Multiple methods have been proposed to aggregate genetic variants in a gene or a region and jointly test their association with a trait of interest. However, these joint tests do not provide estimates of the individual effect of each variant. Moreover, few methods have evaluated the joint association of multiple variants with DNA methylation. We propose a method based on linear mixed models to estimate the joint and individual effect of multiple genetic variants on DNA methylation leveraging genomic annotations. Our approach is flexible, can incorporate covariates and annotation features, and takes into account relatedness and linkage disequilibrium (LD). Our method had correct Type‐I error and overall high power for different simulated scenarios where we varied the number and specificity of functional annotations, number of causal and total genetic variants, frequency of genetic variants, LD, and genetic variant effect. Our method outperformed the family Sequence Kernel Association Test and had more stable estimations of effects than a classical single‐variant linear mixed‐effect model. Applied genome‐wide to the Framingham Heart Study data, our method identified 921 DNA methylation sites influenced by at least one rare or low‐frequency genetic variant located within 50 kilobases (kb) of the DNA methylation site.

中文翻译：

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JEM：评估多种遗传变异对 DNA 甲基化影响的联合测试

已经提出了多种方法来聚合基因或区域中的遗传变异，并联合测试它们与感兴趣的性状的关联。然而，这些联合测试并未提供对每个变体的个体效应的估计。此外，很少有方法评估多个变体与 DNA 甲基化的联合关联。我们提出了一种基于线性混合模型的方法，利用基因组注释来估计多个遗传变异对 DNA 甲基化的联合和个体影响。我们的方法很灵活，可以结合协变量和注释特征，并考虑到相关性和连锁不平衡（LD）。对于不同的模拟场景，我们的方法具有正确的 I 型错误和总体高功率，其中我们改变了功能注释的数量和特异性，因果和总遗传变异的数量、遗传变异的频率、LD 和遗传变异效应。我们的方法优于家族序列核关联检验，并且比经典的单变量线性混合效应模型具有更稳定的效应估计。将全基因组应用到弗雷明汉心脏研究数据中，我们的方法确定了 921 个 DNA 甲基化位点，这些位点受位于 DNA 甲基化位点 50 千碱基 (kb) 内的至少一种罕见或低频遗传变异的影响。