van Iterson Maarten, van Zwet Erik W, Heijmans Bastiaan T
Molecular Epidemiology section, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, the Netherlands.
Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, the Netherlands.
Genome Biol. 2017 Jan 27;18(1):19. doi: 10.1186/s13059-016-1131-9.
We show that epigenome- and transcriptome-wide association studies (EWAS and TWAS) are prone to significant inflation and bias of test statistics, an unrecognized phenomenon introducing spurious findings if left unaddressed. Neither GWAS-based methodology nor state-of-the-art confounder adjustment methods completely remove bias and inflation. We propose a Bayesian method to control bias and inflation in EWAS and TWAS based on estimation of the empirical null distribution. Using simulations and real data, we demonstrate that our method maximizes power while properly controlling the false positive rate. We illustrate the utility of our method in large-scale EWAS and TWAS meta-analyses of age and smoking.
我们表明，表观基因组和转录组全关联研究（EWAS和TWAS）容易出现检验统计量的显著膨胀和偏差，这是一种未被认识到的现象，如果不加以解决，会引入虚假发现。基于全基因组关联研究（GWAS）的方法和最先进的混杂因素调整方法都不能完全消除偏差和膨胀。我们提出一种贝叶斯方法，基于经验零分布的估计来控制EWAS和TWAS中的偏差和膨胀。通过模拟和实际数据，我们证明我们的方法在适当控制假阳性率的同时最大限度地提高了功效。我们阐述了我们的方法在年龄和吸烟的大规模EWAS和TWAS荟萃分析中的效用。
