Murdoch Childrens Research Institute, Parkville, Victoria, Australia.
Genet Epidemiol. 2010 Sep;34(6):552-60. doi: 10.1002/gepi.20511.
Complex diseases are likely to be caused by the interplay of genetic and environmental factors. Despite this, gene-disease associations are frequently investigated using models that focus solely on a marginal gene effect, ignoring environmental factors entirely. Failing to take into account a gene-environment interaction can weaken the apparent gene-disease association, leading to loss in statistical power and, potentially, inability to identify genuine risk factors. If a gene-environment interaction exists, therefore, a joint analysis allowing the effect of the gene to differ between groups defined by the environmental exposure can have greater statistical power than a marginal gene-disease model. However, environmental data are subject to measurement error. Substantial losses in statistical power for detecting gene-environment interactions can arise from measurement error in the environmental exposure. It is unclear, however, what effect measurement error may have on the power of the joint analysis. We consider the potential benefits, in terms of statistical power, of collecting concurrent environmental data within large cohorts in order to enhance gene detection. We further consider whether these benefits remain in the presence of misclassification in both the gene and the environmental exposure. We find that when an effect of the gene is apparent only in the presence of the environmental exposure, the joint analysis has greater power than a marginal gene-disease analysis. This comparative increase in power remains in the presence of likely levels of misclassification of either the gene or environmental exposure.
复杂疾病很可能是由遗传和环境因素相互作用引起的。尽管如此，基因-疾病关联通常使用仅关注边际基因效应的模型进行研究，完全忽略环境因素。如果不考虑基因-环境相互作用，可能会削弱明显的基因-疾病关联，导致统计效力下降，并且可能无法识别真正的风险因素。因此，如果存在基因-环境相互作用，允许基因在环境暴露定义的组之间产生不同影响的联合分析比边际基因-疾病模型具有更大的统计效力。然而，环境数据可能存在测量误差。环境暴露的测量误差会导致检测基因-环境相互作用的统计效力大大降低。然而，目前尚不清楚测量误差可能对联合分析的效力有何影响。我们考虑了在大型队列中收集并发环境数据以增强基因检测的潜在益处，即统计效力方面。我们进一步考虑在基因和环境暴露都存在分类错误的情况下，这些益处是否仍然存在。我们发现，当基因的作用仅在环境暴露存在时才明显时，联合分析比边际基因-疾病分析具有更大的效力。这种比较增加的效力在基因或环境暴露的分类错误达到可能的水平时仍然存在。
