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Breast Cancer Research
Published in: Breast Cancer Research 1/2018

Open Access 01-12-2018 | Research article

Common genetic variation and novel loci associated with volumetric mammographic density

Authors: Judith S. Brand, Keith Humphreys, Jingmei Li, Robert Karlsson, Per Hall, Kamila Czene

Published in: Breast Cancer Research | Issue 1/2018

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Abstract

Background

Mammographic density (MD) is a strong and heritable intermediate phenotype of breast cancer, but much of its genetic variation remains unexplained.

Methods

We conducted a genetic association study of volumetric MD in a Swedish mammography screening cohort (n = 9498) to identify novel MD loci. Associations with volumetric MD phenotypes (percent dense volume, absolute dense volume, and absolute nondense volume) were estimated using linear regression adjusting for age, body mass index, menopausal status, and six principal components. We also estimated the proportion of MD variance explained by additive contributions from single-nucleotide polymorphisms (SNP-based heritability [h2SNP]) in 4948 participants of the cohort.

Results

In total, three novel MD loci were identified (at P < 5 × 10− 8): one for percent dense volume (HABP2) and two for the absolute dense volume (INHBB, LINC01483). INHBB is an established locus for ER-negative breast cancer, and HABP2 and LINC01483 represent putative new breast cancer susceptibility loci, because both loci were associated with breast cancer in available meta-analysis data including 122,977 breast cancer cases and 105,974 control subjects (P < 0.05). h2SNP (SE) estimates for percent dense, absolute dense, and nondense volume were 0.29 (0.07), 0.31 (0.07), and 0.25 (0.07), respectively. Corresponding ratios of h2SNP to previously observed narrow-sense h2 estimates in the same cohort were 0.46, 0.72, and 0.41, respectively.

Conclusions

These findings provide new insights into the genetic basis of MD and biological mechanisms linking MD to breast cancer risk. Apart from identifying three novel loci, we demonstrate that at least 25% of the MD variance is explained by common genetic variation with h2SNP/h2 ratios varying between dense and nondense MD components.
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Literature
1.
McCormack VA, dos Santos Silva I. Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomark Prev. 2006;15(6):1159–69.CrossRef
2.
Boyd NF, Rommens JM, Vogt K, Lee V, Hopper JL, Yaffe MJ, et al. Mammographic breast density as an intermediate phenotype for breast cancer. Lancet Oncol. 2005;6(10):798–808.CrossRefPubMed
3.
Lindstrom S, Vachon CM, Li J, Varghese J, Thompson D, Warren R, et al. Common variants in ZNF365 are associated with both mammographic density and breast cancer risk. Nat Genet. 2011;43(3):185–7.CrossRefPubMedPubMedCentral
4.
Stevens KN, Lindstrom S, Scott CG, Thompson D, Sellers TA, Wang X, et al. Identification of a novel percent mammographic density locus at 12q24. Hum Mol Genet. 2012;21(14):3299–305.CrossRefPubMedPubMedCentral
5.
Lindstrom S, Thompson DJ, Paterson AD, Li J, Gierach GL, Scott C, et al. Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk. Nat Commun. 2014;5:5303.CrossRefPubMedPubMedCentral
6.
Fernandez-Navarro P, Gonzalez-Neira A, Pita G, Diaz-Uriarte R, Tais Moreno L, Ederra M, et al. Genome wide association study identifies a novel putative mammographic density locus at 1q12-q21. Int J Cancer. 2015;136(10):2427–36.CrossRefPubMed
7.
Brand JS, Li J, Humphreys K, Karlsson R, Eriksson M, Ivansson E, et al. Identification of two novel mammographic density loci at 6Q25.1. Breast Cancer Res. 2015;17:75.CrossRefPubMedPubMedCentral
8.
Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ. The quantitative analysis of mammographic densities. Phys Med Biol. 1994;39(10):1629–38.CrossRefPubMed
9.
Highnam R, Brady M, Yaffe M, Karssemeijer N, Harvey J. Robust breast composition measurement - Volpara™. In: Martí J, Oliver A, Freixenet J, Martí R, editors. Digital mammography: IWDM 2010 (Lecture Notes in Computer Science series, vol. 6136). Berlin: Springer; 2010. p. 342–9.CrossRef
10.
11.
Eng A, Gallant Z, Shepherd J, McCormack V, Li J, Dowsett M, et al. Digital mammographic density and breast cancer risk: a case-control study of six alternative density assessment methods. Breast Cancer Res. 2014;16(5):439.CrossRefPubMedPubMedCentral
12.
Brand JS, Czene K, Shepherd JA, Leifland K, Heddson B, Sundbom A, et al. Automated measurement of volumetric mammographic density: a tool for widespread breast cancer risk assessment. Cancer Epidemiol Biomark Prev. 2014;23(9):1764–72.CrossRef
13.
Amos CI, Dennis J, Wang Z, Byun J, Schumacher FR, Gayther SA, et al. The OncoArray Consortium: a network for understanding the genetic architecture of common cancers. Cancer Epidemiol Biomark Prev. 2017;26(1):126–35.CrossRef
14.
Michailidou K, Lindstrom S, Dennis J, Beesley J, Hui S, Kar S, et al. Association analysis identifies 65 new breast cancer risk loci. Nature. 2017;551(7678):92–4.CrossRefPubMed
15.
Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7.CrossRefPubMedPubMedCentral
16.
1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491(7422):56–65.CrossRef
17.
Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet. 2012;44(8):955–9.CrossRefPubMedPubMedCentral
18.
Marchini J, Howie B, Myers S, McVean G, Donnelly P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet. 2007;39(7):906–13.CrossRefPubMed
19.
20.
Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics. 2010;26(18):2336–7.CrossRefPubMedPubMedCentral
21.
Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 2012;40(Database issue):D930–4.CrossRefPubMed
22.
Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012;22(9):1790–7.CrossRefPubMedPubMedCentral
23.
ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489(7414):57–74.CrossRef
24.
25.
Yang J, Lee SH, Goddard ME, Visscher PM. Genome-wide complex trait analysis (GCTA): methods, data analyses, and interpretations. Methods Mol Biol. 2013;1019:215–36.CrossRefPubMed
26.
Brand JS, Humphreys K, Thompson DJ, Li J, Eriksson M, Hall P, et al. Volumetric mammographic density: heritability and association with breast cancer susceptibility loci. J Natl Cancer Inst. 2014;106(12):dju334.CrossRefPubMed
27.
Stone J, Thompson DJ, Dos Santos SI, Scott C, Tamimi RM, Lindstrom S, et al. Novel associations between common breast cancer susceptibility variants and risk-predicting mammographic density measures. Cancer Res. 2015;75(12):2457–67.CrossRefPubMedPubMedCentral
28.
Milne RL, Kuchenbaecker KB, Michailidou K, Beesley J, Kar S, Lindstrom S, et al. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nat Genet. 2017;49(12):1767–78.CrossRefPubMed
29.
Douglas JA, Roy-Gagnon MH, Zhou C, Mitchell BD, Shuldiner AR, Chan HP, et al. Mammographic breast density—evidence for genetic correlations with established breast cancer risk factors. Cancer Epidemiol Biomark Prev. 2008;17(12):3509–16.CrossRef
30.
Auvinen P, Tammi R, Parkkinen J, Tammi M, Agren U, Johansson R, et al. Hyaluronan in peritumoral stroma and malignant cells associates with breast cancer spreading and predicts survival. Am J Pathol. 2000;156(2):529–36.CrossRefPubMedPubMedCentral
31.
Masarwah A, Tammi M, Sudah M, Sutela A, Oikari S, Kosma VM, et al. The reciprocal association between mammographic breast density, hyaluronan synthesis and patient outcome. Breast Cancer Res Treat. 2015;153(3):625–34.CrossRefPubMed
32.
Delpech B, Chevallier B, Reinhardt N, Julien JP, Duval C, Maingonnat C, Bastit P, Asselain B. Serum hyaluronan (hyaluronic acid) in breast cancer patients. Int J Cancer. 1990;46(3):388–90.CrossRefPubMed
33.
Peng C, Wallwiener M, Rudolph A, Cuk K, Eilber U, Celik M, et al. Plasma hyaluronic acid level as a prognostic and monitoring marker of metastatic breast cancer. Int J Cancer. 2016;138(10):2499–509.CrossRefPubMed
34.
Robinson GW, Hennighausen L. Inhibins and activins regulate mammary epithelial cell differentiation through mesenchymal-epithelial interactions. Development. 1997;124(14):2701–8.PubMed
35.
Eriksson N, Benton GM, Do CB, Kiefer AK, Mountain JL, Hinds DA, et al. Genetic variants associated with breast size also influence breast cancer risk. BMC Med Genet. 2012;13:53.CrossRefPubMedPubMedCentral
36.
Song H, Ki SH, Kim SG, Moon A. Activating transcription factor 2 mediates matrix metalloproteinase-2 transcriptional activation induced by p38 in breast epithelial cells. Cancer Res. 2006;66(21):10487–96.CrossRefPubMed
37.
Kim ES, Jeong JB, Kim S, Lee KM, Ko E, Noh DY, et al. The G12 family proteins upregulate matrix metalloproteinase-2 via p53 leading to human breast cell invasion. Breast Cancer Res Treat. 2010;124(1):49–61.CrossRefPubMed
38.
Sjoholm K, Palming J, Lystig TC, Jennische E, Woodruff TK, Carlsson B, et al. The expression of inhibin beta B is high in human adipocytes, reduced by weight loss, and correlates to factors implicated in metabolic disease. Biochem Biophys Res Commun. 2006;344(4):1308–14.CrossRefPubMed
39.
Li J, Foo JN, Schoof N, Varghese JS, Fernandez-Navarro P, Gierach GL, et al. Large-scale genotyping identifies a new locus at 22q13.2 associated with female breast size. J Med Genet. 2013;50(10):666–73.CrossRefPubMedPubMedCentral
40.
Pettersson A, Graff RE, Ursin G, Santos Silva ID, McCormack V, Baglietto L, et al. Mammographic density phenotypes and risk of breast cancer: a meta-analysis. J Natl Cancer Inst. 2014;106(5):dju078.CrossRefPubMedPubMedCentral
41.
Pettersson A, Hankinson SE, Willett WC, Lagiou P, Trichopoulos D, Tamimi RM. Nondense mammographic area and risk of breast cancer. Breast Cancer Res. 2011;13(5):R100.CrossRefPubMedPubMedCentral
42.
Lokate M, Kallenberg MG, Karssemeijer N, Van den Bosch MA, Peeters PH, Van Gils CH. Volumetric breast density from full-field digital mammograms and its association with breast cancer risk factors: a comparison with a threshold method. Cancer Epidemiol Biomark Prev. 2010;19(12):3096–105.CrossRef
43.
Cheddad A, Czene K, Eriksson M, Li J, Easton D, Hall P, et al. Area and volumetric density estimation in processed full-field digital mammograms for risk assessment of breast cancer. PLoS One. 2014;9(10):e110690.CrossRefPubMedPubMedCentral
44.
Astley SM, Harkness EF, Sergeant JC, Warwick J, Stavrinos P, Warren R, et al. A comparison of five methods of measuring mammographic density: a case-control study. Breast Cancer Res. 2018;20:10.CrossRefPubMedPubMedCentral
45.
Gubern-Merida A, Kallenberg M, Platel B, Mann RM, Marti R, Karssemeijer N. Volumetric breast density estimation from full-field digital mammograms: a validation study. PLoS One. 2014;9(1):e85952.CrossRefPubMedPubMedCentral
46.
van Engeland S, Snoeren PR, Huisman H, Boetes C, Karssemeijer N. Volumetric breast density estimation from full-field digital mammograms. IEEE Trans Med Imaging. 2006;25(3):273–82.CrossRefPubMed
47.
Kallenberg MG, van Gils CH, Lokate M, den Heeten GJ, Karssemeijer N. Effect of compression paddle tilt correction on volumetric breast density estimation. Phys Med Biol. 2012;57(16):5155–68.CrossRefPubMed
Metadata
Title
Common genetic variation and novel loci associated with volumetric mammographic density
Authors
Judith S. Brand
Keith Humphreys
Jingmei Li
Robert Karlsson
Per Hall
Kamila Czene
Publication date
01-12-2018
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2018
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-018-0954-6

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