Multiple testing corrections for imputed SNPs.
Multiple testing corrections for imputed SNPs.
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DOI：
10.1002/gepi.20563
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发表时间：
2011-04
影响因子：
2.1
通讯作者：
Gao, Xiaoyi
中科院分区：
文献类型：
--
作者：
Gao, Xiaoyi
多重检验校正是遗传关联研究中的一个活跃的研究课题，特别是对于全基因组关联研究（GWAS），其中现在在估计等位基因剂量的数百万个估算的SNP上进行与性状的关联检验。未能适当地解决多重比较可能会引入过多的假阳性结果，并使后续研究对这些结果的跟踪效率低下。排列测试被认为是多重测试调整中的金标准;然而，该过程对计算要求很高，尤其是对于GWAS。值得注意的是，在真实的数据集中大量估计的等位基因剂量的排列阈值尚未报道。虽然许多研究人员最近开发了算法，以快速近似的置换阈值与置换测试的准确性相似，这些方法还没有得到验证，估计等位基因剂量。在这项研究中，我们比较了最近发表的多个测试校正方法使用2.5M估计等位基因剂量。我们还根据10，000个GWAS结果在无关联的零假设下推导出排列显著性水平。我们的研究结果表明，simpleM方法与估计的等位基因剂量，并给出了最接近的近似置换阈值，同时需要最少的计算时间。
Multiple testing corrections are an active research topic in genetic association studies, especially for genome-wide association studies (GWAS), where tests of association with traits are conducted at millions of imputed SNPs with estimated allelic dosages now. Failure to address multiple comparisons appropriately can introduce excess false positive results and make subsequent studies following up those results inefficient. Permutation tests are considered the gold standard in multiple testing adjustment; however, this procedure is computationally demanding, especially for GWAS. Notably, the permutation thresholds for the huge number of estimated allelic dosages in real data sets have not been reported. Although many researchers have recently developed algorithms to rapidly approximate the permutation thresholds with accuracy similar to the permutation test, these methods have not been verified with estimated allelic dosages. In this study, we compare recently published multiple testing correction methods using 2.5M estimated allelic dosages. We also derive permutation significance levels based on 10,000 GWAS results under the null hypothesis of no association. Our results show that the simpleM method works well with estimated allelic dosages and gives the closest approximation to the permutation threshold while requiring the least computation time.
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