Instrumental variable model average with applications in Mendelian randomization

Seng, Loraine; Liu, Ching‐Ti; Wang, Jingli; Li, Jialiang

doi:10.1002/sim.9819

Cited by 6 publications

(1 citation statement)

References 82 publications

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“…11 Mendelian randomization (MR) is a powerful methodology that effectively utilizes genetic variation as a robust instrument to meticulously unravel and ascertain the intricate web of causal associations between exposure factors and outcomes. 12 This approach mitigates the influence of confounding variables and furnishes robust evidence for causality. 13 The aims of this study were to explore the causal relationship between GM and IBD through MR and multivariate Mendelian randomization (MVMR) analysis and to find the GM taxa and microbiota-associated pathways that can potentially increase or reduce the risk of IBD, so as to provide a foundation for the prevention and treatment of IBD in the future.…”

Section: Introductionmentioning

confidence: 99%

Causality between Gut Microbiota and Inflammatory Bowel Disease: A Bidirectional Mendelian Randomization Study

Zheng,

Li,

et al. 2024

Infect Microb Dis

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The exact relationship between inflammatory bowel disease (IBD) and gut microbiota (GM) is still unclear. This study aimed to explore the cause-and-effect relationship between IBD and GM by Mendelian randomization (MR) analysis. The IBD data used in this study were obtained from Genome-Wide Association Studies (GWAS). The GM data were from the Dutch Microbiome Project and included 207 taxa and 205 microbiota-associated pathways. Multivariate Mendelian randomization (MVMR) analysis was performed to investigate the relationship between GM and IBD. The results demonstrated that susceptibility to developing IBD is negatively correlated with class Clostridia (OR = 0.80, P = 0.003), family Sutterellacea (OR = 0.87, P = 0.014), genus Coprobacter (OR = 0.90, P = 0.009), order Clostridiales (OR = 0.80, P = 0.003), phylum Firmicutes (OR = 0.82, P = 0.002) and Coprobacter fastidiosus (OR = 0.90, P = 0.009). Conversely, the risk of developing IBD is positively correlated with the phosphopantothenate biosynthesis I pathway (OR = 1.17, P = 0.006), Bacteroides caccae (OR = 1.14, P = 0.021) and Bacteroides uniformis (OR = 1.26, P = 0.003). Reverse causality was found between the phosphopantothenate biosynthesis I pathway and IBD (OR = 1.03, P = 0.04), but not for the remaining specific GM taxa. In conclusion, our study demonstrated a causal relationship between specific GM features and IBD.

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Section: Introductionmentioning

confidence: 99%

Causality between Gut Microbiota and Inflammatory Bowel Disease: A Bidirectional Mendelian Randomization Study

Zheng,

Li,

et al. 2024

Infect Microb Dis

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Estimating Time‐Varying Exposure Effects Through Continuous‐Time Modelling in Mendelian Randomization

Tian,

Patel,

Burgess

2024

Statistics in Medicine

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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 time‐varying causal effect of the exposure can yield detailed insights into mechanistic effects and the potential impact of public health interventions. Recently, a growing number of Mendelian randomization studies have attempted to explore time‐varying causal effects. However, the proposed approaches oversimplify temporal information and rely on overly restrictive structural assumptions, limiting their reliability in addressing time‐varying causal problems. This article considers a novel approach to estimate time‐varying effects through continuous‐time modelling by combining functional principal component analysis and weak‐instrument‐robust techniques. Our method effectively utilizes available data without making strong structural assumptions and can be applied in general settings where the exposure measurements occur at different timepoints for different individuals. We demonstrate through simulations that our proposed method performs well in estimating time‐varying effects and provides reliable inference when the time‐varying effect form is correctly specified. The method could theoretically be used to estimate arbitrarily complex time‐varying effects. However, there is a trade‐off between model complexity and instrument strength. Estimating complex time‐varying effects requires instruments that are unrealistically strong. We illustrate the application of this method in a case study examining the time‐varying effects of systolic blood pressure on urea levels.

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Instrumental Variable Model Average With Applications in Nonlinear Causal Inference

Chen,

Wang,

Shi

et al. 2024

Statistics in Medicine

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The instrumental variable method is widely used in causal inference research to improve the accuracy of estimating causal effects. However, the weak correlation between instruments and exposure, as well as the direct impact of instruments on the outcome, can lead to biased estimates. To mitigate the bias introduced by such instruments in nonlinear causal inference, we propose a two‐stage nonlinear causal effect estimation based on model averaging. The model uses different subsets of instruments in the first stage to predict exposure after a nonlinear transformation with the help of sliced inverse regression. In the second stage, adaptive Lasso penalty is applied to instruments to obtain the estimation of causal effect. We prove that the proposed estimator exhibits favorable asymptotic properties and evaluate its performance through a series of numerical studies, demonstrating its effectiveness in identifying nonlinear causal effects and its capability to handle scenarios with weak and invalid instruments. We apply the proposed method to the Atherosclerosis Risk in Communities dataset to investigate the relationship between BMI and hypertension.

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Instrumental variable model average with applications in Mendelian randomization

Cited by 6 publications

References 82 publications

Causality between Gut Microbiota and Inflammatory Bowel Disease: A Bidirectional Mendelian Randomization Study

Causality between Gut Microbiota and Inflammatory Bowel Disease: A Bidirectional Mendelian Randomization Study

Estimating Time‐Varying Exposure Effects Through Continuous‐Time Modelling in Mendelian Randomization

Instrumental Variable Model Average With Applications in Nonlinear Causal Inference

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