Mammographic breast density and breast cancer: evidence of a shared genetic basis

Jajini S Varghese, Deborah J Thompson, Kyriaki Michailidou, Sara Lindström, Clare Turnbull, Judith Brown, Jean Leyland, Ruth M L Warren, Robert N Luben, Ruth J Loos, Nicholas J Wareham, Johanna Rommens, Andrew D Paterson, Lisa J Martin, Celine M Vachon, Christopher G Scott, Elizabeth J Atkinson, Fergus J Couch, Carmel Apicella, Melissa C Southey, Jennifer Stone, Jingmei Li, Louise Eriksson, Kamila Czene, Norman F Boyd, Per Hall, John L Hopper, Rulla M Tamimi, MODE Consortium, Nazneen Rahman, Douglas F Easton, Jajini S Varghese, Deborah J Thompson, Kyriaki Michailidou, Sara Lindström, Clare Turnbull, Judith Brown, Jean Leyland, Ruth M L Warren, Robert N Luben, Ruth J Loos, Nicholas J Wareham, Johanna Rommens, Andrew D Paterson, Lisa J Martin, Celine M Vachon, Christopher G Scott, Elizabeth J Atkinson, Fergus J Couch, Carmel Apicella, Melissa C Southey, Jennifer Stone, Jingmei Li, Louise Eriksson, Kamila Czene, Norman F Boyd, Per Hall, John L Hopper, Rulla M Tamimi, MODE Consortium, Nazneen Rahman, Douglas F Easton

Abstract

Percent mammographic breast density (PMD) is a strong heritable risk factor for breast cancer. However, the pathways through which this risk is mediated are still unclear. To explore whether PMD and breast cancer have a shared genetic basis, we identified genetic variants most strongly associated with PMD in a published meta-analysis of five genome-wide association studies (GWAS) and used these to construct risk scores for 3,628 breast cancer cases and 5,190 controls from the UK2 GWAS of breast cancer. The signed per-allele effect estimates of single-nucleotide polymorphisms (SNP) were multiplied with the respective allele counts in the individual and summed over all SNPs to derive the risk score for an individual. These scores were included as the exposure variable in a logistic regression model with breast cancer case-control status as the outcome. This analysis was repeated using 10 different cutoff points for the most significant density SNPs (1%-10% representing 5,222-50,899 SNPs). Permutation analysis was also conducted across all 10 cutoff points. The association between risk score and breast cancer was significant for all cutoff points from 3% to 10% of top density SNPs, being most significant for the 6% (2-sided P = 0.002) to 10% (P = 0.001) cutoff points (overall permutation P = 0.003). Women in the top 10% of the risk score distribution had a 31% increased risk of breast cancer [OR = 1.31; 95% confidence interval (CI), 1.08-1.59] compared with women in the bottom 10%. Together, our results show that PMD and breast cancer have a shared genetic basis that is mediated through a large number of common variants.

Figures

Figure 1
Figure 1
Significance of the mammographic breast density polygenic risk score (PRS) for the prediction of breast cancer risk, according to percent cut-off of SNPs used in the PRS. Significance levels were obtained using unconditional logistic regression.

Source: PubMed

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