scholarly journals Bayesian estimation of IVW and MR-Egger models for two-sample Mendelian randomization studies

2019 ◽  
Author(s):  
Okezie Uche-Ikonne ◽  
Frank Dondelinger ◽  
Tom Palmer
Keyword(s):  
Bayesian Estimation ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Model Parameters ◽  
Software Environment ◽  
Link Type ◽  
Mcmc Estimation ◽  
Level Data ◽  
Future Work ◽  
Model Slope

AbstractWe present our package, mrbayes, for the open source software environment R. The package implements Bayesian estimation of IVW and MR-Egger models, including the radial MR-Egger model, for summary-level data Mendelian randomization analyses. We have implemented a choice of prior distributions for the model parameters, namely; weakly informative, non-informative, a joint prior for the MR-Egger model slope and intercept, and a pseudo-horseshoe prior, or the user can specify their own prior. We show how to use the package through an applied example investigating the causal effect of BMI on insulin resistance. In future work, we plan to provide functions for alternative MCMC estimation software such as Stan and OpenBugs.AvailabilityThe package is freely available, under the MIT license, on GitHub here https://github.com/okezie94/mrbayes.It can be installed in R using the following commands.There is a website of the package helpfiles at https://okezie94.github.io/mrbayes/.

Software Application Profile: Bayesian estimation of inverse variance weighted and MR-Egger models for two-sample Mendelian randomization studies—mrbayes

2020 ◽  
Author(s):  
Okezie Uche-Ikonne ◽  
Frank Dondelinger ◽  
Tom Palmer
Keyword(s):  
Bayesian Estimation ◽  
Prior Distribution ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Model Parameters ◽  
Software Environment ◽  
Prior Distributions ◽  
Software Application ◽  
Level Data ◽  
Inverse Variance

Abstract Motivation We present our package, mrbayes, for the open source software environment R. The package implements Bayesian estimation for inverse variance weighted (IVW) and MR-Egger models, including the radial MR-Egger model, for summary-level data in Mendelian randomization (MR) analyses. Implementation We have implemented a choice of prior distributions for the model parameters, namely; weakly informative, non-informative, a joint prior for the MR-Egger model slope and intercept, and an informative prior (pseudo-horseshoe prior), or the user can specify their own prior distribution. General features Users have the option of fitting the models using either JAGS or Stan software packages with similar prior distributions; the option for the user-defined prior distribution is only in our JAGS functions. We show how to use the package through an applied example investigating the causal effect of body mass index (BMI) on acute ischaemic stroke. Availability The package is freely available, under the GNU General Public License v3.0, on GitHub [https://github.com/okezie94/mrbayes] or CRAN [https://CRAN.R-project.org/package=mrbayes].

scholarly journals Using the MR-Base platform to investigate risk factors and drug targets for thousands of phenotypes

2019 ◽  
Vol 4 ◽  
pp. 113 ◽  
Author(s):  
Venexia M Walker ◽  
Neil M Davies ◽  
Gibran Hemani ◽  
Jie Zheng ◽  
Philip C Haycock ◽  
...  
Keyword(s):  
Risk Factors ◽  
Web Application ◽  
Drug Targets ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
R Package ◽  
Sensitivity Analyses ◽  
Link Type ◽  
Study Results

Mendelian randomization (MR) estimates the causal effect of exposures on outcomes by exploiting genetic variation to address confounding and reverse causation. This method has a broad range of applications, including investigating risk factors and appraising potential targets for intervention. MR-Base has become established as a freely accessible, online platform, which combines a database of complete genome-wide association study results with an interface for performing Mendelian randomization and sensitivity analyses. This allows the user to explore millions of potentially causal associations. MR-Base is available as a web application or as an R package. The technical aspects of the tool have previously been documented in the literature. The present article is complementary to this as it focuses on the applied aspects. Specifically, we describe how MR-Base can be used in several ways, including to perform novel causal analyses, replicate results and enable transparency, amongst others. We also present three use cases, which demonstrate important applications of Mendelian randomization and highlight the benefits of using MR-Base for these types of analyses.

scholarly journals An efficient and robust approach to Mendelian randomization with measured pleiotropic effects in a high-dimensional setting

Biostatistics ◽  
2020 ◽  
Author(s):  
Andrew J Grant ◽  
Stephen Burgess
Keyword(s):  
Risk Factor ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Robust Estimator ◽  
Simulation Studies ◽  
Individual Level ◽  
Large Numbers ◽  
Level Data ◽  
Randomization Analysis

Summary Valid estimation of a causal effect using instrumental variables requires that all of the instruments are independent of the outcome conditional on the risk factor of interest and any confounders. In Mendelian randomization studies with large numbers of genetic variants used as instruments, it is unlikely that this condition will be met. Any given genetic variant could be associated with a large number of traits, all of which represent potential pathways to the outcome which bypass the risk factor of interest. Such pleiotropy can be accounted for using standard multivariable Mendelian randomization with all possible pleiotropic traits included as covariates. However, the estimator obtained in this way will be inefficient if some of the covariates do not truly sit on pleiotropic pathways to the outcome. We present a method that uses regularization to identify which out of a set of potential covariates need to be accounted for in a Mendelian randomization analysis in order to produce an efficient and robust estimator of a causal effect. The method can be used in the case where individual-level data are not available and the analysis must rely on summary-level data only. It can be used where there are any number of potential pleiotropic covariates up to the number of genetic variants less one. We show the results of simulation studies that demonstrate the performance of the proposed regularization method in realistic settings. We also illustrate the method in an applied example which looks at the causal effect of urate plasma concentration on coronary heart disease.

scholarly journals Automating Mendelian randomization through machine learning to construct a putative causal map of the human phenome

2017 ◽  
Author(s):  
Gibran Hemani ◽  
Jack Bowden ◽  
Philip Haycock ◽  
Jie Zheng ◽  
Oliver Davis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Causal Inference ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
False Discovery Rates ◽  
Level Data ◽  
Invalid Instruments ◽  
Major Application

AbstractA major application for genome-wide association studies (GWAS) has been the emerging field of causal inference using Mendelian randomization (MR), where the causal effect between a pair of traits can be estimated using only summary level data. MR depends on SNPs exhibiting vertical pleiotropy, where the SNP influences an outcome phenotype only through an exposure phenotype. Issues arise when this assumption is violated due to SNPs exhibiting horizontal pleiotropy. We demonstrate that across a range of pleiotropy models, instrument selection will be increasingly liable to selecting invalid instruments as GWAS sample sizes continue to grow. Methods have been developed in an attempt to protect MR from different patterns of horizontal pleiotropy, and here we have designed a mixture-of-experts machine learning framework (MR-MoE 1.0) that predicts the most appropriate model to use for any specific causal analysis, improving on both power and false discovery rates. Using the approach, we systematically estimated the causal effects amongst 2407 phenotypes. Almost 90% of causal estimates indicated some level of horizontal pleiotropy. The causal estimates are organised into a publicly available graph database (http://eve.mrbase.org), and we use it here to highlight the numerous challenges that remain in automated causal inference.

scholarly journals An examination of multivariable Mendelian randomization in the single sample and two-sample summary data settings

2018 ◽  
Author(s):  
Eleanor Sanderson ◽  
George Davey Smith ◽  
Frank Windmeijer ◽  
Jack Bowden
Keyword(s):  
Mendelian Randomization ◽  
Causal Effect ◽  
Causal Effects ◽  
Single Sample ◽  
Mendelian Randomisation ◽  
Uk Biobank ◽  
Level Data ◽  
Wide Range ◽  
Using Data ◽  
Summary Data

AbstractBackgroundMendelian Randomisation (MR) is a powerful tool in epidemiology which can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilising genetic variants that are instrumental variables (IVs) for the exposure. This has been extended to Multivariable MR (MVMR) to estimate the effect of two or more exposures on an outcome.Methods/ResultsWe use simulations and theory to clarify the interpretation of estimated effects in a MVMR analysis under a range of underlying scenarios, where a secondary exposure acts variously as a confounder, a mediator, a pleiotropic pathway and a collider. We then describe how instrument strength and validity can be assessed for an MVMR analysis in the single sample setting, and develop tests to assess these assumptions in the popular two-sample summary data setting. We illustrate our methods using data from UK biobank to estimate the effect of education and cognitive ability on body mass index.ConclusionMVMR analysis consistently estimates the effect of an exposure, or exposures, of interest and provides a powerful tool for determining causal effects in a wide range of scenarios with either individual or summary level data.

scholarly journals A Two-Sample Robust Bayesian Mendelian Randomization Method Accounting for Linkage Disequilibrium and Idiosyncratic Pleiotropy with Applications to the COVID-19 Outcome

2021 ◽  
Author(s):  
Anqi Wang ◽  
Zhonghua Liu
Keyword(s):  
Linkage Disequilibrium ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Standard Errors ◽  
Model Parameters ◽  
Ratio Test ◽  
Direct Effects ◽  
Model Framework ◽  
Increased Risk

ABSTRACTMendelian 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 MR methods based on genome-wide association study summary level data, however, those methods could suffer from potential power loss or/and biased inference when the chosen genetic variants are in linkage disequilibrium (LD), and also have relatively large direct effects on the outcome whose distribution might be heavy-tailed which is commonly referred to as the idiosyncratic pleiotropy phenomenon. To resolve those two issues, we propose a novel Robust Bayesian Mendelian Randomization (RBMR) model that uses the more robust multivariate generalized t-distribution (Arellano-Valle and Bolfarine, 1995) to model such direct effects in a probabilistic model framework which can also incorporate the LD structure explicitly. The generalized t-distribution can be represented as a Gaussian scaled mixture so that our model parameters can be estimated by the EM-type algorithms. We compute the standard errors by calibrating the evidence lower bound using the likelihood ratio test. Through extensive simulation studies, we show that our RBMR has robust performance compared to other competing methods. We also apply our RBMR method to two benchmark data sets and find that RBMR has smaller bias and standard errors. Using our proposed RBMR method, we find that coronary artery disease is associated with increased risk of critically ill coronavirus disease 2019 (COVID-19). We also develop a user-friendly R package RBMR (https://github.com/AnqiWang2021/RBMR) for public use.

scholarly journals An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings

2018 ◽  
Vol 48 (3) ◽  
pp. 713-727 ◽  
Author(s):  
Eleanor Sanderson ◽  
George Davey Smith ◽  
Frank Windmeijer ◽  
Jack Bowden
Keyword(s):  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Causal Effects ◽  
Single Sample ◽  
Uk Biobank ◽  
Level Data ◽  
Wide Range ◽  
Using Data ◽  
Summary Data

Abstract Background Mendelian randomization (MR) is a powerful tool in epidemiology that can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilizing genetic variants that are instrumental variables (IVs) for the exposure. This has been extended to multivariable MR (MVMR) to estimate the effect of two or more exposures on an outcome. Methods and results We use simulations and theory to clarify the interpretation of estimated effects in a MVMR analysis under a range of underlying scenarios, where a secondary exposure acts variously as a confounder, a mediator, a pleiotropic pathway and a collider. We then describe how instrument strength and validity can be assessed for an MVMR analysis in the single-sample setting, and develop tests to assess these assumptions in the popular two-sample summary data setting. We illustrate our methods using data from UK Biobank to estimate the effect of education and cognitive ability on body mass index. Conclusion MVMR analysis consistently estimates the direct causal effect of an exposure, or exposures, of interest and provides a powerful tool for determining causal effects in a wide range of scenarios with either individual- or summary-level data.

scholarly journals OP96 The causal effect of BMI on neurodevelopment: a within family mendelian randomization study using MoBa

2020 ◽  
Author(s):  
AM Hughes ◽  
H Ask ◽  
T Tesli ◽  
RB Askeland ◽  
T Reichborn-Kjennerud ◽  
...  
Keyword(s):  
Mendelian Randomization ◽  
Causal Effect

Nearly Exact Bayesian Estimation of Non-linear No-Arbitrage Term-Structure Models*

2021 ◽  
Author(s):  
Marcello Pericoli ◽  
Marco Taboga
Keyword(s):  
Bayesian Estimation ◽  
Term Structure ◽  
Broad Class ◽  
Model Parameters ◽  
State Variables ◽  
Computationally Efficient ◽  
Macroeconomic Factors ◽  
No Arbitrage ◽  
Term Structure Models ◽  
Non Linear

Abstract We propose a general method for the Bayesian estimation of a very broad class of non-linear no-arbitrage term-structure models. The main innovation we introduce is a computationally efficient method, based on deep learning techniques, for approximating no-arbitrage model-implied bond yields to any desired degree of accuracy. Once the pricing function is approximated, the posterior distribution of model parameters and unobservable state variables can be estimated by standard Markov Chain Monte Carlo methods. As an illustrative example, we apply the proposed techniques to the estimation of a shadow-rate model with a time-varying lower bound and unspanned macroeconomic factors.

A Preliminary Report on Alcohol-Associated DNA Methylation Changes and Suicidal Behavior: Evidence Using Mendelian Randomization

2021 ◽  
pp. 105413732095224
Author(s):  
Charleen D. Adams
Keyword(s):  
Public Health ◽  
Dna Methylation ◽  
Suicidal Behavior ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Economic Stress ◽  
Self Report ◽  
Health Concern ◽  
The Us

Suicide is a major public health concern. In 2015, it was the 10th leading cause of death in the US. The number of suicides increased by 30% in the US from 1999 to 2016, and a greater uptick in suicides is predicted to occur as a result of the COVID-19 crisis, for which the primary public-health strategy is physical distancing and during which alcohol sales have soared. Thus, current strategies for identifying at-risk individuals and preventing suicides, such as relying on self-reported suicidal ideation, are insufficient, especially under conditions of physical distancing, which exacerbate isolation, loneliness, economic stress, and possibly alcohol consumption. New strategies are urgent now and into the future. To that aim, here, a two-sample Mendelian randomization (an instrumental variables technique using public genome-wide association study data as data sources) was performed to determine whether alcohol-associated changes in DNA methylation mediate risk for suicidal behavior. The results suggest that higher alcohol-associated DNA methylation levels at cg18120259 confer a weak causal effect. Replication and triangulation of the results, both experimentally and with designs other than Mendelian randomization, are needed. If the findings replicate, the information might be utilized to raise awareness about the biological links between alcohol and suicide and possibly explored as a biomarker of risk, perhaps especially for early detection of those who may not self-report suicidal intent.

Sign in / Sign up

Export Citation Format

Share Document