Foppa I, Spiegelman D
Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
Am J Epidemiol. 1997 Oct 1;146(7):596-604. doi: 10.1093/oxfordjournals.aje.a009320.
Genetic polymorphisms may appear to the epidemiologist most commonly as different levels of susceptibility to exposure. Epidemiologic studies of heterogeneity in exposure susceptibility aim at estimating the parameter quantifying the gene-environment interaction. In this paper, the authors use a general approach to power and sample size calculations for case-control studies, which is applicable to settings where the exposure variable is polytomous and where the assumption of independence between the distribution of the genotype and the environmental factor may not be met. It was found through exploration of different scenarios that in the cases explored, power calculations were relatively insensitive to assumptions about the odds ratio for the exposure in the referent genotype category and to assumptions about the odds ratio for the genetic factor in the lowest exposure category, yet they were relatively sensitive to assumptions about gene frequency, particularly when gene frequency was low. In general, to detect a small to moderate gene-environment interaction effect, large sample sizes are needed. Because the examples studied represent only a small subset of possible scenarios that could occur in practice, the authors encourage the use of their user-friendly Fortran program for calculating power and sample size for gene-environment interactions with exposures grouped by quantiles that are explicitly tailored to the study at hand.
对流行病学家来说，基因多态性最常见的表现可能是对暴露的易感性水平不同。暴露易感性异质性的流行病学研究旨在估计量化基因-环境相互作用的参数。在本文中，作者使用了一种用于病例对照研究的功效和样本量计算的通用方法，该方法适用于暴露变量为多分类且基因型分布与环境因素之间可能不满足独立性假设的情况。通过对不同场景的探索发现，在所探讨的案例中，功效计算对参考基因型类别中暴露的比值比假设以及最低暴露类别中基因因素的比值比假设相对不敏感，但对基因频率假设相对敏感，尤其是当基因频率较低时。一般来说，要检测小到中等程度的基因-环境相互作用效应，需要大样本量。由于所研究的例子仅代表实际中可能出现的一小部分可能场景，作者鼓励使用他们用户友好的Fortran程序来计算基因-环境相互作用的功效和样本量，该程序针对手头的研究，对按分位数分组的暴露进行了明确的定制。
