利用全基因组关联研究数据进行表型因果推断:孟德尔随机化及其他。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Annual Review of Biomedical Data Science Pub Date : 2022-08-10 Epub Date: 2022-04-01 DOI:10.1146/annurev-biodatasci-122120-024910
Venexia M Walker, Jie Zheng, Tom R Gaunt, George Davey Smith
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引用次数: 0

摘要

越来越多的全基因组关联研究(GWAS)统计数据可用于下游分析。同时,随着我们希望为新型医疗和公共卫生干预措施收集可靠证据,因果推断方法也越来越受欢迎。因此,我们开发了使用 GWAS 摘要统计进行因果推断的方法。在此,我们将按照复杂程度的递增顺序介绍这些方法,从遗传关联到孟德尔随机化的扩展(同时考虑数千种表型)。在考虑研究人员利用 GWAS 数据进行因果推断所面临的挑战之前,我们还将介绍这些方法的假设和局限性。GWAS 统计摘要是因果推断研究的一个重要数据源,在三角测量证据时可与非遗传方法相抗衡。继续努力解决使用 GWAS 数据进行因果推断时所面临的挑战,将使这些方法的影响得以充分发挥。
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Phenotypic Causal Inference Using Genome-Wide Association Study Data: Mendelian Randomization and Beyond.

statistics for genome-wide association studies (GWAS) are increasingly available for downstream analyses. Meanwhile, the popularity of causal inference methods has grown as we look to gather robust evidence for novel medical and public health interventions. This has led to the development of methods that use GWAS summary statistics for causal inference. Here, we describe these methods in order of their escalating complexity, from genetic associations to extensions of Mendelian randomization that consider thousands of phenotypes simultaneously. We also cover the assumptions and limitations of these approaches before considering the challenges faced by researchers performing causal inference using GWAS data. GWAS summary statistics constitute an important data source for causal inference research that offers a counterpoint to nongenetic methods when triangulating evidence. Continued efforts to address the challenges in using GWAS data for causal inference will allow the full impact of these approaches to be realized.

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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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