Dataset Information

The contributions of breast density and common genetic variation to breast cancer risk.

ABSTRACT: We evaluated whether a 76-locus polygenic risk score (PRS) and Breast Imaging Reporting and Data System (BI-RADS) breast density were independent risk factors within three studies (1643 case patients, 2397 control patients) using logistic regression models. We incorporated the PRS odds ratio (OR) into the Breast Cancer Surveillance Consortium (BCSC) risk-prediction model while accounting for its attributable risk and compared five-year absolute risk predictions between models using area under the curve (AUC) statistics. All statistical tests were two-sided. BI-RADS density and PRS were independent risk factors across all three studies (P interaction = .23). Relative to those with scattered fibroglandular densities and average PRS (2(nd) quartile), women with extreme density and highest quartile PRS had 2.7-fold (95% confidence interval [CI] = 1.74 to 4.12) increased risk, while those with low density and PRS had reduced risk (OR = 0.30, 95% CI = 0.18 to 0.51). PRS added independent information (P < .001) to the BCSC model and improved discriminatory accuracy from AUC = 0.66 to AUC = 0.69. Although the BCSC-PRS model was well calibrated in case-control data, independent cohort data are needed to test calibration in the general population.

SUBMITTER: Vachon CM

PROVIDER: S-EPMC4598340 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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The contributions of breast density and common genetic variation to breast cancer risk.

Vachon Celine M CM Pankratz V Shane VS Scott Christopher G CG Haeberle Lothar L Ziv Elad E Jensen Matthew R MR Brandt Kathleen R KR Whaley Dana H DH Olson Janet E JE Heusinger Katharina K Hack Carolin C CC Jud Sebastian M SM Beckmann Matthias W MW Schulz-Wendtland Ruediger R Tice Jeffrey A JA Norman Aaron D AD Cunningham Julie M JM Purrington Kristen S KS Easton Douglas F DF Sellers Thomas A TA Kerlikowske Karla K Fasching Peter A PA Couch Fergus J FJ

Journal of the National Cancer Institute 20150304 5

We evaluated whether a 76-locus polygenic risk score (PRS) and Breast Imaging Reporting and Data System (BI-RADS) breast density were independent risk factors within three studies (1643 case patients, 2397 control patients) using logistic regression models. We incorporated the PRS odds ratio (OR) into the Breast Cancer Surveillance Consortium (BCSC) risk-prediction model while accounting for its attributable risk and compared five-year absolute risk predictions between models using area under th ...[more]

PMID: 25745020

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Project description:Association studies in candidate genes have been widely used to search for common low penetrance susceptibility alleles, but few definite associations have been established. We have conducted association studies in breast cancer using an empirical single nucleotide polymorphism (SNP) tagging approach to capture common genetic variation in genes that are candidates for breast cancer based on their known function. We genotyped 710 SNPs in 120 candidate genes in up to 4,400 breast cancer cases and 4,400 controls using a staged design. Correction for population stratification was done using the genomic control method, on the basis of data from 280 genomic control SNPs. Evidence for association with each SNP was assessed using a Cochran-Armitage trend test (p-trend) and a two-degrees of freedom chi(2) test for heterogeneity (p-het). The most significant single SNP (p-trend = 8 x 10(-5)) was not significant at a nominal 5% level after adjusting for population stratification and multiple testing. To evaluate the overall evidence for an excess of positive associations over the proportion expected by chance, we applied two global tests: the admixture maximum likelihood (AML) test and the rank truncated product (RTP) test corrected for population stratification. The admixture maximum likelihood experiment-wise test for association was significant for both the heterogeneity test (p = 0.0031) and the trend test (p = 0.017), but no association was observed using the rank truncated product method for either the heterogeneity test or the trend test (p = 0.12 and p = 0.24, respectively). Genes in the cell-cycle control pathway and genes involved in steroid hormone metabolism and signalling were the main contributors to the association. These results suggest that a proportion of SNPs in these candidate genes are associated with breast cancer risk, but that the effects of individual SNPs is likely to be small. Large sample sizes from multicentre collaboration will be needed to identify associated SNPs with certainty.

Dataset Information

The contributions of breast density and common genetic variation to breast cancer risk.

Publications

The contributions of breast density and common genetic variation to breast cancer risk.

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