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Mendelian randomization uses genetic variants to make causal inferences about the effect of a risk factor on an outcome. With fine-mapped genetic data, there may be hundreds of genetic variants in a single gene region any of which could be…

Methodology · Statistics 2017-07-10 Stephen Burgess , Verena Zuber , Elsa Valdes-Marquez , Benjamin B Sun , Jemma C Hopewell

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

Instrumental variable methods have been widely used to identify causal effects in the presence of unmeasured confounding. A key identification condition known as the exclusion restriction states that the instrument cannot have a direct…

Methodology · Statistics 2022-08-05 Baoluo Sun , Yifan Cui , Eric Tchetgen Tchetgen

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

The use of instrumental variables for estimating the effect of an exposure on an outcome is popular in econometrics, and increasingly so in epidemiology. This increasing popularity may be attributed to the natural occurrence of instrumental…

Methodology · Statistics 2016-08-03 T. Martinussen , S. Vansteelandt , E. J. Tchetgen Tchetgen , D. M. Zucker

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…

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

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…

Methodology · Statistics 2019-04-30 Jia Zhao , Jingsi Ming , Xianghong Hu , Gang Chen , Jin Liu , Can Yang

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) 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

Omics biomarkers play a pivotal role in personalized medicine by providing molecular-level insights into the etiology of diseases, guiding precise diagnostics, and facilitating targeted therapeutic interventions. Recent advancements in…

Applications · Statistics 2024-02-21 Minhao Yao , Zhonghua Liu

Instrumental variables have been widely used to estimate the causal effect of a treatment on an outcome. Existing confidence intervals for causal effects based on instrumental variables assume that all of the putative instrumental variables…

Methodology · Statistics 2020-06-03 Hyunseung Kang , Youjin Lee , T. Tony Cai , Dylan S. Small

Standard Mendelian randomization analysis can produce biased results if the genetic variant defining the instrumental variable (IV) is confounded and/or has a horizontal pleiotropic effect on the outcome of interest not mediated by the…

Methodology · Statistics 2021-03-31 Zhonghua Liu , Ting Ye , Baoluo Sun , Mary Schooling , Eric Tchetgen Tchetgen

Mendelian randomization (MR) is an epidemiological method that can be used to strengthen causal inference regarding the relationship between a modifiable environmental exposure and a medically relevant trait and to estimate the magnitude of…

Quantitative Methods · Quantitative Biology 2023-08-30 David M Evans , George Davey Smith , Gunn-Helen Moen

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…

Methodology · Statistics 2023-02-22 Xinwei Ma , Jingshen Wang , Chong Wu

Methods utilizing instrumental variables have been a fundamental statistical approach to estimation in the presence of unmeasured confounding, usually occurring in non-randomized observational data common to fields such as economics and…

Methodology · Statistics 2022-10-06 Charles Spanbauer , Wei Pan

Instrumental variables (IVs) are widely used to estimate causal effects in the presence of unobserved confounding between exposure and outcome. An IV must affect the outcome exclusively through the exposure and be unconfounded with the…

Mendelian randomization (MR) is a natural experimental design based on the random transmission of genes from parents to offspring. However, this inferential basis is typically only implicit or used as an informal justification. As…

Methodology · Statistics 2023-04-19 Matthew J Tudball , George Davey Smith , Qingyuan Zhao

In observational studies, instrumental variables estimation is greatly utilized to identify causal effects. One of the key conditions for the instrumental variables estimator to be consistent is the exclusion restriction, which indicates…

Methodology · Statistics 2020-06-16 Gyuhyeong Goh , Jisang Yu

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