Jiang Lai, Zhao Kaiqiong, Klein Kathleen, Canty Angelo J, Oualkacha Karim, Greenwood Celia M T
1Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 1020 Pine Avenue West, Quebec, Montreal H3A 1A2 Canada.
2Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Côte Ste. Catherine, Quebec, Montréal H3T 1E2 Canada.
BMC Proc. 2018 Sep 17;12(Suppl 9):20. doi: 10.1186/s12919-018-0117-x. eCollection 2018.
Using data on 680 patients from the GAW20 real data set, we conducted Mendelian randomization (MR) studies to explore the causal relationships between methylation levels at selected probes (cytosine-phosphate-guanine sites [CpGs]) and high-density lipoprotein (HDL) changes (Δ) using single-nucleotide polymorphisms (SNPs) as instrumental variables. Several methods were used to estimate the causal effects at CpGs of interest on Δ, including a newly developed method that we call (CIV). CIV performs automatic SNP selection while providing estimates of causal effects adjusted for possible pleiotropy, when the potentially-pleiotropic phenotypes are measured. For CpGs in or near the 10 genes identified as associated with Δ using a family-based VC-score test, we compared CIV to Egger regression and the two-stage least squares (TSLS) method. All 3 approaches selected at least 1CpG in 2 genes- and -as showing a causal relationship with Δ.
利用GAW20真实数据集的680名患者的数据，我们进行了孟德尔随机化（MR）研究，以探索选定探针（胞嘧啶-磷酸-鸟嘌呤位点 [CpG]）处的甲基化水平与高密度脂蛋白（HDL）变化（Δ）之间的因果关系，使用单核苷酸多态性（SNP）作为工具变量。我们使用了几种方法来估计感兴趣的CpG对Δ的因果效应，包括一种新开发的方法，我们称之为（CIV）。当测量潜在多效性表型时，CIV在提供针对可能的多效性进行调整的因果效应估计的同时，还能自动选择SNP。对于使用基于家族的VC评分测试确定与Δ相关的10个基因内或附近的CpG，我们将CIV与Egger回归和两阶段最小二乘法（TSLS）进行了比较。所有这三种方法在两个基因（和）中至少选择了1个CpG，显示出与Δ存在因果关系。
