4.5 Article

Post-G WAS gene-environment interplay in breast cancer: results from the Breast and Prostate Cancer Cohort Consortium and a meta-analysis on 79 000 women

期刊

HUMAN MOLECULAR GENETICS
卷 23, 期 19, 页码 5260-5270

出版社

OXFORD UNIV PRESS
DOI: 10.1093/hmg/ddu223

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资金

  1. U.S. National Institutes of Health, National Cancer Institute [U19-CA148065, U01-CA98233-07, U01-CA98710-06, U01-CA98216-06, U01-CA98758-07]
  2. Intramural Research Program of National Institutes of Health
  3. National Cancer Institute, Division of Cancer Epidemiology and Genetics
  4. National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services [HHSN268201100046C, HHSN268201100001C, HHS N268201100002C, HHSN268201100003C, HHSN2682011000 04C, HHSN271201100004C]
  5. Cancer Research UK [14136, 16491] Funding Source: researchfish
  6. Medical Research Council [G0401527, G1000143] Funding Source: researchfish
  7. National Institute for Health Research [NF-SI-0512-10114] Funding Source: researchfish

向作者/读者索取更多资源

We studied the interplay between 39 breast cancer (BC) risk SNPs and established BC risk (body mass index, height, age at menarche, parity, age at menopause, smoking, alcohol and family history of BC) and prognostic factors (TNM stage, tumor grade, tumor size, age at diagnosis, estrogen receptor status and progesterone receptor status) as joint determinants of BC risk. We used a nested case-control design within the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3), with 16 285 BC cases and 19 376 controls. We performed stratified analyses for both the risk and prognostic factors, testing for heterogeneity for the risk factors, and case-case comparisons for differential associations of polymorphisms by subgroups of the prognostic factors. We analyzed multiplicative interactions between the SNPs and the risk factors. Finally, we also performed a meta-analysis of the interaction ORs from BPC3 and the Breast Cancer Association Consortium. After correction for multiple testing, no significant interaction between the SNPs and the established risk factors in the BPC3 study was found. The meta-analysis showed a suggestive interaction between smoking status and SLC4A7-rs4973768 (P-interaction = 8.84 x 10(-4)) which, although not significant after considering multiple comparison, has a plausible biological explanation. In conclusion, in this study of up to almost 79 000 women we can conclusively exclude any novel major interactions between genome-wide association studies hits and the epidemiologic risk factors taken into consideration, but we propose a suggestive interaction between smoking status and SLC4A7-rs4973768 that if further replicated could help our understanding in the etiology of BC.

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