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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) 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…

统计方法学 · 统计学 2026-01-15 Bitan Sarkar , Yuchao Jiang , Tian Ge , Yang Ni

Recent advances in genotyping technology have delivered a wealth of genetic data, which is rapidly advancing our understanding of the underlying genetic architecture of complex diseases. Mendelian Randomization (MR) leverages such genetic…

统计方法学 · 统计学 2023-12-19 Wenhao Cao , Saonli Basu

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

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,…

统计方法学 · 统计学 2025-08-05 Xinpei Wang , Tao Huang , Jinzhu Jia

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…

统计方法学 · 统计学 2025-11-18 Yinxiang Wu , Neil M. Davies , Ting Ye

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

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

Multivariate Mendelian randomization (MVMR) is a statistical technique that uses sets of genetic instruments to estimate the direct causal effects of multiple exposures on an outcome of interest. At genomic loci with pleiotropic gene…

统计方法学 · 统计学 2024-09-23 Mariyam Khan , Adriaan-Alexander Ludl , Sean Bankier , Johan Bjorkegren , Tom Michoel

Multivariable Mendelian randomization (MVMR) uses genetic variants as instrumental variables to infer the direct effects of multiple exposures on an outcome. However, unlike univariable Mendelian randomization, MVMR often faces greater…

统计方法学 · 统计学 2025-08-19 Yinxiang Wu , Hyunseung Kang , Ting Ye

Mendelian randomization (MR) uses genetic variants as instrumental variables to make causal claims. Standard MR approaches typically report a single population-averaged estimate, limiting their ability to explore effect heterogeneity or…

统计方法学 · 统计学 2025-07-16 Stephen Burgess , Benjamin A R Woolf , Amy M Mason

Mendelian randomization (MR) is a widely-used method to estimate the causal relationship between a risk factor and disease. A fundamental part of any MR analysis is to choose appropriate genetic variants as instrumental variables.…

统计方法学 · 统计学 2023-04-26 Ashish Patel , Francis J. DiTraglia , Verena Zuber , Stephen Burgess

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

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…

统计方法学 · 统计学 2021-12-16 Linyi Zou , Hui Guo , Carlo Berzuini

Mendelian Randomization (MR) is a prominent observational epidemiological research method designed to address unobserved confounding when estimating causal effects. However, core assumptions -- particularly the independence between…

机器学习 · 计算机科学 2026-02-24 Shimeng Huang , Matthew Robinson , Francesco Locatello

Mendelian randomization is the use of genetic variants to assess the existence of a causal relationship between a risk factor and an outcome of interest. Here, we focus on two-sample summary-data Mendelian randomization analyses with many…

定量方法 · 定量生物学 2022-09-16 Apostolos Gkatzionis , Stephen Burgess , Paul J. Newcombe

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 (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…

应用统计 · 统计学 2022-06-15 Daniel Iong , Qingyuan Zhao , Yang Chen

Mendelian randomization (MR) is a statistical method exploiting genetic variants as instrumental variables to estimate the causal effect of modifiable risk factors on an outcome of interest. Despite wide uses of various popular two-sample…

统计方法学 · 统计学 2021-11-17 Anqi Wang , Zhonghua 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…

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