The MR-Base platform supports systematic causal inference across the human phenome.
The MR-Base platform supports systematic causal inference across the human phenome.
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DOI：
10.7554/elife.34408
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发表时间：
2018-05-30
期刊：
影响因子：
7.7
通讯作者：
Haycock PC
中科院分区：
文献类型：
--
作者：
Hemani G;Zheng J;Elsworth B;Wade KH;Haberland V;Baird D;Laurin C;Burgess S;Bowden J;Langdon R;Tan VY;Yarmolinsky J;Shihab HA;Timpson NJ;Evans DM;Relton C;Martin RM;Davey Smith G;Gaunt TR;Haycock PC
全基因组关联研究 (GWAS) 的结果可用于推断表型之间的因果关系，使用称为 2 样本孟德尔随机化 (2SMR) 的策略并绕过对个体水平数据的需求。然而，2SMR 方法正在迅速发展，GWAS 结果往往没有得到充分的策划，从而破坏了该方法的有效实施。因此，我们开发了 MR-Base (http://www.mrbase.org)：一个平台，集成了完整 GWAS 结果的精选数据库（根据统计显着性没有限制）与应用程序编程接口、Web 应用程序和 R 软件包，可自动执行 2SMR。该软件包括多项敏感性分析，用于评估水平多效性和其他违反假设的影响。该数据库目前包含来自 1673 个 GWAS 的 110 亿个单核苷酸多态性-性状关联，并定期更新。将数据与软件集成可确保更严格地应用假设驱动的分析，并允许在全表组关联研究中有效评估数百万个潜在因果关系。我们的健康受到许多暴露和风险因素的影响，包括我们的生活方式、环境和生物学的各个方面。然而，找出健康结果的原因可能很困难，因为健康不良会影响风险因素，而风险因素往往会相互影响。为了弄清楚特定的干预措施是否会影响健康结果，科学家们理想情况下会进行所谓的随机对照试验，其中一些随机选择的参与者接受改变风险因素的干预措施，而其他参与者则不接受。但进行这种类型的实验可能成本高昂或不切实际。另外，科学家还可以利用遗传学来模拟随机对照试验。这种技术（称为孟德尔随机化）之所以可行有两个原因。首先，因为一个人是否拥有某种基因版本本质上是随机的。其次，因为我们的基因影响不同的风险因素。例如，具有一种基因版本的人可能比具有另一种基因版本的人更容易喝酒。研究人员可以比较具有不同版本基因的人，以推断饮酒对其健康的影响。每天都有新的研究调查遗传变异在人类健康中的作用，科学家可以利用这些研究来使用孟德尔随机化进行研究。但到目前为止，这些研究的完整结果尚未整理到一个地方。与此同时，孟德尔随机化的统计方法正在不断发展和改进。为了利用这些进步，Hemani、Zheng、Elsworth 等人。制作了一个名为“MR-Base”的计算机程序和在线平台，将最新的遗传数据与最新的统计方法相结合。 MR-Base 使孟德尔随机化过程自动化，使研究速度大大加快：以前需要数月才能完成的分析现在只需几分钟即可完成。它还使研究更加可靠，降低人为错误的风险并确保科学家使用最新的方法。 MR-Base 包含人类基因与健康相关结果之间超过 110 亿个关联。这将使研究人员能够调查健康状况不佳的许多潜在原因。随着新的统计方法和遗传学研究的新发现被添加到 MR-Base 中，它对研究人员的价值将会增长。
Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies. Our health is affected by many exposures and risk factors, including aspects of our lifestyles, our environments, and our biology. It can, however, be hard to work out the causes of health outcomes because ill-health can influence risk factors and risk factors tend to influence each other. To work out whether particular interventions influence health outcomes, scientists will ideally conduct a so-called randomized controlled trial, where some randomly-chosen participants are given an intervention that modifies the risk factor and others are not. But this type of experiment can be expensive or impractical to conduct. Alternatively, scientists can also use genetics to mimic a randomized controlled trial. This technique – known as Mendelian randomization – is possible for two reasons. First, because it is essentially random whether a person has one version of a gene or another. Second, because our genes influence different risk factors. For example, people with one version of a gene might be more likely to drink alcohol than people with another version. Researchers can compare people with different versions of the gene to infer what effect alcohol drinking has on their health. Every day, new studies investigate the role of genetic variants in human health, which scientists can draw on for research using Mendelian randomization. But until now, complete results from these studies have not been organized in one place. At the same time, statistical methods for Mendelian randomization are continually being developed and improved. To take advantage of these advances, Hemani, Zheng, Elsworth et al. produced a computer programme and online platform called “MR-Base”, combining up-to-date genetic data with the latest statistical methods. MR-Base automates the process of Mendelian randomization, making research much faster: analyses that previously could have taken months can now be done in minutes. It also makes studies more reliable, reducing the risk of human error and ensuring scientists use the latest methods. MR-Base contains over 11 billion associations between people’s genes and health-related outcomes. This will allow researchers to investigate many potential causes of poor health. As new statistical methods and new findings from genetic studies are added to MR-Base, its value to researchers will grow.