Franke Daniel, Philippi Anne, Tores Frédéric, Hager Jörg, Ziegler Andreas, König Inke R
Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Ratzeburger Allee 160, Haus 4, DE-23538 Lübeck, Germany.
Hum Hered. 2005;60(2):81-8. doi: 10.1159/000088528. Epub 2005 Sep 28.
Scherag et al. [Hum Hered 2002;54:210-217] recently proposed point estimates and asymptotic as well as exact confidence intervals for genotype relative risks (GRRs) and the attributable risk (AR) in case parent trio designs using single nucleotide polymorphism (SNP) data. The aim of this study was the investigation of coverage probabilities and bias in estimates if the marker locus is not identical to the disease locus. Using a variety of parameter constellations, including marker allele frequencies identical to and different from the SNP at the disease locus, we performed an analytical study to quantify the bias and a Monte-Carlo simulation study for quantifying both bias and coverage probabilities. No bias was observed if marker and trait locus coincided. Two parameters had a strong impact on coverage probabilities of confidence intervals and bias in point estimates if they did not coincide: the linkage disequilibrium (LD) parameter delta and the allele frequency at the marker SNP. If marker allele frequencies were different from the allele frequencies at the functional SNP, substantial biases occurred. Further, if delta between the marker and the disease locus was lower than the maximum possible delta, estimates were also biased. In general, biases were towards the null hypothesis for both GRRs and AR. If one GRR was not increased, as e.g. in a recessive genetic model, biases away from the null could be observed. If both GRRs were in identical directions and if both were substantially larger than 1, the bias always was towards the null. When applying point estimates and confidence intervals for GRRs and AR in candidate gene studies, great care is needed. Effect estimates are substantially biased towards the null if either the allele frequencies at the marker SNP and the true disease locus are different or if the LD between the marker SNP and the disease locus is not at its maximum. A bias away from the null occurs only in uncommon study situations; it is small and can therefore be ignored for applications.
舍拉格等人[《人类遗传学》2002年；54:210 - 217]最近针对使用单核苷酸多态性（SNP）数据的病例亲代三联体设计，提出了基因型相对风险（GRR）和归因风险（AR）的点估计值以及渐近和精确置信区间。本研究的目的是调查当标记位点与疾病位点不同时，估计值的覆盖概率和偏差。我们使用了多种参数组合，包括与疾病位点处的SNP相同和不同的标记等位基因频率，进行了一项分析研究以量化偏差，并进行了蒙特卡罗模拟研究以量化偏差和覆盖概率。当标记和性状位点一致时未观察到偏差。如果它们不一致，有两个参数对置信区间的覆盖概率和点估计值的偏差有很大影响：连锁不平衡（LD）参数δ和标记SNP处的等位基因频率。如果标记等位基因频率与功能性SNP处的等位基因频率不同，就会出现显著偏差。此外，如果标记与疾病位点之间的δ低于最大可能的δ，估计值也会有偏差。一般来说，GRR和AR的偏差都趋向于零假设。如果一个GRR没有增加，例如在隐性遗传模型中，可以观察到偏离零假设的偏差。如果两个GRR方向相同且都显著大于1，偏差总是趋向于零假设。在候选基因研究中应用GRR和AR的点估计值和置信区间时，需要格外小心。如果标记SNP处的等位基因频率与真正的疾病位点不同，或者标记SNP与疾病位点之间的LD未达到最大值，效应估计值会显著偏向零假设。只有在不常见的研究情况下才会出现偏离零假设的偏差；这种偏差很小，因此在应用中可以忽略不计。
