A summary-data-based Mendelian randomization method with application to correlated lipidomic data

项目总结 近年来，最先进的技术使得高通量分析组学标记成为可能，并且 这些新出现的数据已经成为了解复杂生物系统的强大工具。同时，基因组- 已经进行了与组学标记的广泛关联研究(GWAS)，并且正在出现的概述 统计学为检验组学标志物和疾病之间的因果关系提供了可评估的机会 结果采用孟德尔随机化(MR)。然而，大多数时候，这些新奇的组学数据是高的- 维度和相关性，给先生的使用带来了巨大的挑战。我们将制定一个摘要-数据- 基于 用于高维相关数据的MR方法。我们的模型是一个多变量线性混合模型， 创新之处在于，我们将考虑自变量和因变量的随机误差 固定效应模型和随机效应模型下的变量。我们的模型允许多个组学标记作为独立的 变量，并通过将P值不那么严格的变量包含为IV并逐步实现来增强能力 保留了最重要的选择--组学标记。我们将按组织进行分层分析 基因表达与生物学功能 用于测试因果关联的稳健性的遗传变异。W E将 开发一个R脚本，并与Github的整个研究社区共享(目标1)。我们将进行一项 模拟研究，比较我们的MR模型和以前的基于汇总数据的MR方法的性能 (目标2)。长期以来，高密度脂蛋白胆固醇(hdl-c)水平一直被认为是一个重要的反转因素。 冠心病的预测指标。然而，临床试验和孟德尔随机化表明， 高密度脂蛋白-c是高密度脂蛋白功能的一个很差的生物标志物，并提出了一个重要的问题，即是否有其他新的测量方法 高密度脂蛋白(即不同颗粒大小的高密度脂蛋白中的脂类)对心脏代谢性疾病可能有因果作用。高密度脂蛋白 是脂类成分高度不均匀的不同大小颗粒的集合。小的高密度脂蛋白 胆固醇流出能力越强， 磷脂和较低的甘油三酯和胆固醇， 而高密度脂蛋白的甘油三酯和胆固醇含量较高，磷脂含量较低。因此，它是 必须了解高密度脂蛋白中的脂类是否与心脏代谢性疾病有因果关系。 我们 将应用我们的磁共振方法来检查脂类(胆固醇、磷脂和甘油三酯)在 高密度脂蛋白颗粒(极大、大、中、小、极小)对心脏代谢性疾病(冠心病)风险的影响 疾病、中风和2型糖尿病)使用最近在英国生物库和现有的 汇总统计数据和来自 磁性联合体(目标3)。

PROJECT SUMMARY In recent years, state-of-the-art technologies have allowed for high-throughput profilings of -omics markers, and these emerging data have been powerful tools to understand complex biological systems. Meanwhile, genome- wide association studies (GWAS) with -omics markers have been conducted, and the emerging summary statistics has provided evaluable opportunity to examine causality between -omics markers and disease outcomes using Mendelian randomization (MR). However, most of the time, these novel -omics data are high- dimensional and correlated, imposing significant challenges in use of MR. We will develop a summary-data- based MR method for use with high-dimensional correlated data. Our mode is a multivariable linear mixed model, and the innovation lies in that we will account for random errors in both independent variables and dependent variable under fixed- and random-effects models. Our model allows for multiple -omics markers as independent variables, and boosts power by including variants with less stringent P value as IVs and implementing stepwise selection which retains the most important -omics markers. We will conduct stratified analysis by tissue expression and biological function of genetic variants to test for robustness of causal associations. W e will develop an R script and share it with the whole research community in Github (aim 1). We will conduct a simulation study to compare the performance of our MR model to previous summary-data-based-MR methods (aim 2). High-density lipoprotein cholesterol (HDL-c) levels have long been recognized an important inverse predictor of coronary heart disease. However, clinical trials and Mendelian randomization suggest that level of HDL-c is a poor biomarker of HDL function, and raise an important question about whether other novel measures of HDL (i.e. lipids in HDLs of different particle sizes) may have causal effects on cardiometabolic disease. HDL is a collection of particles with different sizes highly heterogeneous in lipid composition. Small HDL that exerts stronger cholesterol efflux capacity has higher contents of phospholipids and lower triglycerides and cholesterols, while large HDL has higher contents of triglycerides and cholesterols and lower phospholipids. Thus, it is imperative to understand whether lipids in HDL lipoproteins are causally related to cardiometabolic disease. We will apply our MR method to examine causal effects of lipids (cholesterol, phospholipids, and triglycerides) in HDL particles (very large, large, medium, small, very small) on risk of cardiometabolic disease (coronary heart disease, stroke, and type 2 diabetes) using the recently released lipidomics data in the UK Biobank and existing summary statistics with lipid fractions from the MAGNETIC consortium (aim 3).