Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
1] Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA [2] Bioinformatics Program, Boston University, Boston, MA, USA.
Eur J Hum Genet. 2014 Mar;22(3):414-8. doi: 10.1038/ejhg.2013.144. Epub 2013 Jul 10.
Several methods to correct for multiple testing within a gene region have been proposed. These methods are useful for candidate gene studies, and to fine map gene-regions from GWAs. The Bonferroni correction and permutation are common adjustments, but are overly conservative and computationally intensive, respectively. Other options include calculating the effective number of independent single-nucleotide polymorphisms (SNPs) or using theoretical approximations. Here, we compare a theoretical approximation based on extreme tail theory with four methods for calculating the effective number of independent SNPs. We evaluate the type-I error rates of these methods using single SNP association tests over 10 gene regions simulated using 1000 Genomes data. Overall, we find that the effective number of independent SNP method by Gao et al, as well as extreme tail theory produce type-I error rates at the or close to the chosen significance level. The type-I error rates for the other effective number of independent SNP methods vary by gene region characteristics. We find Gao et al and extreme tail theory to be efficient alternatives to more computationally intensive approaches to control for multiple testing in gene regions.
已经提出了几种在基因区域内进行多重检验校正的方法。这些方法对于候选基因研究和从 GWAS 精细映射基因区域非常有用。Bonferroni 校正和置换是常见的调整方法，但分别过于保守和计算密集。其他选择包括计算有效独立单核苷酸多态性（SNP）的数量或使用理论近似值。在这里，我们比较了基于极端尾理论的理论近似值和计算有效独立 SNP 数量的四种方法。我们使用 1000 基因组数据模拟的 10 个基因区域的单 SNP 关联测试来评估这些方法的Ⅰ型错误率。总体而言，我们发现 Gao 等人提出的有效独立 SNP 方法以及极端尾理论产生的Ⅰ型错误率在或接近所选的显著水平。其他有效独立 SNP 方法的Ⅰ型错误率因基因区域特征而异。我们发现 Gao 等人提出的有效独立 SNP 方法以及极端尾理论是控制基因区域多重检验的更计算密集方法的有效替代方法。
