Longitudinal analysis strategies for modelling epigenetic trajectories.
Longitudinal analysis strategies for modelling epigenetic trajectories.
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DOI：
10.1093/ije/dyy012
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发表时间：
2018-04-01
影响因子：
7.7
通讯作者：
Tilling K
中科院分区：
文献类型：
--
作者：
Staley JR;Suderman M;Simpkin AJ;Gaunt TR;Heron J;Relton CL;Tilling K
众所周知，DNA甲基化水平会随着时间的推移而变化，对这些轨迹进行建模对于我们理解这些变化随时间变化的生物学相关性至关重要。然而，由于在表观基因组中拟合多水平模型的计算成本，到目前为止，大多数轨迹建模工作都集中在通过表观基因组范围的关联研究(EWAS)在个别时间点确定的CpG位点的子集。我们建议在重复测量中使用线性回归，使用三明治估计器估计集群稳健的标准误差，作为一种比多水平建模计算量更小的策略。我们比较了这两种纵向方法，以及基于Ewas的三种方法(在基线、任何时间点和所有时间点关联)，使用模拟来识别与暴露相关的表观遗传学随时间的变化，并将它们应用于来自可访问的表观基因组学研究资源(ARIES)的血液DNA甲基化特征。将关联性测试限制在基线上，确定的关联性集合不如在每个时间点执行EWAS或将纵向建模方法应用于整个数据集。具有聚类稳健标准误差的线性回归模型确定了类似的关联集合，其效果估计几乎与多水平模型相同，同时效率也是多水平模型的74倍。这两种纵向建模方法都确定了白羊座中与产前吸烟相关的类似的CpG位点集(>；70%的一致性)。具有聚类稳健标准误差的线性回归是DNA甲基化数据纵向分析的一种合适而有效的方法。
DNA methylation levels are known to vary over time, and modelling these trajectories is crucial for our understanding of the biological relevance of these changes over time. However, due to the computational cost of fitting multilevel models across the epigenome, most trajectory modelling efforts to date have focused on a subset of CpG sites identified through epigenome-wide association studies (EWAS) at individual time-points. We propose using linear regression across the repeated measures, estimating cluster-robust standard errors using a sandwich estimator, as a less computationally intensive strategy than multilevel modelling. We compared these two longitudinal approaches, as well as three approaches based on EWAS (associated at baseline, at any time-point and at all time-points), for identifying epigenetic change over time related to an exposure using simulations and by applying them to blood DNA methylation profiles from the Accessible Resource for Integrated Epigenomics Studies (ARIES). Restricting association testing to EWAS at baseline identified a less complete set of associations than performing EWAS at each time-point or applying the longitudinal modelling approaches to the full dataset. Linear regression models with cluster-robust standard errors identified similar sets of associations with almost identical estimates of effect as the multilevel models, while also being 74 times more efficient. Both longitudinal modelling approaches identified comparable sets of CpG sites in ARIES with an association with prenatal exposure to smoking (>70% agreement). Linear regression with cluster-robust standard errors is an appropriate and efficient approach for longitudinal analysis of DNA methylation data.
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影响因子：
3.8
作者：
Dedeurwaerder, Sarah;Defrance, Matthieu;Fuks, Francois
通讯作者：
Fuks, Francois
影响因子：
9.8
作者：
Joubert, Bonnie R.;Felix, Janine F.;London, Stephanie J.
通讯作者：
London, Stephanie J.
通过替代变量分析捕获基因表达研究中的异质性。
影响因子：
4.5
作者：
Leek, Jeffrey T.;Storey, John D.
通讯作者：
Storey, John D.
DOI：
10.1038/nrg3000
发表时间：
2011-07-12
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
通讯作者：
--
影响因子：
7.7
作者：
Relton CL;Gaunt T;McArdle W;Ho K;Duggirala A;Shihab H;Woodward G;Lyttleton O;Evans DM;Reik W;Paul YL;Ficz G;Ozanne SE;Wipat A;Flanagan K;Lister A;Heijmans BT;Ring SM;Davey Smith G
通讯作者：
Davey Smith G