Hodgson M Elizabeth, Olshan Andrew F, North Kari E, Poole Charles L, Zeng Donglin, Tse Chiu-Kit, Keku Tope O, Galanko Joseph, Sandler Robert, Millikan Robert C
Departments of Epidemiology, University of North Carolina Chapel Hill, North Carolina 27599.
Int J Mol Epidemiol Genet. 2012;3(4):333-60. Epub 2012 Nov 15.
The independence assumption for a case-only analysis of statistical interaction, i. e. that genetic (G) and environmental exposures (E) are not associated in the source population, is often checked in surrogate populations. Few studies have examined G-E association in empirical data, particularly in controls from population-based studies, the type of controls expected to provide the most valid surrogate estimates of G-E association. We used controls from two population-based case-control studies to evaluate G-E independence for 43 selected genetic polymorphisms and smoking behavior. The odds ratio (OR(z)) was used to estimate G-E association and, therefore, the magnitude of bias introduced into the case-only odds ratio (COR). Odds ratios of moderate magnitude [mmOR(z)], defined as OR(z)≤0.7 or OR(z)≥1.4, were found at least one of the six smoking measures (ever, former, current, cig/day, years smoked, pack-years) for 45% and 59% of the SNPs examined in the control groups of two independently conducted North Carolina studies, respectively. Consequently, case-only estimates of G-E interaction in the context of a multiplicative benchmark would be biased for these SNPs and smoking measures. MmOR(z)s were found more often for smoking amount than smoking status. We recommend that a stand-alone case-only study should only be conducted when G-E independence can be verified for each polymorphism and exposure metric with population-specific data. Our results suggest that OR(z) is specific to each underlying population rather than an estimate of a 'universal' OR(z) for that SNP and smoking measure. Further, misspecification of smoking is likely to introduce bias into the COR.
在仅针对病例的统计交互分析中，即基因（G）与环境暴露（E）在源人群中不相关这一独立性假设，通常在替代人群中进行检验。很少有研究在实证数据中检验基因 - 环境关联，特别是在基于人群研究的对照中，这类对照被认为能为基因 - 环境关联提供最有效的替代估计。我们使用了两项基于人群的病例对照研究中的对照，来评估43个选定基因多态性与吸烟行为之间的基因 - 环境独立性。优势比（OR(z)）用于估计基因 - 环境关联，进而估计仅针对病例的优势比（COR）中引入的偏差大小。在两项独立开展的北卡罗来纳州研究的对照组中，分别有45%和59%的单核苷酸多态性（SNP），在六种吸烟测量指标（曾经吸烟、既往吸烟、当前吸烟、每日吸烟支数、吸烟年数、吸烟包年数）中的至少一项上，发现了中等大小的优势比[mmOR(z)]，定义为OR(z)≤0.7或OR(z)≥1.4。因此，在乘法基准背景下仅针对病例的基因 - 环境交互作用估计，对于这些SNP和吸烟测量指标会产生偏差。发现吸烟量的mmOR(z)比吸烟状态的更常见。我们建议，只有在能够利用特定人群数据验证每种多态性和暴露指标的基因 - 环境独立性时，才应开展单独的仅针对病例的研究。我们的结果表明，OR(z)特定于每个基础人群，而非该SNP和吸烟测量指标的“通用”OR(z)估计值。此外，吸烟情况的错误分类很可能会给COR引入偏差。
