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题目:: A multistage genetic association study identifies breast cancer risk loci at 10q25 and 16q24.
作者:: Higginbotham(Kathryn S),Breyer(Joan P),McReynolds(Kate M),Bradley(Kevin M),Schuyler(Peggy A),Plummer(W Dale),Freudenthal(Marcia E),Trentham-Dietz(Amy),Newcomb(Polly A),Parl(Fritz F),Sanders(Melinda E),Page(David L),Egan(Kathleen M),Dupont(William D),Smith(Jeffrey R)
状态:: 发布时间2012-09-07 , 更新时间 2016-12-03
期刊:: Cancer Epidemiol Biomarkers Prev
摘要:: Heritable risk for breast cancer includes an increasing number of common, low effect risk variants. We conducted a multistage genetic association study in a series of independent epidemiologic breast cancer study populations to identify novel breast cancer risk variants.,We tested 1,162 SNPs of greatest nominal significance from stage I of the Cancer Genetic Markers of Susceptibility breast cancer study (CGEMS; 1,145 cases, 1,142 controls) for evidence of replicated association with breast cancer in the Nashville Breast Cohort (NBC; 599 cases, 1,161 controls), the Collaborative Breast Cancer Study (CBCS; 1,552 cases, 1,185 controls), and BioVU Breast Cancer Study (BioVU; 1,172 cases, 1,172 controls).,Among these SNPs, a series of validated breast cancer risk variants yielded expected associations in the study populations. In addition, we observed two previously unreported loci that were significantly associated with breast cancer risk in the CGEMS, NBC, and CBCS study populations and had a consistent, although not statistically significant, risk effect in the BioVU study population. These were rs1626678 at 10q25.3 near ENO4 and KIAA1598 (meta-analysis age-adjusted OR = 1.13 [1.07-1.20], P = 5.6 × 10(-5)), and rs8046508 at 16q23.1 in the eighth intron of WWOX (meta-analysis age-adjusted OR = 1.20 [1.10-1.31], P = 3.5 × 10(-5)).,Our data supports the association of two novel loci, at 10q25.3 and 16q23.1, with risk of breast cancer.,The expanding compendium of known breast cancer genetic risk variants holds increasing power for clinical risk prediction models of breast cancer, improving upon the Gail model.
语言:: eng
DOI:

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