The detection of gene-environment interaction for continuous traits: should we deal with measurement error by bigger studies or better measurement?
The detection of gene-environment interaction for continuous traits: should we deal with measurement error by bigger studies or better measurement?
复制标题
DOI：
10.1093/ije/dyg002
复制
发表时间：
2003-02-01
影响因子：
7.7
通讯作者：
Wareham, NJ
中科院分区：
文献类型：
--
作者：
Wong, MY;Day, NE;Wareham, NJ
背景基因分型技术的进步促进了对生物学相关基因-环境相互作用的研究。研究设计，以检测相互作用的连续性状，如血压和胰岛素敏感性正吸引越来越多的关注。我们以前已经描述了这样的研究的功率计算，本文介绍了这些计算的扩展，以考虑测量误差。方法在本文中考虑的模型是一个简单的线性回归连续的结果，一个连续分布的暴露变量，其中每个基因型的斜率的比率被认为是相互作用参数。经典的测量误差模型被用来描述测量结果和暴露的不确定度。对于暴露和结果均存在误差的给定主效应，计算样本量以检测与不同频率的次要等位基因的不同相互作用幅度。样本量检测给定的相互作用，一个给定的次要等位基因频率计算不同程度的测量误差的评估中的曝光和，outcome.Results所需的样本量是依赖于相互作用的大小，等位基因频率和强度的关联在那些与共同的等位基因。作为一个例子，我们采取的情况下，在那些与共同等位基因的影响大小是一个标准偏差的结果的四分之一的标准偏差变化的暴露。如果频率为20%的次要等位基因导致效应量加倍，则样本量高度依赖于测量暴露和结果的精度。rho（Tx）和rho（Ty）分别是测量的暴露和结果与真实值之间的相关性。如果使用不良的暴露和结果测量（例如rho（Tx）= 0.3，rho（Ty）= 0.4），则需要150 989人的研究规模才能在10（-4）的显著性水平下以95%的把握度检测相互作用。如果更精确的话，这种相互作用可以在10 000人以下的研究样本中检测到。暴露和结果的测量。发了（例如rho（Tx）= 0.7，rho（Ty）= 0.7），结论在测量误差的情况下，研究连续暴露和遗传因素对连续结果变量的相互作用所需的样本量的计算公式将在研究连续暴露和遗传因素对连续结果变量的相互作用时具有相当大的实用性。设计具有适当功效的研究。这些计算表明，重复和更精确测量暴露和结果的小型研究将与甚至20倍的大型研究一样强大，因为它们的规模必然采用不太精确的测量方法。尽管基因分型的成本正在下降，测量误差对检测连续性状相互作用的影响程度表明，投资于具有更好测量的研究可能是比试图通过增加样本量来处理误差更合适的策略。
Background The search for biologically relevant gene-environment interactions has been facilitated by technological advances in genotyping. The design of studies to detect interactions on continuous traits such as blood pressure and insulin sensitivity is attracting increasing attention. We have previously described power calculations for such studies, and this paper describes the extension of those calculations to take account of measurement error.Methods The model considered in this paper is a simple linear regression relating a continuous outcome to a continuously distributed exposure variable in which the ratio of slopes for each genotype is considered as the interaction parameter. The classical measurement error model is used to describe the uncertainty in measurement in the outcome and the exposure. The sample size to detect differing magnitudes of interaction With varying frequencies of the minor allele are calculated for a given main effect observed with error both in the exposure and the outcome. The sample size to detect a given interaction for, a given minor allele frequency is calculated for differing degrees of measurement error in the assessment of the exposure and,the outcome.Results The required sample size is dependent upon the magnitude of the interaction, the allele frequency and the strength of the association in those with the common allele. As an example, we take the situation in which the effect size in those with the common allele was a quarter of a standard deviation change in the outcome for a standard deviation change in the exposure. If a minor allele with a frequency of 20% leads to a doubling of that effect size, then the sample size is highly dependent upon the precision with which the exposure and outcome are measured. rho(Tx) and rho(Ty) are the correlation between the measured exposure and outcome, respectively and the true value. if poor measures of the exposure and outcome are used, (e.g. rho(Tx) = 0.3, rho(Ty) = 0.4), then a study size of 150 989 people would be required to detect the interaction with 95% power at a significance level of 10(-4). Such an interaction could be detected in study samples of under 10 000 people if more precise. measurements of exposure and outcome. were made (e.g. rho(Tx) = 0.7, rho(Ty) = 0.7), and possibly in samples of under 5000 if the precision of estimation were enhanced by taking repeated measurements.Conclusions The formulae for calculating the sample size required to study the interaction between a continuous exposure and a genetic factor on a continuous outcome variable in the face of measurement error will be of considerable utility in designing studies with appropriate power. These calculations suggest that smaller studies with repeated and more precise measurement of the exposure and outcome will be as powerful as studies even 20 times bigger, which necessarily employ less precise measures because of their size. Even though the cost of genotyping is falling, the magnitude of the effect of measurement error on the power to detect interaction on continuous traits suggests that investment in studies with better measurement may be a more appropriate strategy than attempting to deal with error by increasing sample sizes.