Gene-environment interactions involving functional variants: Results from the Breast Cancer Association Consortium.
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Investigating the most likely causal variants identified by fine-mapping analyses may improve the power to detect gene-environment interactions. We assessed the interplay between 70 single nucleotide polymorphisms identified by genetic fine-scale mapping of susceptibility loci and 11 epidemiological breast cancer risk factors in relation to breast cancer. Analyses were conducted on up to 58,573 subjects (26,968 cases and 31,605 controls) from the Breast Cancer Association Consortium, in one of the largest studies of its kind. Analyses were carried out separately for estrogen receptor (ER) positive (ER+) and ER negative (ER-) disease. The Bayesian False Discovery Probability (BFDP) was computed to assess the noteworthiness of the results. Four potential gene-environment interactions were identified as noteworthy (BFDP < 0.80) when assuming a true prior interaction probability of 0.01. The strongest interaction result in relation to overall breast cancer risk was found between CFLAR-rs7558475 and current smoking (ORint = 0.77, 95% CI: 0.67-0.88, pint = 1.8 × 10(-4) ). The interaction with the strongest statistical evidence was found between 5q14-rs7707921 and alcohol consumption (ORint =1.36, 95% CI: 1.16-1.59, pint = 1.9 × 10(-5) ) in relation to ER- disease risk. The remaining two gene-environment interactions were also identified in relation to ER- breast cancer risk and were found between 3p21-rs6796502 and age at menarche (ORint = 1.26, 95% CI: 1.12-1.43, pint =1.8 × 10(-4) ) and between 8q23-rs13267382 and age at first full-term pregnancy (ORint = 0.89, 95% CI: 0.83-0.95, pint = 5.2 × 10(-4) ). While these results do not suggest any strong gene-environment interactions, our results may still be useful to inform experimental studies. These may in turn, shed light on the potential interactions observed.
The Institute of Cancer Research (Grant ID: Unspecified)
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Breast Cancer Association Consortium
single nucleotide polymorphism
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Int J Cancer, 2017