孟德尔随机化
混淆
观察研究
结果(博弈论)
因果推理
遗传关联
全基因组关联研究
代理(统计)
统计
单核苷酸多态性
计算机科学
生物
遗传学
遗传变异
机器学习
基因型
数学
基因
数理经济学
作者
Danielle Rasooly,Chirag Patel
摘要
Abstract Mendelian randomization (MR) is defined as the utilization of genetic variants as instrumental variables to assess the causal relationship between an exposure and an outcome. By leveraging genetic polymorphisms as proxy for an exposure, the causal effect of an exposure on an outcome can be assessed while addressing susceptibility to biases prone to conventional observational studies, including confounding and reverse causation, where the outcome causes the exposure. Analogous to a randomized controlled trial where patients are randomly assigned to subgroups based on different treatments, in an MR analysis, the random allocation of alleles during meiosis from parent to offspring assigns individuals to different subgroups based on genetic variants. Recent methods use summary statistics from genome‐wide association studies to perform MR, bypassing the need for individual‐level data. Here, we provide a straightforward protocol for using summary‐level data to perform MR and provide guidance for utilizing available software. © 2019 by John Wiley & Sons, Inc.
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