Additive Interactions Between Susceptibility Single-Nucleotide Polymorphisms Identified in Genome-Wide Association Studies and Breast Cancer Risk Factors in the Breast and Prostate Cancer Cohort Consortium
Additive Interactions Between Susceptibility Single-Nucleotide Polymorphisms Identified in Genome-Wide Association Studies and Breast Cancer Risk Factors in the Breast and Prostate Cancer Cohort Consortium
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DOI：
10.1093/aje/kwu214
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发表时间：
2014-11-15
影响因子：
5
通讯作者：
Kraft, Peter
中科院分区：
文献类型：
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作者：
Joshi, Amit D.;Lindstrom, Sara;Kraft, Peter
相加的相互作用可能会对公共卫生和病因学产生影响，但很少有报道。我们评估了9个已确立的乳腺癌风险因素和23个易感性单核苷酸多态(SNPs)在绝对风险等级上的偏差，这些SNPs是从国家癌症研究所的乳腺癌和前列腺癌队列联盟的10,146例非西班牙裔白人乳腺癌病例和12,760名对照的全基因组关联研究中确定的。我们使用年龄和队列调整的Logistic回归模型估计了SNPs和非遗传风险因素的每个成对组合的相互作用导致的相对超额风险及其95%的可信区间。在对多重比较进行校正后，我们发现DNA修复蛋白RAD51同源基因中的SNP(RAD51L1；rs10483813)与体重指数(体重(Kg)/身高(M)(2))之间的相互作用(未校正的P=4.51 x 10(-5))导致了统计学上显著的超额风险。我们还使用以前乳腺癌易感性SNPs研究中的每等位基因优势比估计值比较了加法和乘法多基因风险预测模型，并观察到乘法模型比加法模型具有更好的拟合优度。
Additive interactions can have public health and etiological implications but are infrequently reported. We assessed departures from additivity on the absolute risk scale between 9 established breast cancer risk factors and 23 susceptibility single-nucleotide polymorphisms (SNPs) identified from genome-wide association studies among 10,146 non-Hispanic white breast cancer cases and 12,760 controls within the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium. We estimated the relative excess risk due to interaction and its 95% confidence interval for each pairwise combination of SNPs and nongenetic risk factors using age- and cohort-adjusted logistic regression models. After correction for multiple comparisons, we identified a statistically significant relative excess risk due to interaction (uncorrected P = 4.51 x 10(-5)) between a SNP in the DNA repair protein RAD51 homolog 2 gene (RAD51L1; rs10483813) and body mass index (weight (kg)/height (m)(2)). We also compared additive and multiplicative polygenic risk prediction models using per-allele odds ratio estimates from previous studies for breast-cancer susceptibility SNPs and observed that the multiplicative model had a substantially better goodness of fit than the additive model.