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Mendelian randomization (MR) is a powerful method that uses genetic variants as instrumental variables (IVs) to infer the causal effect of a modifiable exposure on an outcome. Although recent years have seen many extensions of basic MR…

统计方法学 · 统计学 2022-03-15 Sai Li , Ting Ye

Mendelian randomization (MR) has become a popular approach to study the effect of a modifiable exposure on an outcome by using genetic variants as instrumental variables. A challenge in MR is that each genetic variant explains a relatively…

统计方法学 · 统计学 2020-10-13 Ting Ye , Jun Shao , Hyunseung Kang

Mendelian randomization is the use of genetic variants as instrumental variables to assess whether a risk factor is a cause of a disease outcome. Increasingly, Mendelian randomization investigations are conducted on the basis of summarized…

应用统计 · 统计学 2015-12-15 Stephen Burgess , Jack Bowden

Mendelian randomization is an instrumental variable method that utilizes genetic information to investigate the causal effect of a modifiable exposure on an outcome. In most cases, the exposure changes over time. Understanding the…

统计方法学 · 统计学 2024-03-11 Haodong Tian , Ashish Patel , Stephen Burgess

Mendelian randomization (MR) has become an essential tool for causal inference in biomedical and public health research. By using genetic variants as instrumental variables, MR helps address unmeasured confounding and reverse causation,…

统计方法学 · 统计学 2025-11-04 Minhao Yao , Anqi Wang , Xihao Li , Zhonghua Liu

The results from Genome-Wide Association Studies (GWAS) on thousands of phenotypes provide an unprecedented opportunity to infer the causal effect of one phenotype (exposure) on another (outcome). Mendelian randomization (MR), an…

统计方法学 · 统计学 2019-04-30 Jia Zhao , Jingsi Ming , Xianghong Hu , Gang Chen , Jin Liu , Can Yang

The use of genetic variants as instrumental variables - an approach known as Mendelian randomization - is a popular epidemiological method for estimating the causal effect of an exposure (phenotype, biomarker, risk factor) on a disease or…

统计方法学 · 统计学 2020-12-21 Ioan Gabriel Bucur , Tom Claassen , Tom Heskes

Many diseases and traits involve a complex interplay between genes and environment, generating significant interest in studying gene-environment interaction through observational data. However, for lifestyle and environmental risk factors,…

统计方法学 · 统计学 2023-09-22 Malka Gorfine , Conghui Qu , Ulrike Peters , Li Hsu

Mendelian randomization is the use of genetic variants to make causal inferences from observational data. The field is currently undergoing a revolution fuelled by increasing numbers of genetic variants demonstrated to be associated with…

统计方法学 · 统计学 2018-08-31 Stephen Burgess , Jack Bowden , Frank Dudbridge , Simon G Thompson

Mendelian randomization (MR) considers using genetic variants as instrumental variables (IVs) to infer causal effects in observational studies. However, the validity of causal inference in MR can be compromised when the IVs are potentially…

统计方法学 · 统计学 2024-02-06 Ziya Xu , Sai Li

Two-sample summary-data Mendelian randomization (MR) has become a popular research design to estimate the causal effect of risk exposures. With the sample size of GWAS continuing to increase, it is now possible to utilize genetic…

应用统计 · 统计学 2018-11-20 Qingyuan Zhao , Yang Chen , Jingshu Wang , Dylan S. Small

Mendelian randomization is a widely-used method to estimate the unconfounded effect of an exposure on an outcome by using genetic variants as instrumental variables. Mendelian randomization analyses which use variants from a single genetic…

统计方法学 · 统计学 2024-02-20 Ashish Patel , Dipender Gill , Paul J. Newcombe , Stephen Burgess

In Mendelian randomization (MR) studies, genetic variants are used as instrumental variables (IVs) to investigate causal relationships between exposures and outcomes based on observational data. However, numerous genetic studies have shown…

统计方法学 · 统计学 2026-04-10 Julien St-Pierre , Archer Y. Yang , Mireille E. Schnitzer , Marc-André Legault

Mendelian randomization (MR) is an instrumental variable (IV) approach to infer causal relationships between exposures and outcomes with genome-wide association studies (GWAS) summary data. However, the multivariable inverse-variance…

统计方法学 · 统计学 2024-02-13 Yihe Yang , Noah Lorincz-Comi , Xiaofeng Zhu

Mendelian Randomisation (MR) uses genetic variants as instrumental variables to infer causal effects of exposures on an outcome. One key assumption of MR is that the genetic variants used as instrumental variables are independent of the…

统计方法学 · 统计学 2025-02-21 Maximilian M Mandl , Anne-Laure Boulesteix , Stephen Burgess , Verena Zuber

Developments in genome-wide association studies and the increasing availability of summary genetic association data have made the application of two-sample Mendelian Randomization (MR) with summary data increasingly popular. Conventional…

统计方法学 · 统计学 2023-02-22 Xinwei Ma , Jingshen Wang , Chong Wu

Mendelian randomization (MR) is a popular method in genetic epidemiology to estimate the effect of an exposure on an outcome by using genetic instruments. These instruments are often selected from a combination of prior knowledge from…

统计方法学 · 统计学 2019-11-12 Nan Bi , Hyunseung Kang , Jonathan Taylor

Background: Mendelian randomization (MR) has been widely applied to causal inference in medical research. It uses genetic variants as instrumental variables (IVs) to investigate putative causal relationship between an exposure and an…

统计方法学 · 统计学 2020-11-04 Linyi Zou , Hui Guo , Carlo Berzuini

Mendelian Randomization is a widely used instrumental variable method for assessing causal effects of lifelong exposures on health outcomes. Many exposures, however, have causal effects that vary across the life course and often influence…

Mendelian randomization (MR) is a popular instrumental variable (IV) approach, in which one or several genetic markers serve as IVs that can sometimes be leveraged to recover valid inferences about a given exposure-outcome causal…

统计方法学 · 统计学 2021-08-10 Eric J. Tchetgen Tchetgen , BaoLuo Sun , Stefan Walter
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