Hein Rebecca, Beckmann Lars, Chang-Claude Jenny
Department of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany.
Genet Epidemiol. 2008 Apr;32(3):235-45. doi: 10.1002/gepi.20298.
Association studies accounting for gene-environment interactions (G x E) may be useful for detecting genetic effects. Although current technology enables very dense marker spacing in genetic association studies, the true disease variants may not be genotyped. Thus, causal genes are searched for by indirect association using genetic markers in linkage disequilibrium (LD) with the true disease variants. Sample sizes needed to detect G x E effects in indirect case-control association studies depend on the true genetic main effects, disease allele frequencies, whether marker and disease allele frequencies match, LD between loci, main effects and prevalence of environmental exposures, and the magnitude of interactions. We explored variables influencing sample sizes needed to detect G x E, compared these sample sizes with those required to detect genetic marginal effects, and provide an algorithm for power and sample size estimations. Required sample sizes may be heavily inflated if LD between marker and disease loci decreases. More than 10,000 case-control pairs may be required to detect G x E. However, given weak true genetic main effects, moderate prevalence of environmental exposures, as well as strong interactions, G x E effects may be detected with smaller sample sizes than those needed for the detection of genetic marginal effects. Moreover, in this scenario, rare disease variants may only be detectable when G x E is included in the analyses. Thus, the analysis of G x E appears to be an attractive option for the detection of weak genetic main effects of rare variants that may not be detectable in the analysis of genetic marginal effects only.
考虑基因-环境相互作用(G×E)的关联研究可能有助于检测基因效应。尽管当前技术能够在基因关联研究中实现非常密集的标记间距,但真正的疾病变异可能未进行基因分型。因此,通过使用与真正疾病变异处于连锁不平衡(LD)状态的遗传标记进行间接关联来寻找因果基因。在间接病例对照关联研究中检测G×E效应所需的样本量取决于真正的基因主效应、疾病等位基因频率、标记和疾病等位基因频率是否匹配、基因座之间的LD、环境暴露的主效应和患病率以及相互作用的大小。我们探讨了影响检测G×E所需样本量的变量,将这些样本量与检测基因边际效应所需的样本量进行了比较,并提供了一种功效和样本量估计的算法。如果标记和疾病基因座之间的LD降低,所需样本量可能会大幅增加。可能需要超过10000对病例对照来检测G×E。然而,鉴于真正的基因主效应较弱、环境暴露患病率适中以及相互作用较强,检测G×E效应所需的样本量可能比检测基因边际效应所需的样本量小。此外,在这种情况下,只有在分析中纳入G×E时,罕见疾病变异才可能被检测到。因此,对于检测仅在基因边际效应分析中可能无法检测到的罕见变异的弱基因主效应而言,G×E分析似乎是一个有吸引力的选择。