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Scannell Bryan M, Argos M, Andrulis IL, Hopper JL, Chang-Claude J, Malone KE, John EM, Gammon MD, Daly MB, Terry MB, Buys SS, Huo D, Olopade OI, Genkinger JM, Whittemore AS, Jasmine F, Kibriya MG, Chen LS, Ahsan H
Germline variation and breast cancer incidence: A gene-based association study and whole-genome prediction of early onset breast cancer
Cancer Epidemiol Biomarkers Prev (2018) 27:1057-1064.
Abstract
BACKGROUND: Although germline genetics influences breast cancer incidence, published research only explains approximately half of the expected association. Moreover, the accuracy of prediction models remains low. For women who develop breast cancer early, the genetic architecture is less established. METHODS: To identify loci associated with early-onset breast cancer, gene-based tests were carried out using exome array data from 3479 women with breast cancer diagnosed before age 50 and 973 age-matched controls. Replication was undertaken in a population that developed breast cancer at all ages of onset. Three gene regions were associated with breast cancer incidence: FGFR2 (p=1.2310-5; replication p<1.0010-6), NEK10 (p=3.5710-4; replication p<1.0010-6), and SIVA1 (p=5.4910-4; replication p<1.0010-6). Of the 151 gene regions reported in previous literature, 19 (12.5%) showed evidence of association (p<0.05) with the risk of early-onset breast cancer in the early-onset population. RESULTS: To predict incidence, whole-genome prediction was implemented on a subset of 3076 participants who were additionally genotyped on a genome wide array. The whole-genome prediction outperformed a polygenic risk score (AUC: 0.636; 95%CI: 0.614-0.659 compared to 0.601; 95%CI: 0.578-0.623), and when combined with known epidemiologic risk factors, the AUC rose to 0.662 (95% CI:0.640-0.684). CONCLUSIONS: This research supports a role for variation within FGFR2 and NEK10 in breast cancer incidence, and suggests SIVA1 as a novel risk locus. IMPACT: This analysis supports a shared genetic etiology between women with early- and late-onset breast cancer, and suggests whole-genome data can improve risk assessment.
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Publication Date: 2018-09-01.
PMCID: PMC6125194
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Last updated on Friday, December 06, 2019