consensus regions across individuals. Thus, the strength of the approach used herein is that it avoids the variability associated with both identifying candidate CNV regions and defining common CNV regions across individuals. Motivation for our approach is shown in simulations presented by Breheny, who found that probe-level testing can offer a significant increase (>12-fold) in power over traditional CNV-level testing [46]. We acknowledge that probe-level testing can be computationally intensive; however, we minimized this issue by performing segmentation only in those regions where there was some indication of a statistical association. A potential weakness with our approach, as shown by Breheny, is that it may not perform as well when CNV are large and rare. Additionally, our approach is not as sensitive when there are duplications and insertions at the same location. These limitations notwithstanding, our probe-level testing approach identified candidate CNVs that were evident in both datasets, as shown in Figure 2.


In summary, we identified five candidate CNV regions [3q26.1 (2 regions), 8q24.22, 11p15.3 and 17q22] that showed evidence of an association with PD. While three of CNV regions were observed in a second dataset [3q26.1 (2 regions), 11p15.3], we were unable to replicate the associations with PD. However, these CNVs have been previously implicated in breast cancer risk as well as other malignancies. Thus, there is a possibility that they did not replicate in the Mayo VTE samples because of the different experimental design, power and/or the different PD measurements. As such, we recommend additional investigations to further examine these CNVs with PD and breast cancer in other populations to better understand genetic mechanisms by which PD may influence breast cancer risk.



B-allele frequency


body mass index


circular binary segmentation


copy number variation


genome-wide association studies


log R ratio


Minnesota breast cancer family study


percent mammographic density


single nucleotide polymorphism

Mayo VTE: 

venous thromboembolism Case–Control Study


Authors’ contributions

CV, EA and JE conceived of the study and drafted the manuscript, with significant input and revision by AG. EA, JE, and VP directed the statistical analysis. AW and CS performed the statistical analysis and TS, FC and CV designed and conducted analyses of the MBCFS cohort, including the GWAS. FFW carried out the phenotype acquisition. DR helped with the study design. JH and MdA carried out the Mayo VTE study. JM helped with study design and critical review of the manuscript. All authors read and approved the final manuscript.


We acknowledge Stanley Pounds at St. Jude Children’s Research-Hospital, Memphis, TN, USA for his assistance with the statistical methodology. We also acknowledge the women who were participants in the MBCFS and Mayo VTE studies. The authors would like to thank Dr. Steven Hart for his guidance with utilization of the ENCODE database. This work was supported by National Institutes of Health Grant R01 CA128931, Mayo Clinic Breast Cancer SPORE, P50 CA116201 and the Cancer Center Support Grant, P30 CA15083. The Mayo VTE was supported by National Heart, Lung and Blood Institute Grant HL83131; National Human Genome Research Institute grant HG04735; National Cancer Institute Grant CA92153; and Centers for Disease Control Grant DD000235.

Compliance with ethical guidelines

Competing interests The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
Wayne State University School of Medicine and Karmanos Cancer Institute, Detroit, MI, USA
Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
Division of Cardiovascular Disease, Department of Medicine, Mayo Clinic, Rochester, MN, USA
Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA


  1. McCormack VA (2006) I dSS: Breasst density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomark Prev 15(6):1159–1169View ArticleGoogle Scholar
  2. Boyd NF, Dite GS, Stone J, Gunasekara A, English DR, McCredie MR et al (2002) Heritability of mammographic density, a risk factor for breast cancer. N Engl J Med 347:886–894PubMedView ArticleGoogle Scholar
  3. Pankow JS, Vachon CM, Kuni CC, King RA, Arnett DK, Gabrick DM et al (1997) Genetic analysis of mammographic breast density in adult women: evidence of a gene effect. J Natl Cancer Inst 89(8):549–556PubMedView ArticleGoogle Scholar
  4. Vachon CM, Sellers TA, Carlson EE, Cunningham JM, Hilker CA, Smalley RL et al (2007) Strong evidence of a genetic determinant for mammographic density, a major risk factor for breast cancer. Cancer Ress 67(17):8412–8418View ArticleGoogle Scholar
  5. Ursin G, Lillie EO, Lee E, Cockburn M, Schork NJ, Cozen W et al (2009) The relative importance of genetics and environment on mammographic density. Cancer Epidemiol Biomark Prev 18(1):102–112View ArticleGoogle Scholar
  6. Stevens KN, Lindstrom S, Scott CG, Thompson D, Sellers TA, Wang X et al (2012) Identification of a novel percent mammographic density locus at 12q24. Hum Mol Genet 21(14):3299–3305PubMed CentralPubMedView ArticleGoogle Scholar
  7. Lindstrom S, Vachon CM, Li J, Varghese J, Thompson D, Warren R et al (2011) Common variants in ZNF365 are associated with both mammographic density and breast cancer risk. Nat Genet 43(3):185–187PubMed CentralPubMedView ArticleGoogle Scholar
  8. Vachon CM, Scott CG, Fasching PA, Hall P, Tamimi RM, Li J et al (2012) Common breast cancer susceptibility variants in LSP1 and RAD51L1 are associated with mammographic density measures that predict breast cancer risk. Cancer Epidemiol Biomark Prev 21(7):1156–1166View ArticleGoogle Scholar
  9. Conrad DF, Pinto D, Redon R, Feuk L, Gokcumen O, Zhang Y et al (2010) Origins and functional impact of copy number variation in the human genome. Nature 464(7289):704–712PubMed CentralPubMedView ArticleGoogle Scholar
  10. Diskin SJ, Hou C, Clessner JT, Attiyeh EF, Laudenslager M, Bosse J et al (2009) Copy number variation at 1q21.1 associated with neuroblastoma. Nature 459(7249):987–991PubMed CentralPubMedView ArticleGoogle Scholar
  11. Jiang J, Zhou J, Yao Y, Zhu R, Liang C, Jiang S et al (2013) Copy number variations of DNA repair genes and the age-related cataract: Jiangsu Eye Study. Invest Ophthalmol Vis Sci 54(2):932–938PubMedView ArticleGoogle Scholar
  12. Liu W, Sun J, Li G, Zhu Y, Zhang S, Kim ST et al (2009) Association of a germ-line copy number variation at 2p24.3 and risk for aggressive prostate cancer. Cancer Res 69(6):2176–2179PubMed CentralPubMedView ArticleGoogle Scholar
  13. Al-Sukhni W, Joe S, Lionel AC, Zwingerman N, Zogopoulos G, Marhsall CR et al (2012) Identification of germline genomic copy number variation in familial pancreatic cancer. Hum Genet 131(9):1481–1494PubMedView ArticleGoogle Scholar
  14. Iafrate AJ, Feuk L, Rivera MN, Listewnik ML, Donahoe PK, Qi Y et al (2004) Detection of large-scale variation in the human genome. Nat Genet 36(9):949–951PubMedView ArticleGoogle Scholar
  15. Stankiewicz P, Lupski JR (2010) Structural variation in the human genome and its role in disease. Annu Rev Med 61:437–455PubMedView ArticleGoogle Scholar
  16. Redon R, Ishikawa S, Fitch KR, Feuk L, Perry GH, Andrews TD et al (2006) Global variation in copy number in the human genome. Nature 444(7118):444–454PubMed CentralPubMedView ArticleGoogle Scholar
  17. McCarroll SA (2008) Extending genome-wide association studies to copy-number variation. Hum Mol Genet 17(R2):R135–R142PubMedView ArticleGoogle Scholar
  18. Petrij-Bosch A, Peelen T, van Vliet M, van Eijk R, Olmer R, Drusedau M et al (1997) BRCA1 genomic deletions are major founder mutations in Dutch breast cancer patients. Nat Genet 17(3):341–345PubMedView ArticleGoogle Scholar
  19. Casili F, Tournier I, Sinilnikova OM, Coulet F, Soubrier F, Houdayer C et al (2006) The contribution of germline rearrangements to the spectrum of BRCA2 mutations. J Med Genet 43(9):e49View ArticleGoogle Scholar
  20. Montagna M, Palma MD, Menin C, Agata S, Niclo AD, Chieco-Bianchi L et al (2003) Genomic rearrangements account for more than one-third of the BRCA1 mutations in northern Italian breast/ovarian cancer families. Hum Mol Genet 12(9):1055–1061PubMedView ArticleGoogle Scholar
  21. Long J, Delahanty RJ, Li G, Gao YT, Lu W, Cai Q et al (2013) A common deletion in the APOBEC3 genes and breast cancer risk. J Natl Cancer Inst 105(8):573–579PubMed CentralPubMedView ArticleGoogle Scholar
  22. Krepischi AC, Achatz MI, Santos EM, Costa SS, Lisboa BC, Brentani H et al (2012) Germline DNA copy number variation in familial and early-onset breast cancer. Breast Cancer Res 14(1):R24PubMed CentralPubMedView ArticleGoogle Scholar
  23. Suehiro Y, Okada T, Shikamoto N, Zhan Y, Sakai K, Okayama N et al (2013) Germline copy number variations associated with breast cancer susceptibility in a Japanese population. Tumour Biol 34(2):947–952PubMed CentralPubMedView ArticleGoogle Scholar
  24. Sapkota Y, Ghosh S, Lai R, Coe BP, Cass CE, Yasui Y et al (2013) Germline DNA copy number aberrations identified as potential prognostic factors for breast cancer recurrence. PLoS One 8(1):e53850PubMed CentralPubMedView ArticleGoogle Scholar
  25. Sellers TA, King RA, Cerhan JR, Chen PL, Grabrick DM, Kushi LH et al (1999) Fifty-year follow-up of cancer incidence in a historical cohort of Minnesota breast cancer families. Cancer Epidemiol Biomarkers Prev 8(12):1051–1057PubMedGoogle Scholar
  26. Sellers TA, Jensen LE, Vierkant RA, Fredericksen ZS, Brandt KR, Giuliano AR et al (2007) Association of diabetes with mammographic breast density and breast cancer in the Minnesota breast cancer family study. Cancer Causes Control 18(5):505–515PubMedView ArticleGoogle Scholar
  27. Heit JA, Cunningham JM, Petterson TM, Armasu SM, Rider DN, Andrade MD (2011) Genetic variation within the anticoagulant, procoagulant, fibrinolytic and innate immunity pathways as risk factors for venous thromboembolism. J Thromb Haemost 9(6):1133–1142PubMed CentralPubMedView ArticleGoogle Scholar
  28. Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ (1996) Automated analysis of mammographic densities. Phys Med Biol 41(5):909–923PubMedView ArticleGoogle Scholar
  29. Barnes C (2009) Normtools.
  30. Reese SE, Archer KJ, Therneau TM, Atkinson EJ, Vachon CM, de Andrade M (2013) A New statistic for identifying batch effects in high-throughput genomic data that uses guided principal components analysis. Bioinformatics 29(22):2877–2883PubMed CentralPubMedView ArticleGoogle Scholar
  31. Wang K, Li M, Hadley D, Liu R, Glessner J, Grant SF et al (2007) PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res 17(11):1665–1674PubMed CentralPubMedView ArticleGoogle Scholar
  32. de Andrade M, Atkinson EJ, Lunde E, Amos CI, Chen J (2006) Estimating genetic components of variance for quantitative traits in family studies using the MULTIC routines. Technical report series no. 78. Department of Health Science Research, Mayo Clinic, Rochester, Minnesota.
  33. Seshan VE, Olshen A DNAcopy: DNA copy number data analysis. R package version 1.38.1.
  34. Richards FM, Phipps ME, Latif F, Yao M, Crossey PA, Foster K et al (1993) Mapping the Von Hippel-Lindau disease tumour suppressor gene: identification of germline deletions by pulsed field gel electrophoresis. Hum Mol Genet 2(7):879–882PubMedView ArticleGoogle Scholar
  35. Thean LF, Loi C, Ho KS, Koh PK, Eu KW, Cheah PY (2010) Genome-wide scan identifies a copy number variable region at 3q26 that regulates PPM1L in APC mutation-negative familial colorectal cancer patients. Genes Chromosom Cancer 49(2):99–106PubMedGoogle Scholar
  36. Goode EL, Chenevix-Trench G, Song H, Ramus SJ, Notaridou M, Lawrenson K et al (2010) A genome-wide association study identifies susceptibility loci for ovarian cancer at 2q31 and 8q24. Nat Genet 42(10):874–879PubMed CentralPubMedView ArticleGoogle Scholar
  37. Kiemeney LA, Thorlacius S, Sulem P, Geller F, Aben KK, Stacey SN, Gudmundsson J et al (2008) Sequence variant on 8q24 confers susceptibility to urinary bladder cancer. Nat Genet 40(11):1307–1312PubMedView ArticleGoogle Scholar
  38. Zanke BW, Greenwood CM, Rangrej J, Kustra R, Tenesa A, Farrington SM et al (2007) Genome-wide association scan identifies a colorectal cancer susceptibility locus on chromosome 8q24. Nat Genet 39(8):989–994PubMedView ArticleGoogle Scholar
  39. Thomas G, Jacobs KB, Yeager M, Kraft P, Wacholder S, Orr N et al (2008) Multiple loci identified in a genome-wide association study of prostate cancer. Nat Genet 40(3):310–315PubMedView ArticleGoogle Scholar
  40. Easton DF, Pooley KA, Dunning AM, Pharoah PD, Thompson D, Ballinger DG et al (2007) Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 447(7148):1087–1093PubMed CentralPubMedView ArticleGoogle Scholar
  41. Michailidou K, Hall P, Gonzalez-Neira A, Ghoussaini M, Dennis J, Milne RL et al (2013) Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nature genetics 45(4):353–361PubMed CentralPubMedView ArticleGoogle Scholar
  42. Broeks A, Schmidt MK, Sherman ME, Couch FJ, Hopper JL, Dite GS et al (2011) Low penetrance breast cancer susceptibility loci are associated with specific breast tumor subtypes: findings from the Breast Cancer Association Consortium. Hum Mol Genet 20(16):3289–3303PubMed CentralPubMedView ArticleGoogle Scholar
  43. Wu G, Sinclair C, Hinson S, Ingle JN, Roche PC, Couch FJ (2001) Structural analysis of the 17q22-23 amplicon identifies several independent targets of amplification in breast cancer cell lines and tumors. Cancer Res 61(13):4951–4955PubMedGoogle Scholar
  44. Forozan F, Mahlamaki EH, Monni O, Chen Y, Veldman R, Jiang Y et al (2000) Comparative genomic hybridization analysis of 38 breast cancer cell lines: a basis for interpreting complementary DNA microarray data. Cancer Res 60(16):4519–4525PubMedGoogle Scholar
  45. Tirkkonen M, Tanner M, Karhu R, Kallioniemi A, Isola J, Kallioniemi OP (1998) Molecular cytogenetics of primary breast cancer by CGH. Genes Chromosom Cancer 21(3):177–184PubMedView ArticleGoogle Scholar
  46. Breheny P, Chalise P, Batzler A, Wang L, Fridley BL (2012) Genetic association studies of copy-number variation: should assignment of copy number states precede testing? PLoS One 7(4):e34262PubMed CentralPubMedView ArticleGoogle Scholar
  47. Eckel-Passow JE, Atkinson EJ, Maharjan S, Kardia SL, Andrade MD (2011) Software comparison for evaluating genomic copy number variation for Affymetrix 6.0 SNP array platform. BMC Bioinform 12:220View ArticleGoogle Scholar
  48. Winchester L, Yau C, Ragoussis J (2009) Comparing CNV detection methods for SNP arrays. Brief Funct Genomic Proteomic 8(5):353–366PubMedView ArticleGoogle Scholar
  49. Lin P, Hartz SM, Wang JC, Krueger RF, Foroud TM, Edenberg HJ et al (2011) Copy number variation accuracy in genome-wide association studies. Hum Hered 71(3):141–147PubMed CentralPubMedView ArticleGoogle Scholar


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