Understanding the influence of genetic variants on DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease. There is a need for systematic approaches not only for determining methylation quantitative trait loci (methQTL), but also for discriminating general from cell type-specific effects. Here, we present a two-step computational framework MAGAR ( https://bioconductor.org/packages/MAGAR ), which fully supports the identification of methQTLs from matched genotyping and DNA methylation data, and additionally allows for illuminating cell type-specific methQTL effects. In a pilot analysis, we apply MAGAR on data in four tissues (ileum, rectum, T cells, B cells) from healthy individuals and demonstrate the discrimination of common from cell type-specific methQTLs. We experimentally validate both types of methQTLs in an independent data set comprising additional cell types and tissues. Finally, we validate selected methQTLs located in the PON1, ZNF155, and NRG2 genes by ultra-deep local sequencing. In line with previous reports, we find cell type-specific methQTLs to be preferentially located in enhancer elements. Our analysis demonstrates that a systematic analysis of methQTLs provides important new insights on the influences of genetic variants to cell type-specific epigenomic variation.

中文翻译：

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使用 MAGAR 鉴定健康个体中组织特异性和常见甲基化数量性状基因座

了解遗传变异对 DNA 甲基化的影响对于在疾病背景下解释表观基因组数据至关重要。需要系统的方法，不仅用于确定甲基化数量性状基因座 (methQTL)，还用于区分一般的细胞类型特异性效应。在这里，我们提出了一个两步计算框架 MAGAR (https://bioconductor.org/packages/MAGAR)，它完全支持从匹配的基因分型和 DNA 甲基化数据中识别 methQTL，另外还允许阐明细胞类型特异性的 methQTL效果。在初步分析中，我们将 MAGAR 应用于来自健康个体的四种组织（回肠、直肠、T 细胞、B 细胞）中的数据，并证明了常见与细胞类型特异性 methQTL 的区别。我们在包含其他细胞类型和组织的独立数据集中通过实验验证了这两种类型的 methQTL。最后，我们通过超深度局部测序验证了位于 PON1、ZNF155 和 NRG2 基因中的选定 methQTL。根据之前的报道，我们发现细胞类型特异性 methQTLs 优先位于增强子元件中。我们的分析表明，对 methQTLs 的系统分析为遗传变异对细胞类型特异性表观基因组变异的影响提供了重要的新见解。