A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms
A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms
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DOI：
10.1002/gepi.20310
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发表时间：
2008-05-01
影响因子：
2.1
通讯作者：
Martin, Eden R.
中科院分区：
文献类型：
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作者：
Gao, Xiaoyi;Stamier, Joshua;Martin, Eden R.
在使用大量单核苷酸多态性（SNP）标记的遗传关联研究中，多重检测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）。未能适当调整多次测试可能会产生过多的假阳性或忽略真阳性信号。Bonferroni调整多重比较的方法易于计算，但众所周知，在LID存在时是保守的。另一方面，基于排列的校正可以正确地解释snp之间的LID，但计算量很大。在这项工作中，我们提出了一种新的多重测试校正方法，用于使用SNP标记进行关联研究。我们表明，它比最近开发的方法简单，快速和更准确，并且可以与使用模拟和实际数据的基于排列的校正相媲美。我们还演示了如何在全基因组关联研究中使用它来控制I型错误。该方法的效率和准确性使其成为SNP数据集中存在高标记间LID时多次测试调整的理想选择。
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 LID. On the other hand, permutation-based corrections can correctly account for LID 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 LID in the SNP data set.