Nustad Haakon E, Almeida Marcio, Canty Angelo J, LeBlanc Marissa, Page Christian M, Melton Phillip E
Department of Medical Genetics, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway.
Faculty of Medicine, University of Oslo, Klaus Torgårds vei 3, 0372, Oslo, Norway.
BMC Genet. 2018 Sep 17;19(Suppl 1):77. doi: 10.1186/s12863-018-0648-1.
Longitudinal data and repeated measurements in epigenome-wide association studies (EWAS) provide a rich resource for understanding epigenetics. We summarize 7 analytical approaches to the GAW20 data sets that addressed challenges and potential applications of phenotypic and epigenetic data. All contributions used the GAW20 real data set and employed either linear mixed effect (LME) models or marginal models through generalized estimating equations (GEE). These contributions were subdivided into 3 categories: (a) quality control (QC) methods for DNA methylation data; (b) heritability estimates pretreatment and posttreatment with fenofibrate; and (c) impact of drug response pretreatment and posttreatment with fenofibrate on DNA methylation and blood lipids.
Two contributions addressed QC and identified large statistical differences with pretreatment and posttreatment DNA methylation, possibly a result of batch effects. Two contributions compared epigenome-wide heritability estimates pretreatment and posttreatment, with one employing a Bayesian LME and the other using a variance-component LME. Density curves comparing these studies indicated these heritability estimates were similar. Another contribution used a variance-component LME to depict the proportion of heritability resulting from a genetic and shared environment. By including environmental exposures as random effects, the authors found heritability estimates became more stable but not significantly different. Two contributions investigated treatment response. One estimated drug-associated methylation effects on triglyceride levels as the response, and identified 11 significant cytosine-phosphate-guanine (CpG) sites with or without adjusting for high-density lipoprotein. The second contribution performed weighted gene coexpression network analysis and identified 6 significant modules of at least 30 CpG sites, including 3 modules with topological differences pretreatment and posttreatment.
Four conclusions from this GAW20 working group are: (a) QC measures are an important consideration for EWAS studies that are investigating multiple time points or repeated measurements; (b) application of heritability estimates between time points for individual CpG sites is a useful QC measure for DNA methylation studies; (c) drug intervention demonstrated strong epigenome-wide DNA methylation patterns across the 2 time points; and (d) new statistical methods are required to account for the environmental contributions of DNA methylation across time. These contributions demonstrate numerous opportunities exist for the analysis of longitudinal data in future epigenetic studies.
表观基因组关联研究（EWAS）中的纵向数据和重复测量为理解表观遗传学提供了丰富资源。我们总结了针对GAW20数据集的7种分析方法，这些方法解决了表型和表观遗传数据的挑战及潜在应用。所有投稿均使用了GAW20真实数据集，并通过广义估计方程（GEE）采用线性混合效应（LME）模型或边际模型。这些投稿被分为3类：（a）DNA甲基化数据的质量控制（QC）方法；（b）非诺贝特治疗前后的遗传力估计；（c）非诺贝特治疗前后药物反应对DNA甲基化和血脂的影响。
两篇投稿涉及质量控制，并发现治疗前和治疗后DNA甲基化存在较大统计差异，这可能是批次效应的结果。两篇投稿比较了治疗前和治疗后的表观基因组遗传力估计，一篇采用贝叶斯LME，另一篇使用方差成分LME。比较这些研究的密度曲线表明，这些遗传力估计相似。另一篇投稿使用方差成分LME来描述遗传和共享环境导致的遗传力比例。通过将环境暴露作为随机效应纳入，作者发现遗传力估计变得更加稳定，但差异不显著。两篇投稿研究了治疗反应。一篇估计了药物相关甲基化对甘油三酯水平的影响作为反应，并确定了11个显著的胞嘧啶 - 磷酸 - 鸟嘌呤（CpG）位点，无论是否调整高密度脂蛋白。第二篇投稿进行了加权基因共表达网络分析，并确定了6个至少包含30个CpG位点的显著模块，其中3个模块在治疗前和治疗后存在拓扑差异。
这个GAW20工作组得出的四个结论是：（a）质量控制措施是EWAS研究中调查多个时间点或重复测量时的重要考虑因素；（b）在个体CpG位点的时间点之间应用遗传力估计是DNA甲基化研究中一种有用的质量控制措施；（c）药物干预在两个时间点显示出强烈的全表观基因组DNA甲基化模式；（d）需要新的统计方法来考虑DNA甲基化随时间的环境贡献。这些投稿表明，在未来的表观遗传研究中，纵向数据分析存在众多机会。
