目前的技术允许以单点分辨率快速评估人类基因组中成千上万个位点的DNA序列和甲基化水平。成千上万的人同时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)解释不清的人。然而,基因组和表观基因组交织在一起,例如,已知DNA甲基化通过影响基因表达,例如,基因组印记。因此,需要超越单组学数据分析,并开发允许来自EWAS和GWAS的信息的有意义组合的交互模型。
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。我们的方法搜索SNP等位基因和DNA甲基化(G×Me)之间的统计相互作用,以及亲本起源效应和DNA甲基化(PoO×Me)之间的统计相互作用。使用case-parent三元组或dyad。我们使用附近基因组区域的甲基化水平来简化生物学解释。这些方法在亲子关系的数据集上进行了测试,与后代的EWAS数据。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,我们展示了控制数据集如何识别假阳性。
新方法,G×Me和PoO×Me,在遗传相对风险评估中整合DNA甲基化,从而对全基因组扫描进行更全面的生物学解释。此外,我们在区域内浓缩DNA甲基化水平的策略有助于克服使用基于稀疏芯片的测量的特定缺点.这些方法在免费提供的R包Haplin(https://cran。r-project.org/package=Haplin),实现多组数据集的快速扫描。
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.