Gao Xiaoyi, Starmer Joshua, Martin Eden R
Center for Genetic Epidemiology and Statistical Genetics, Miami Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida 33136, USA.
Genet Epidemiol. 2008 May;32(4):361-9. doi: 10.1002/gepi.20310.
Multiple testing is a challenging issue in genetic association studies using large numbers of single nucleotide polymorphism (SNP) markers, many of which exhibit linkage disequilibrium (LD). Failure to adjust for multiple testing appropriately may produce excessive false positives or overlook true positive signals. The Bonferroni method of adjusting for multiple comparisons is easy to compute, but is well known to be conservative in the presence of LD. On the other hand, permutation-based corrections can correctly account for LD among SNPs, but are computationally intensive. In this work, we propose a new multiple testing correction method for association studies using SNP markers. We show that it is simple, fast and more accurate than the recently developed methods and is comparable to permutation-based corrections using both simulated and real data. We also demonstrate how it might be used in whole-genome association studies to control type I error. The efficiency and accuracy of the proposed method make it an attractive choice for multiple testing adjustment when there is high intermarker LD in the SNP data set.
在使用大量单核苷酸多态性（SNP）标记的基因关联研究中，多重检验是一个具有挑战性的问题，其中许多标记表现出连锁不平衡（LD）。未能适当地调整多重检验可能会产生过多的假阳性结果或忽略真阳性信号。用于调整多重比较的Bonferroni方法易于计算，但在存在LD的情况下众所周知是保守的。另一方面，基于置换的校正可以正确考虑SNP之间的LD，但计算量很大。在这项工作中，我们提出了一种用于使用SNP标记的关联研究的新的多重检验校正方法。我们表明，它比最近开发的方法更简单、快速且更准确，并且在使用模拟数据和真实数据时与基于置换的校正相当。我们还展示了它如何用于全基因组关联研究以控制I型错误。当SNP数据集中存在高度的标记间LD时，所提出方法的效率和准确性使其成为多重检验调整的一个有吸引力的选择。
