Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk.

Sara Lindström; Deborah J Thompson; Andrew D Paterson; Jingmei Li; Gretchen L Gierach; Christopher Scott; Jennifer Stone; Julie A Douglas; Isabel dos-Santos-Silva ORCID logo; Pablo Fernandez-Navarro; +35 more... Jajini Verghase; Paula Smith; Judith Brown; Robert Luben; Nicholas J Wareham; Ruth JF Loos; John A Heit; V Shane Pankratz; Aaron Norman; Ellen L Goode; Julie M Cunningham; Mariza deAndrade; Robert A Vierkant; Kamila Czene; Peter A Fasching; Laura Baglietto; Melissa C Southey; Graham G Giles; Kaanan P Shah; Heang-Ping Chan; Mark A Helvie; Andrew H Beck; Nicholas W Knoblauch; Aditi Hazra; David J Hunter; Peter Kraft; Marina Pollan; Jonine D Figueroa; Fergus J Couch; John L Hopper; Per Hall; Douglas F Easton; Norman F Boyd; Celine M Vachon; Rulla M Tamimi; (2014) Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk. Nature communications, 5 (1). 5303-. ISSN 2041-1723 DOI: 10.1038/ncomms6303
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Mammographic density reflects the amount of stromal and epithelial tissues in relation to adipose tissue in the breast and is a strong risk factor for breast cancer. Here we report the results from meta-analysis of genome-wide association studies (GWAS) of three mammographic density phenotypes: dense area, non-dense area and percent density in up to 7,916 women in stage 1 and an additional 10,379 women in stage 2. We identify genome-wide significant (P<5 × 10(-8)) loci for dense area (AREG, ESR1, ZNF365, LSP1/TNNT3, IGF1, TMEM184B and SGSM3/MKL1), non-dense area (8p11.23) and percent density (PRDM6, 8p11.23 and TMEM184B). Four of these regions are known breast cancer susceptibility loci, and four additional regions were found to be associated with breast cancer (P<0.05) in a large meta-analysis. These results provide further evidence of a shared genetic basis between mammographic density and breast cancer and illustrate the power of studying intermediate quantitative phenotypes to identify putative disease-susceptibility loci.


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