Mendelian randomization for modern data: Integrating data resources to improve accuracy of causal estimates.

现代数据的孟德尔随机化：整合数据资源以提高因果估计的准确性。

• 批准号: 10716241
• 负责人: Jean V. Morrison
• 金额: $ 35.88万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-14 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10716241
• 关键词: Accounting; Address; Affect; Alzheimer' s disease risk; Awareness; Biological; C-reactive protein; Cardiometabolic Disease; Catalogs; Clinical; Computer software; Confounding Factors (Epidemiology); Coupled; Data; Data Set; Data Sources; Databases; Deposition; Elements; Ensure; Genotype; Goals; Heritability; Individual; Knowledge; LDL Cholesterol Lipoproteins; Linkage Disequilibrium; Measurable; Measures; Mediating; Mendelian randomization; Methodology; Methods; Modeling; Modernization; National Human Genome Research Institute; Outcome; Pattern; Performance; Plasma; Quality Control; Research Methodology; Research Personnel; Risk Factors; Role; Sampling Studies; Screening procedure; Selection Bias; Source; Statistical Methods; Techniques; Testing; Variant; Work; biobank; cardiometabolic risk; computerized tools; data integration; data resource; genetic association; genome wide association study; heart disease risk; human disease; improved; innovation; knowledge base; model building; multiple data sources; novel; open source; phenome; pleiotropism; public database; randomized trial; simulation; software development; statistics; study population; symposium; tool; trait; user friendly software; user-friendly

Project Summary/Abstract Mendelian randomization (MR) is a widely applicable causal inference technique that makes it possible to estimate causal effects using only summary association statistics from genome-wide association studies (GWAS). In recent years, MR has moved from being relatively unknown to a common element of post-GWAS analysis. By facilitating causal inference without a randomized trial, MR makes it possible to rapidly and cheaply assess potential risk factors for human disease. However, most MR methods rely on strong, sometimes unrealistic assumptions. When assumptions are violated, MR will produce biased, misleading results. The goals of this proposal are to 1) develop robust MR statistical methods that address the most crucial problems that arise in analysis of real data sets and 2) develop accessible open-source software to guide a user through the practical challenges of performing MR. We focus on two shortcomings of existing MR methods. First, horizontal pleiotropy is a well-known source of bias in MR. State-of-the-art MR methods are more robust to some types of horizontal pleiotropy than traditional methods. However, there are some forms of horizontal pleiotropy that can only be accounted for by augmenting the analysis with data for confounding variables via multivariable MR (MVMR). Current MVMR methods can only accommodate a few additional variables, while many problems would be best addressed by including larger numbers of traits. In Aim 1, we develop an MVMR method that is computationally scalable and remains accurate when large numbers of traits are included. In Aim 2, we extend this work, developing a method to automatically identify variables that should be included in an MVMR analysis. This is particularly important for understanding the causal role of exposures that have been sparsely studied or have only recently become measurable. In Aim 3, we focus on the challenges posed by linkage disequilibrium (LD). The majority of existing methods rely on LO-pruning variants to obtain an independent set, leading to a loss of valuable information. All current methods assume that LD is the same in the exposure and outcome GWAS. This assumption will not always hold, leading to errors that bias causal estimates. To address these problems, we develop an efficient screening tool to alert users when mis-matching LD may be affecting the results and an LD-aware MR method that can accommodate different LD patterns in exposure and outcome. The methods developed in this proposal will be distributed in user-friendly open-source software. Because the goals of Aims 1-3 are complimentary, in Aim 4 we will integrate these tools into an umbrella software package that guides users through the multiple choices involved in performing MR, from data selection and formatting to interpretion of results. The goal of this package is to address data considerations that are often ignored in methodological research, enabling investigators to obtain more robust, reliable inference.

项目概要/摘要 孟德尔随机化 (MR) 是一种广泛应用的因果推理技术，可以仅使用全基因组关联研究 (GWAS) 的汇总关联统计数据来估计因果效应。近年来，MR 已从相对不为人知变成了 GWAS 分析中的常见要素。通过在无需随机试验的情况下促进因果推断，MR 可以快速、廉价地评估人类疾病的潜在风险因素。然而，大多数 MR 方法都依赖于强有力的、有时不切实际的假设。当假设被违反时，MR 将产生有偏见的、误导性的结果。该提案的目标是 1) 开发强大的 MR 统计方法，解决实际数据集分析中出现的最关键问题；2) 开发可访问的开源软件，指导用户应对执行 MR 的实际挑战。我们关注现有 MR 方法的两个缺点。首先，水平多效性是 MR 中众所周知的偏差来源。最先进的 MR 方法对于某些类型的水平多效性比传统方法更稳健。然而，某些形式的水平多效性只能通过多变量 MR (MVMR) 的混杂变量数据增强分析来解释。当前的 MVMR 方法只能容纳一些额外的变量，而许多问题最好通过包含更多的特征来解决。在目标 1 中，我们开发了一种 MVMR 方法，该方法在计算上可扩展，并且在包含大量特征时仍保持准确。在目标 2 中，我们扩展了这项工作，开发了一种自动识别 MVMR 分析中应包含的变量的方法。这对于理解很少被研究或最近才变得可测量的暴露的因果作用尤其重要。在目标 3 中，我们重点关注连锁不平衡 (LD) 带来的挑战。大多数现有方法依赖LO剪枝变体来获得独立的集合，从而导致有价值信息的丢失。目前所有的方法都假设 LD 在暴露和结果 GWAS 中是相同的。这种假设并不总是成立，从而导致因果估计出现偏差的错误。为了解决这些问题，我们开发了一种有效的筛查工具，当不匹配的 LD 可能影响结果时提醒用户，并开发了一种 LD 感知 MR 方法，可以适应不同的 LD 暴露模式和结果。本提案中开发的方法将分布在用户友好的开源软件中。由于目标 1-3 的目标是互补的，因此在目标 4 中，我们将这些工具集成到一个伞式软件包中，引导用户完成执行 MR 时涉及的多种选择，从数据选择和格式化到结果解释。该软件包的目标是解决方法研究中经常被忽视的数据考虑因素，使研究人员能够获得更稳健、更可靠的推论。