Sample size requirements for indirect association studies of gene-environment interactions (G x E)
Sample size requirements for indirect association studies of gene-environment interactions (G x E)
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DOI：
10.1002/gepi.20298
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发表时间：
2008-04-01
影响因子：
2.1
通讯作者：
Chang-Claude, Jenny
中科院分区：
文献类型：
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作者：
Hein, Rebecca;Beckmann, Lars;Chang-Claude, Jenny
考虑到基因-环境相互作用(G×E)的关联研究可能有助于检测遗传效应。尽管目前的技术使遗传关联研究中的标记间距非常密集，但真正的疾病变体可能无法进行基因分型。因此，通过使用与真实疾病变种的连锁不平衡(LD)中的遗传标记的间接关联来寻找原因基因。在间接病例对照研究中检测G×E效应所需的样本量取决于真实的遗传主效应、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流行率以及相互作用的大小。我们探索了影响检测G×E所需样本量的变量，将这些样本量与检测遗传边际效应所需的样本量进行了比较，并提供了功率和样本量估计的算法。如果标记物和疾病位点之间的LD减少，所需的样本量可能会严重膨胀。可能需要10,000多个病例对照对来检测G×E。然而，考虑到弱的真实遗传主效应、适度的环境暴露以及强烈的相互作用，检测到的G×E效应的样本量可能比检测遗传边际效应所需的样本量小。此外，在这种情况下，只有在分析中包括G×E时，才能检测到罕见疾病的变异。因此，对于仅在遗传边际效应分析中可能检测不到的稀有变异的弱遗传主效应而言，G×E分析似乎是一个有吸引力的选择。
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.