Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing
Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing
复制标题
利用生物学和统计协变量提高表观基因组范围关联测试的检测能力
DOI：
10.1186/s13059-020-02001-7
复制
发表时间：
2020-04-06
期刊：
影响因子：
12.3
通讯作者：
Chen, Jun
中科院分区：
文献类型：
--
作者：
Huang, Jinyan;Bai, Ling;Chen, Jun
背景全表观基因组关联研究（EWAS）寻求表观遗传标记与结果或暴露之间的关联，涉及多重假设检验。错误发现率（FDR）控制已广泛用于多重测试校正。然而，传统的 FDR 控制方法不使用辅助协变量，如果协变量可以告知原假设的可能性，那么它们的作用可能会减弱。最近，已经开发了许多协变量自适应 FDR 控制方法，但尚未探索将这些方法应用于 EWAS 数据。目前尚不清楚这些方法是否可以显着提高检测能力，如果可以，哪些协变量与 EWAS 数据更相关。结果在本研究中，我们使用模拟和真实的 EWAS 数据集评估了五种协变量自适应 FDR 控制方法与 EWAS 相关协变量的性能。我们开发了一项综合测试来评估协变量的信息量。我们发现统计协变量通常比生物协变量提供更多信息，并且甲基化均值和方差的协变量几乎普遍提供信息。相反，生物协变量的信息量取决于特定的数据集。我们表明，独立假设加权 (IHW) 和协变量自适应多重测试 (CAMT) 方法总体上更强大，尤其是对于稀疏信号，与 ST 程序相比，可以将真实数据集上的检测能力提高中值 25% 和 68%。我们进一步在各种生物学背景下验证了这些发现。结论具有信息性协变量的协变量自适应 FDR 控制方法可以显着提高 EWAS 的检测能力。对于稀疏信号，建议使用 IHW 和 CAMT。
BackgroundEpigenome-wide association studies (EWAS), which seek the association between epigenetic marks and an outcome or exposure, involve multiple hypothesis testing. False discovery rate (FDR) control has been widely used for multiple testing correction. However, traditional FDR control methods do not use auxiliary covariates, and they could be less powerful if the covariates could inform the likelihood of the null hypothesis. Recently, many covariate-adaptive FDR control methods have been developed, but application of these methods to EWAS data has not yet been explored. It is not clear whether these methods can significantly improve detection power, and if so, which covariates are more relevant for EWAS data.ResultsIn this study, we evaluate the performance of five covariate-adaptive FDR control methods with EWAS-related covariates using simulated as well as real EWAS datasets. We develop an omnibus test to assess the informativeness of the covariates. We find that statistical covariates are generally more informative than biological covariates, and the covariates of methylation mean and variance are almost universally informative. In contrast, the informativeness of biological covariates depends on specific datasets. We show that the independent hypothesis weighting (IHW) and covariate adaptive multiple testing (CAMT) method are overall more powerful, especially for sparse signals, and could improve the detection power by a median of 25% and 68% on real datasets, compared to the ST procedure. We further validate the findings in various biological contexts.ConclusionsCovariate-adaptive FDR control methods with informative covariates can significantly increase the detection power for EWAS. For sparse signals, IHW and CAMT are recommended.