Bermejo Justo Lorenzo, Hemminki Kari
Division of Molecular Genetic Epidemiology, German Cancer Research Center, Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany.
Carcinogenesis. 2007 Jul;28(7):1526-32. doi: 10.1093/carcin/bgm068. Epub 2007 Mar 26.
Gene-environment studies have been motivated by the likely existence of prevalent low-risk genes that interact with common environmental exposures. The present study assessed the statistical advantage of the simultaneous consideration of genes and environment to investigate the effect of environmental risk factors on disease. In particular, we contemplated the possibility that several genes modulate the environmental effect. Environmental exposures, genotypes and phenotypes were simulated according to a wide range of parameter settings. Different models of gene-gene-environment interaction were considered. For each parameter combination, we estimated the probability of detecting the main environmental effect, the power to identify the gene-environment interaction and the frequency of environmentally affected individuals at which environmental and gene-environment studies show the same statistical power. The proportion of cases in the population attributable to the modeled risk factors was also calculated. Our data indicate that environmental exposures with weak effects may account for a significant proportion of the population prevalence of the disease. A general result was that, if the environmental effect was restricted to rare genotypes, the power to detect the gene-environment interaction was higher than the power to identify the main environmental effect. In other words, when few individuals contribute to the overall environmental effect, individual contributions are large and result in easily identifiable gene-environment interactions. Moreover, when multiple genes interacted with the environment, the statistical benefit of gene-environment studies was limited to those studies that included major contributors to the gene-environment interaction. The advantage of gene-environment over plain environmental studies also depends on the inheritance mode of the involved genes, on the study design and, to some extend, on the disease prevalence.
基因-环境研究的动机在于可能存在与常见环境暴露相互作用的普遍低风险基因。本研究评估了同时考虑基因和环境以调查环境风险因素对疾病影响的统计学优势。特别是,我们考虑了几种基因调节环境效应的可能性。根据广泛的参数设置模拟了环境暴露、基因型和表型。考虑了不同的基因-基因-环境相互作用模型。对于每个参数组合,我们估计了检测主要环境效应的概率、识别基因-环境相互作用的效能以及环境研究和基因-环境研究显示相同统计效能时受环境影响个体的频率。还计算了人群中归因于建模风险因素的病例比例。我们的数据表明,效应较弱的环境暴露可能占疾病人群患病率的很大比例。一个普遍的结果是,如果环境效应仅限于罕见基因型,检测基因-环境相互作用的效能高于识别主要环境效应的效能。换句话说,当很少个体对总体环境效应有贡献时,个体贡献很大,从而导致易于识别的基因-环境相互作用。此外,当多个基因与环境相互作用时,基因-环境研究的统计学优势仅限于那些纳入了基因-环境相互作用主要贡献者的研究。基因-环境研究相对于单纯环境研究的优势还取决于所涉及基因的遗传模式、研究设计,并且在一定程度上还取决于疾病患病率。