Schaid D J
Department of Health Sciences Research, Mayo Clinic/Mayo Foundation, Rochester, Minnesota 55905, USA.
Genet Epidemiol. 1999;16(3):250-60. doi: 10.1002/(SICI)1098-2272(1999)16:3<250::AID-GEPI2>3.0.CO;2-T.
Association studies using diseased cases and their parents avoid biases due to population stratification, and the transmission/disequilibrium test (TDT) is a popular method of analysis. Sample size and power calculations for the TDT method have been reported, but often for the special situation of multiplicative effects of alleles on the genotype relative risks. Furthermore, some of the proposed calculations ignore the dependence of transmitted alleles from a pair of heterozygous parents when the effects are not multiplicative, which can lead to erroneous sample size calculations. We demonstrate how to calculate sample size and power for the TDT method for general genotype relative risks. As an alternative to the TDT method, we present likelihood methods for a variety of genotype relative risk models. Exact likelihood methods are presented to allow for accurate small-sample analyses. We demonstrate by numerical comparisons: (1) that the TDT method is inefficient for recessive patterns of relative risks, (2) for alleles that are not rare, falsely assuming a multiplicative model can lead to gross underestimation of the required sample size for the TDT statistic, and (3) for common alleles, if the true genotype relative risks have an approximately dominant pattern, then the TDT method can be grossly inefficient compared to likelihood methods. An alternative likelihood ratio statistic, based on two degrees of freedom, tends to be robust for a wide range of genotype relative risk models. Finally, we discuss how to use standard software for conditional logistic regression to accurately assess effects of alleles as well as genotype-environment interaction.
使用患病病例及其父母进行的关联研究可避免因群体分层导致的偏差,而传递/不平衡检验(TDT)是一种常用的分析方法。关于TDT方法的样本量和效能计算已有报道,但通常是针对等位基因对基因型相对风险的乘法效应这种特殊情况。此外,一些提出的计算方法在效应不是乘法效应时忽略了来自一对杂合父母的传递等位基因之间的依赖性,这可能导致错误的样本量计算。我们展示了如何针对一般的基因型相对风险计算TDT方法的样本量和效能。作为TDT方法的替代方法,我们针对多种基因型相对风险模型提出了似然方法。提出了精确似然方法以进行准确的小样本分析。我们通过数值比较证明:(1)TDT方法对于隐性相对风险模式效率低下;(2)对于并非罕见的等位基因,错误地假设乘法模型会导致对TDT统计量所需样本量的严重低估;(3)对于常见等位基因,如果真实的基因型相对风险具有近似显性模式,那么与似然方法相比,TDT方法可能效率极低。一种基于两个自由度的替代似然比统计量对于广泛的基因型相对风险模型往往具有稳健性。最后,我们讨论了如何使用用于条件逻辑回归的标准软件来准确评估等位基因的效应以及基因型 - 环境相互作用。