Current technology allows rapid assessment of DNA sequences and methylation levels at a single-site resolution for hundreds of thousands of sites in the human genome, in thousands of individuals simultaneously. This has led to an increase in epigenome-wide association studies (EWAS) of complex traits, particularly those that are poorly explained by previous genome-wide association studies (GWAS). However, the genome and epigenome are intertwined, e.g., DNA methylation is known to affect gene expression through, for example, genomic imprinting. There is thus a need to go beyond single-omics data analyses and develop interaction models that allow a meaningful combination of information from EWAS and GWAS. We present two new methods for genetic association analyses that treat offspring DNA methylation levels as environmental exposure. Our approach searches for statistical interactions between SNP alleles and DNA methylation (G ×Me) and between parent-of-origin effects and DNA methylation (PoO ×Me), using case-parent triads or dyads. We use summarized methylation levels over nearby genomic region to ease biological interpretation. The methods were tested on a dataset of parent–offspring dyads, with EWAS data on the offspring. Our results showed that methylation levels around a SNP can significantly alter the estimated relative risk. Moreover, we show how a control dataset can identify false positives. The new methods, G ×Me and PoO ×Me, integrate DNA methylation in the assessment of genetic relative risks and thus enable a more comprehensive biological interpretation of genome-wide scans. Moreover, our strategy of condensing DNA methylation levels within regions helps overcome specific disadvantages of using sparse chip-based measurements. The methods are implemented in the freely available R package Haplin ( https://cran.r-project.org/package=Haplin ), enabling fast scans of multi-omics datasets.

中文翻译：

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基因甲基化相互作用：发现改变 SNP 相关疾病风险的区域性 DNA 甲基化水平。

目前的技术能够以单位点分辨率快速评估人类基因组中数十万个位点、同时评估数千个个体的 DNA 序列和甲基化水平。这导致了复杂性状的全表观基因组关联研究（EWAS）的增加，特别是那些以前的全基因组关联研究（GWAS）解释不清的性状。然而，基因组和表观基因组是交织在一起的，例如，已知DNA甲基化通过基因组印记等影响基因表达。因此，需要超越单一组学数据分析，并开发交互模型，以便对来自 EWAS 和 GWAS 的信息进行有意义的组合。我们提出了两种新的遗传关联分析方法，将后代 DNA 甲基化水平视为环境暴露。我们的方法使用案例亲本三联体或二联体来搜索 SNP 等位基因和 DNA 甲基化 (G ×Me) 之间以及亲本效应和 DNA 甲基化 (PoO ×Me) 之间的统计相互作用。我们使用附近基因组区域的甲基化水平汇总来简化生物学解释。这些方法在亲代-后代二元数据集以及后代的 EWAS 数据上进行了测试。我们的结果表明，SNP 周围的甲基化水平可以显着改变估计的相对风险。此外，我们还展示了控制数据集如何识别误报。新方法 G ×Me 和 PoO ×Me 将 DNA 甲基化整合到遗传相关风险的评估中，从而能够对全基因组扫描进行更全面的生物学解释。此外，我们压缩区域内 DNA 甲基化水平的策略有助于克服使用稀疏芯片测量的特定缺点。这些方法在免费提供的 R 包 Haplin ( https://cran.r-project.org/package=Haplin ) 中实现，从而能够快速扫描多组学数据集。