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000137598 0247_ $$2doi$$a10.1158/1055-9965.EPI-17-1185
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000137598 041__ $$aeng
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000137598 1001_ $$aScannell Bryan, Molly$$b0
000137598 245__ $$aGermline Variation and Breast Cancer Incidence: A Gene-Based Association Study and Whole-Genome Prediction of Early-Onset Breast Cancer.
000137598 260__ $$aPhiladelphia, Pa.$$bAACR$$c2018
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000137598 520__ $$aBackground: 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 3,479 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.Results: Three gene regions were associated with breast cancer incidence: FGFR2 (P = 1.23 × 10-5; replication P < 1.00 × 10-6), NEK10 (P = 3.57 × 10-4; replication P < 1.00 × 10-6), and SIVA1 (P = 5.49 × 10-4; replication P < 1.00 × 10-6). 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. To predict incidence, whole-genome prediction was implemented on a subset of 3,076 participants who were additionally genotyped on a genome wide array. The whole-genome prediction outperformed a polygenic risk score [AUC, 0.636; 95% confidence interval (CI), 0.614-0.659 compared with 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. Cancer Epidemiol Biomarkers Prev; 27(9); 1057-64. ©2018 AACR.
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000137598 7001_ $$aArgos, Maria$$b1
000137598 7001_ $$aAndrulis, Irene L$$b2
000137598 7001_ $$aHopper, John L$$b3
000137598 7001_ $$0P:(DE-He78)c259d6cc99edf5c7bc7ce22c7f87c253$$aChang-Claude, Jenny$$b4$$udkfz
000137598 7001_ $$aMalone, Kathleen E$$b5
000137598 7001_ $$aJohn, Esther M$$b6
000137598 7001_ $$aGammon, Marilie D$$b7
000137598 7001_ $$aDaly, Mary B$$b8
000137598 7001_ $$aTerry, Mary Beth$$b9
000137598 7001_ $$aBuys, Saundra S$$b10
000137598 7001_ $$aHuo, Dezheng$$b11
000137598 7001_ $$aOlopade, Olofunmilayo I$$b12
000137598 7001_ $$aGenkinger, Jeanine M$$b13
000137598 7001_ $$aWhittemore, Alice S$$b14
000137598 7001_ $$aJasmine, Farzana$$b15
000137598 7001_ $$aKibriya, Muhammad G$$b16
000137598 7001_ $$aChen, Lin S$$b17
000137598 7001_ $$aAhsan, Habibul$$b18
000137598 773__ $$0PERI:(DE-600)2036781-8$$a10.1158/1055-9965.EPI-17-1185$$gVol. 27, no. 9, p. 1057 - 1064$$n9$$p1057 - 1064$$tCancer epidemiology, biomarkers & prevention$$v27$$x1538-7755$$y2018
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000137598 9141_ $$y2018
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