Klein Alison P, Kovac Ilija, Sorant Alexa J M, Baffoe-Bonnie Agnes, Doan Betty Q, Ibay Grace, Lockwood Erica, Mandal Diptasri, Santhosh Lekshmi, Weissbecker Karen, Woo Jessica, Zambelli-Weiner April, Zhang Jie, Naiman Daniel Q, Malley James, Bailey-Wilson Joan E
Inherited Disease Research Branch, NHGRI, NIH, Baltimore, Maryland, USA.
BMC Genet. 2003 Dec 31;4 Suppl 1(Suppl 1):S73. doi: 10.1186/1471-2156-4-S1-S73.
Using the Genetic Analysis Workshop 13 simulated data set, we compared the technique of importance sampling to several other methods designed to adjust p-values for multiple testing: the Bonferroni correction, the method proposed by Feingold et al., and naïve Monte Carlo simulation. We performed affected sib-pair linkage analysis for each of the 100 replicates for each of five binary traits and adjusted the derived p-values using each of the correction methods. The type I error rates for each correction method and the ability of each of the methods to detect loci known to influence trait values were compared. All of the methods considered were conservative with respect to type I error, especially the Bonferroni method. The ability of these methods to detect trait loci was also low. However, this may be partially due to a limitation inherent in our binary trait definitions.
使用遗传分析研讨会13的模拟数据集，我们将重要性抽样技术与其他几种旨在针对多重检验调整p值的方法进行了比较：Bonferroni校正、Feingold等人提出的方法以及朴素蒙特卡罗模拟。对于五个二元性状中的每一个，我们对100次重复中的每一次进行了受累同胞对连锁分析，并使用每种校正方法调整了导出的p值。比较了每种校正方法的I型错误率以及每种方法检测已知影响性状值的基因座的能力。所考虑的所有方法在I型错误方面都很保守，尤其是Bonferroni方法。这些方法检测性状基因座的能力也很低。然而，这可能部分归因于我们二元性状定义中固有的局限性。
