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Mendelian randomization (MR) is a pivotal tool in genetics, genomics, and epidemiology, leveraging genetic variants as instrumental variables to infer causal relationships between exposures and outcomes. Traditional MR methods, while…

Methodology · Statistics 2026-01-15 Bitan Sarkar , Yuchao Jiang , Tian Ge , Yang Ni

Selection bias affects Mendelian randomization investigations when selection into the study sample depends on a collider between the genetic variant and confounders of the risk factor-outcome association. However, the relative importance of…

Applications · Statistics 2018-03-13 Apostolos Gkatzionis , Stephen Burgess

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,…

Methodology · Statistics 2023-09-22 Malka Gorfine , Conghui Qu , Ulrike Peters , Li Hsu

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…

Methodology · Statistics 2025-02-21 Maximilian M Mandl , Anne-Laure Boulesteix , Stephen Burgess , Verena Zuber

Mendelian randomization (MR) has been a popular method in genetic epidemiology to estimate the effect of an exposure on an outcome using genetic variants as instrumental variables (IV), with two-sample summary-data MR being the most…

Methodology · Statistics 2021-06-08 Sheng Wang , Hyunseung Kang

Mendelian randomization (MR) is a powerful approach to examine the causal relationships between health risk factors and outcomes from observational studies. Due to the proliferation of genome-wide association studies (GWASs) and abundant…

Methodology · Statistics 2020-09-02 Qing Cheng , Baoluo Sun , Yingcun Xia , Jin Liu

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…

Methodology · Statistics 2019-11-12 Nan Bi , Hyunseung Kang , Jonathan Taylor

Mendelian randomization (MR) is widely used to uncover causal relationships in the presence of unmeasured confounders. However, most existing MR methods presuppose linear causality, risking bias when the true relationships are nonlinear,…

Methodology · Statistics 2025-08-05 Xinpei Wang , Tao Huang , Jinzhu Jia

Multivariable Mendelian randomization estimates the causal effect of multiple exposures on an outcome, typically using summary statistics of genetic variant associations. However, exposures of interest in Mendelian randomization…

Methodology · Statistics 2022-03-17 Jiazheng Zhu , Stephen Burgess , Andrew J. Grant

Background: Mendelian randomization (MR) is a useful approach to causal inference from observational studies when randomised controlled trials are not feasible. However, study heterogeneity of two association studies required in MR is often…

Methodology · Statistics 2021-12-16 Linyi Zou , Hui Guo , Carlo Berzuini

Multivariable Mendelian Randomization (MVMR) estimates the direct causal effects of multiple risk factors on an outcome using genetic variants as instruments. The growing availability of summary-level genetic data has created opportunities…

Methodology · Statistics 2025-11-18 Yinxiang Wu , Neil M. Davies , Ting Ye

Mendelian Randomization (MR) is a popular method in epidemiology and genetics that uses genetic variation as instrumental variables for causal inference. Existing MR methods usually assume most genetic variants are valid instrumental…

Applications · Statistics 2022-06-15 Daniel Iong , Qingyuan Zhao , Yang Chen

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…

Methodology · Statistics 2020-11-04 Linyi Zou , Hui Guo , Carlo Berzuini

Mediation analysis is a powerful tool for studying causal pathways between exposure, mediator, and outcome variables of interest. While classical mediation analysis using observational data often requires strong and sometimes unrealistic…

Methodology · Statistics 2024-05-20 Rita Qiuran Lyu , Chong Wu , Xinwei Ma , Jingshen Wang

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…

Background In a study performed on multiplex Multiple Sclerosis (MS) Sardinian families to identify disease causing plasma proteins, application of Mendelian Randomization (MR) methods encounters difficulties due to relatedness of…

Mendelian randomization (MR) has become a popular approach to study causal effects by using genetic variants as instrumental variables. We propose a new MR method, GENIUS-MAWII, which simultaneously addresses the two salient phenomena that…

Methodology · Statistics 2024-02-27 Ting Ye , Zhonghua Liu , Baoluo Sun , Eric Tchetgen Tchetgen

Many Mendelian randomization (MR) papers have been conducted only in people of European ancestry, limiting transportability of results to the global population. Expanding MR to diverse ancestry groups is essential to ensure equitable…

In the past decade, the increased availability of genome-wide association studies summary data has popularized Mendelian Randomization (MR) for conducting causal inference. MR analyses, incorporating genetic variants as instrumental…

Methodology · Statistics 2025-08-26 Zhongming Xie , Wanheng Zhang , Jingshen Wang , Chong Wu

Methods have been developed for Mendelian randomization that can obtain consistent causal estimates while relaxing the instrumental variable assumptions. These include multivariable Mendelian randomization, in which a genetic variant may be…

Methodology · Statistics 2017-08-02 Jessica M. B. Rees , Angela Wood , Stephen Burgess