Boehm Frederick J, Zhou Xiang
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
Comput Struct Biotechnol J. 2022 May 14;20:2338-2351. doi: 10.1016/j.csbj.2022.05.015. eCollection 2022.
Genome-wide association studies have yielded thousands of associations for many common diseases and disease-related complex traits. The identified associations made it possible to identify the causal risk factors underlying diseases and investigate the causal relationships among complex traits through Mendelian randomization. Mendelian randomization is a form of instrumental variable analysis that uses SNP associations from genome-wide association studies as instruments to study and uncover causal relationships between complex traits. By leveraging SNP genotypes as instrumental variables, or proxies, for the exposure complex trait, investigators can tease out causal effects from observational data, provided that necessary assumptions are satisfied. We discuss below the development of Mendelian randomization methods in parallel with the growth of genome-wide association studies. We argue that the recent availability of GWAS summary statistics for diverse complex traits has motivated new Mendelian randomization methods with relaxed causality assumptions and that this area continues to offer opportunities for robust biological discoveries.
全基因组关联研究已经为许多常见疾病和与疾病相关的复杂性状产生了数千个关联。所确定的关联使得识别疾病背后的因果风险因素以及通过孟德尔随机化研究复杂性状之间的因果关系成为可能。孟德尔随机化是一种工具变量分析形式，它使用全基因组关联研究中的单核苷酸多态性（SNP）关联作为工具来研究和揭示复杂性状之间的因果关系。通过将SNP基因型作为暴露复杂性状的工具变量或替代变量，研究人员可以从观察数据中梳理出因果效应，前提是满足必要的假设。我们在下面讨论孟德尔随机化方法的发展与全基因组关联研究的增长情况。我们认为，最近针对各种复杂性状的全基因组关联研究汇总统计数据的可用性推动了具有放宽因果假设的新孟德尔随机化方法的出现，并且这一领域继续为可靠的生物学发现提供机会。
Zotero 插件
