Gene–environment interactions involving functional variants: Results from the Breast Cancer Association Consortium

Abstract

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, p int = 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, p int = 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, p int =1.8 × 10−4) and between 8q23‐rs13267382 and age at first full‐term pregnancy (ORint = 0.89, 95% CI: 0.83–0.95, p int = 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.

Smoking and risk of breast cancer in the Generations Study cohort

“…Increased risk of breast cancer with smoking has also been seen in some [ 40 , 41 ], but not all ( see review [ 1 ] and a large meta-analysis [ 41 ]), studies of BRCA1/2 carriers (or by proxy, women with three or more first-degree relatives with breast or ovarian cancer [ 42 ]). There are also reports of significant interactions with smoking and polymorphisms in carcinogen metabolism genes NAT2 [ 43 ] and CYP1A1 [ 44 , 45 ] as well as breast cancer susceptibility single-nucleotide polymorphisms [ 46 , 47 ]. Moreover, BRCA1 and BRCA2 proteins are involved in the repair of DNA damage, and it is therefore possible that BRCA1/2 carriers may be more sensitive to effects of carcinogens in cigarette smoke.…”

Update Mammakarzinom 2019 Teil 4 – diagnostische und therapeutische Herausforderungen neuer personalisierter Therapien für Patientinnen mit frühem Mammakarzinom

“…Dennoch ist der Einsatz der genetischen und nichtgenetischen Risikoinformation so weit fortgeschritten wie nie zuvor. Es existieren einige Arbeiten, die versuchen, auf der einen Seite die Gen-Gen-Interaktion und auf der anderen Seite die Gen-Umwelt-Interaktion zu entschlüsseln [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Die Analysen, die am ehesten in der Praxis für Patientinnen eingesetzt werden können, betreffen die Nutzung möglichst vieler Risikovarianten, um mit ihnen Risikogruppen für Patientinnen zu definieren [24,[30][31][32].…”

Association of breast cancer risk with polymorphisms in genes involved in the metabolism of xenobiotics and interaction with tobacco smoking: A gene‐set analysis

“…Studies focusing on susceptibility variants identified in GWAS on breast cancer, 58,59 have reported a significant interaction of smoking status with rs7558475 in CFLAR (CASP8 and FADD-like apoptosis regulator). 59 As already mentioned, other studies have concluded that cigarette smoking is associated with increased breast cancer risk among women with NAT2 slow acetylation genotypes, 33 but results have been heterogeneous. However, none of these studies has considered the combined effect of tobacco smoking with genetic variations using a gene-set approach.…”