Genome Research Center, AbbVie, North Chicago, IL 60064, USA.
Department of Population Health Sciences, Augusta University, Augusta, GA 30912, USA.
Genes (Basel). 2019 Apr 12;10(4):298. doi: 10.3390/genes10040298.
Researchers in genomics are increasingly interested in epigenetic factors such as DNA methylation because they play an important role in regulating gene expression without changes in the sequence of DNA. Abnormal DNA methylation is associated with many human diseases.
We propose two different approaches to test for differentially methylated regions (DMRs) associated with complex traits, while accounting for correlations among CpG sites in the DMRs. The first approach is a nonparametric method using a kernel distance statistic and the second one is a likelihood-based method using a binomial spatial scan statistic. The kernel distance method uses the kernel function, while the binomial scan statistic approach uses a mixed-effects model to incorporate correlations among CpG sites. Extensive simulations show that both approaches have excellent control of type I error, and both have reasonable statistical power. The binomial scan statistic approach appears to have higher power, while the kernel distance method is computationally faster. The proposed methods are demonstrated using data from a chronic lymphocytic leukemia (CLL) study.
基因组学研究人员越来越关注表观遗传因素，如 DNA 甲基化，因为它们在调节基因表达方面发挥着重要作用，而不会改变 DNA 的序列。异常的 DNA 甲基化与许多人类疾病有关。
我们提出了两种不同的方法来检测与复杂性状相关的差异甲基化区域（DMR），同时考虑了 DMR 中 CpG 位点之间的相关性。第一种方法是一种使用核距离统计量的非参数方法，第二种方法是一种基于似然的方法，使用二项式空间扫描统计量。核距离方法使用核函数，而二项式扫描统计量方法使用混合效应模型来合并 CpG 位点之间的相关性。广泛的模拟表明，这两种方法都具有出色的第一类错误控制能力，并且都具有合理的统计功效。该二项式扫描统计量方法似乎具有更高的功效，而核距离方法在计算上更快。所提出的方法使用慢性淋巴细胞白血病（CLL）研究的数据进行了演示。
