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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…

Methodology · Statistics 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…

Methodology · Statistics 2025-08-19 Yinxiang Wu , Hyunseung Kang , Ting Ye

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) 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,…

Methodology · Statistics 2025-11-04 Minhao Yao , Anqi Wang , Xihao Li , Zhonghua Liu

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.…

Methodology · Statistics 2023-04-26 Ashish Patel , Francis J. DiTraglia , Verena Zuber , Stephen Burgess

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…

Methodology · Statistics 2023-12-19 Wenhao Cao , Saonli Basu

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…

Applications · Statistics 2018-11-20 Qingyuan Zhao , Yang Chen , Jingshu Wang , Dylan S. Small

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

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…

Methodology · Statistics 2024-02-20 Ashish Patel , Dipender Gill , Paul J. Newcombe , Stephen Burgess

The method of multivariable Mendelian randomization uses genetic variants to instrument multiple exposures, to estimate the effect that a given exposure has on an outcome conditional on all other exposures included in a linear model.…

Methodology · Statistics 2024-08-20 Ashish Patel , James Lane , Stephen Burgess

Mendelian randomization is a powerful tool for inferring the presence, or otherwise, of causal effects from observational data. However, the nature of genetic variants is such that pleiotropy remains a barrier to valid causal effect…

Methodology · Statistics 2021-08-04 Andrew J. Grant , Stephen Burgess

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…

Quantitative Methods · Quantitative Biology 2022-09-16 Apostolos Gkatzionis , Stephen Burgess , Paul J. Newcombe

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 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…

Methodology · Statistics 2024-03-11 Haodong Tian , Ashish Patel , Stephen Burgess

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…

Methodology · Statistics 2018-08-31 Stephen Burgess , Jack Bowden , Frank Dudbridge , Simon G Thompson

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

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…

Methodology · Statistics 2020-12-21 Ioan Gabriel Bucur , Tom Claassen , Tom Heskes

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…

Methodology · Statistics 2021-11-17 Anqi Wang , Zhonghua Liu

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…

Methodology · Statistics 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,…

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