TwoSampleMR - Two Sample MR Functions and Interface to MR Base Database

A package for performing Mendelian randomization using GWAS summary data. It uses the IEU GWAS database <https://gwas.mrcieu.ac.uk/> to automatically obtain data, and a wide range of methods to run the analysis. You can use the MR-Base web app <https://www.mrbase.org/> to try out a limited range of the functionality in this package, but for any serious work we strongly recommend using this R package.

causl - Methods for Specifying, Simulating from and Fitting Causal Models

Model multivariate distributions using causal parameters.

geni.plots - GENI plots

GENI plots is designed to visualise results from genome-wide association studies.

ieugwasr - Interface to the 'OpenGWAS' Database API

Interface to the 'OpenGWAS' database API <https://gwas-api.mrcieu.ac.uk/>. Includes a wrapper to make generic calls to the API, plus convenience functions for specific queries.

cit - Causal Inference Test

A likelihood-based hypothesis testing approach is implemented for assessing causal mediation. Described in Millstein, Chen, and Breton (2016), <DOI:10.1093/bioinformatics/btw135>, it could be used to test for mediation of a known causal association between a DNA variant, the 'instrumental variable', and a clinical outcome or phenotype by gene expression or DNA methylation, the potential mediator. Another example would be testing mediation of the effect of a drug on a clinical outcome by the molecular target. The hypothesis test generates a p-value or permutation-based FDR value with confidence intervals to quantify uncertainty in the causal inference. The outcome can be represented by either a continuous or binary variable, the potential mediator is continuous, and the instrumental variable can be continuous or binary and is not limited to a single variable but may be a design matrix representing multiple variables.

OneSampleMR - One Sample Mendelian Randomization and Instrumental Variable Analyses

Useful functions for one-sample (individual level data) Mendelian randomization and instrumental variable analyses. The package includes implementations of; the Sanderson and Windmeijer (2016) <doi:10.1016/j.jeconom.2015.06.004> conditional F-statistic, the multiplicative structural mean model Hernán and Robins (2006) <doi:10.1097/01.ede.0000222409.00878.37>, and two-stage predictor substitution and two-stage residual inclusion estimators explained by Terza et al. (2008) <doi:10.1016/j.jhealeco.2007.09.009>.

MrDAG - MrDAG: Mendelian randomization (MR) with Bayesian Directed Acyclic (DAG) Graphs exploration and causal effects estimation

This package performs Mendelian randomization for multiple exposures and outcomes with Bayesian Directed Acyclic Graphs exploration and causal effects estimation.

MRAPSS - The MRAPSS package implement the MR-APPSS approach to test for the causal effect of an exposure on a outcome disease.

The MRAPSS package implement the MR-APPSS approach to test for the causal effects between an exposure and a outcome disease. The MR-APPSS is a unified approach to Mendelian Randomization accounting for polygenicity, pleiotropy and sample structure using genome-wide summary statistics. Specifically, MR-APPSS uses a background-foreground model to characterize both SNP-exposure effects and SNP-outcome effects estimates, where the background model accounts for confounding from genetic correlation and sample structure and the foreground model captures the valid signal for causal inference.

genepi.utils - GenEpi Utility Functions

The genepi.utils package is a collection of utility functions for working with genetic epidemiology data.

GenomicSEM - Structural equation modeling based on GWAS summary statistics

Later

bpbounds - Nonparametric Bounds for the Average Causal Effect Due to Balke and Pearl and Extensions

Implementation of the nonparametric bounds for the average causal effect under an instrumental variable model by Balke and Pearl (Bounds on Treatment Effects from Studies with Imperfect Compliance, JASA, 1997, 92, 439, 1171-1176, <doi:10.2307/2965583>). The package can calculate bounds for a binary outcome, a binary treatment/phenotype, and an instrument with either 2 or 3 categories. The package implements bounds for situations where these 3 variables are measured in the same dataset (trivariate data) or where the outcome and instrument are measured in one study and the treatment/phenotype and instrument are measured in another study (bivariate data).

BWMR - Bayesian Weighted Mendelian Randomization (BWMR)

Inference the causality based on BWMR method.

SUMnlmr - Non-Linear Mendelian Randomization On Partially Summarized Data

Runs non-linear MR calculations on partly-summarized data.

TVMR - Time-varying Mendelian randomization with time-continous modelling for the effect function

Use functional principal components analysis (FPCA) within multivariable Mendelian randomization (MVMR) to estimate the time-varying effect function.

RFQT - Random Forest of Q Trees

Data-adaptive method for effect heterogeneity analysis or non-linear causal studies in Mendelian randomization and instrumental variables analysis.

DRMR - Doubly-Ranked Stratification in Mendelian Randomization

Doubly-ranked and residual stratification for instrumental variable and Mendelian randomization studies and further stratification-based analysis.

MRBEE - Mendelian Randomization using Bias-Corrected Estimating Equation and Iterative Pleiotropy Testing

This package performs multivariate Mendelian randomization. It is characterized by the removal of measurement error bias caused by the estimation error of GWAS effect size estimates using an unbiased estimating function in measurement error analysis. It also utilizes a pleiotropy test to dynamically detect and remove potential pleiotropy, making the causal effect robust to pleiotropy.

gsmr2 - gsmr2 - a tool for causal inference between complex traits

GSMR2 (Generalised Summary-data-based Mendelian Randomisation v2) is an improved version of GSMR, which uses GWAS summary statistics to test for a putative causal association between two phenotypes (e.g., a modifiable risk factor and a disease) based on a multi-SNP model. This version implements a global heterogeneity test to remove invalid instrumental variables and provides a causal estimation that is more robust to directional pleiotropy.

MRZero - Diet Mendelian Randomization

Encodes several methods for performing Mendelian randomization analyses with summarized data. Similar to the 'MendelianRandomization' package, but with fewer bells and whistles, and less frequent updates. As described in Yavorska (2017) <doi:10.1093/ije/dyx034> and Broadbent (2020) <doi:10.12688/wellcomeopenres.16374.2>.

MendelianRandomization - Mendelian Randomization Package

Encodes several methods for performing Mendelian randomization analyses with summarized data. Summarized data on genetic associations with the exposure and with the outcome can be obtained from large consortia. These data can be used for obtaining causal estimates using instrumental variable methods.

hyprcoloc -

GWASBrewer - Simulate Realistic GWAS Summary Statistics

Simulate GWAS summary statistics from specified DAG or factor structure.

MVMRcML - Multivariable Mendelian randomization based on constrained maximum likelihood

Robust multivariable Mendelian randomization based on constrained maximum likelihood.

CAMeRa - CAMeRa (Cross Ancestral Mendelian Randomisation)

CAMERA estimates joint causal effect in multiple ancestries and detects pleiotropy via the zero relevance model.

GRAPPLE - R Package for MR Framework GRAPPLE

Fitting and diagnosing two-sample summary data Mendelian randomization with heterogeneous instruments.

prop.coloc - A frequentist test of proportional colocalization after selecting relevant genetic variants

A proportional colocalization test that accounts for uncertainty in variant selection using summary data.

lhcMR - Latent Heritable Confounder - Mendelian Randomisation

lhcMR esimates a causal effect between two traits while accounting for a possible latent heritable confounder acting on them, as well as sample overlap.

alspac - Data dictionary for ALSPAC

Functions to search and extract variables based on keywords from the ALSPAC data dictionary.

simmrd - Simulation of Mendelian Randomization data

This package generates simulation data to use in the evaluation of univariable or multivariable Mendelian Randomization methods. MR scenarios can include uncorrelated horizontal pleiotropy, correlated horizontal pleiotropy, weak instruments, winner's curse, and correlated SNP instruments.

iGasso - Statistical Tests and Utilities for Genetic Association

A collection of statistical tests for genetic association studies and summary data based Mendelian randomization.

winnerscurse - Winner's Curse Adjustment Methods for Summary Statistics from Genome-Wide Association Studies

Designed to provide users with easy access to published methods which aim to correct for Winner's Curse using only summary statistics from genome-wide association studies. With merely estimates of effect size and associated standard error for each genetic variant, users are able to implement these methods to obtain more accurate estimates of the true effect sizes. These methods can be applied to data from both quantitative and binary traits.

metaboprep - Metabolomics data preparation and processing pipeline

Reads in raw Metabolon and Nightingale xls sheets and aids in data preparation of all metabolomics data sets.

MRPATH - R Package for MR-PATH

This package implements methods for fitting the MR-PATH model.

MrMediation - An R Package for Mendelian Randomization in the mediation setting

A Bayesian framework for Mendelian Randomization in the mediation setting.

mrcovreg - Package for an efficient and robust approach to Mendelian randomization with measured pleiotropic effects

Implement a method for an efficient and robust approach to Mendelian randomization with measured pleiotropic effects in a high-dimensional setting.

MR.RGM - Multivariate Bidirectional Mendelian Randomization Networks

Addressing a central challenge encountered in Mendelian randomization (MR) studies, where MR primarily focuses on discerning the effects of individual exposures on specific outcomes and establishes causal links between them. Using a network-based methodology, the intricacy involving interdependent outcomes due to numerous factors has been tackled through this routine. Based on Ni et al. (2018) <doi:10.1214/17-BA1087>, 'MR.RGM' extends to a broader exploration of the causal landscape by leveraging on network structures and involves the construction of causal graphs that capture interactions between response variables and consequently between responses and instrument variables. 'MR.RGM' facilitates the navigation of various data availability scenarios effectively by accommodating three input formats, i.e., individual-level data and two types of summary-level data. In the process, causal effects, adjacency matrices, and other essential parameters of the complex biological networks, are estimated. Besides, 'MR.RGM' provides uncertainty quantification for specific network structures among response variables.

midoc - A Decision-Making System For Multiple Imputation

midoc will guide researchers through their analyses, examining the structure of the dataset to advise on whether multiple imputation is needed, and if so how to perform it. Missing data is a common issue in health and social research, often addressed by multiple imputation (MI). MI is a flexible and general approach, with a suite of software packages. However, using MI in practice can be complex. Application of MI involves multiple decisions which are rarely justified or even documented, and for which little guidance is available. midoc is a decision-making system which incorporates expert, up-to-date guidance to help researchers choose the most appropriate analysis method when there are missing data.

finemapr - R wrapper to fine-mappers

R wrapper to fine-mappers.

MRMiSTERI - Mixed-Scale Treatment Effect Robust Identification (MR MiSTERI) and Estimation

This package performs robust Mendelian randomization to estimate the effect of treatment on the treated with possibly invalid IVs.

mr.simss - MR-SimSS: Mendelian Randomisation (MR) method that combats Winner's Curse using a simulated sample splitting approach

Designed to provide users with a method, namely MR-SimSS, which uses simulated sample splitting in order to alleviate Winner's Curse bias in MR causal effect estimates. This approach also takes into account sample overlap between the exposure and outcome genome-wide association studies. It uses summary statistics from genome-wide association studies and works in combination with existing MR methods, such as IVW and MR-RAPS.

jlst - Joint location-and-scale tests

Joint location-and-scale tests for joint testing of mean (location) and variance (scale).

lmrse - Linear regression with clustered robust standard errors

Longtiudinal analysis of high-dimensional data using linear regression with clustered robust standard errors across markers.

nlmr - Non-linear Mendelian randomisation

Non-linear Mendelian randomization analysis to investigate the shape of exposure-outcome relationships.

gwasglue2 - GWAS summary data sources connected to analytical tools

Description: Many tools exist that use GWAS summary data for colocalisation, fine mapping, Mendelian randomization, visualisation, etc. This package is a conduit that connects R packages that can retrieve GWAS summary data to various tools for analysing those data.

MR.LDP - What the Package Does Using Title Case

More details about what the package does. See <http://cran.r-project.org/doc/manuals/r-release/R-exts.html#The-DESCRIPTION-file> for details on how to write this part.

MR.CUE - What the package does (short line)

More about what it does (maybe more than one line)

EpiViz - An implementation of Circos plots for epidemiologists

An implementation of Circos plots for epidemiologists in R. It takes the Circlize package and adapts it for use by epidemiologists. Circos plots provide an informative way of plotting greater than 50 plotting points. A legend can be plotted automatically and customised.

gsmr - gsmr - a tool for SMR and HEIDI analysis

A tool perform Generalized Summary-data-based Mendelian Randomization analysis (GSMR) and HEterogeneity In Dependent Instruments analysis to remove pleiotropic outliers (HEIDI-outlier)

genetics.binaRies - Binaries for Tools Used in Genetics in an R Package

A convenient way to make binaries such as plink, bcftools, and others available to other packages. This package forms part of the MRC IEU OpenGWAS system. The system includes other R packages, including; gwasglue, ieugwasr, gwasvcf, and TwoSampleMR.

gwasvcf - Tools for Dealing with GWAS Summary Data in VCF Format

Tools for dealing with GWAS summary data in VCF format. Includes reading, querying, writing, as well as helper functions such as LD proxy searches.

mrlocus - MRLocus - Mendelian Randomization per locus

Mendelian Randomization per locus, leveraging eQTL and GWAS summary statistics, for estimation of gene-to-trait effect size and dispersion.

MRPRESSO - Performs the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test.

MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier) is a framework that allows for the evaluation of pleiotropy in multi-instrument Mendelian Randomization utilizing genome-wide summary association statistics.

tmsens - Sensitivity Analysis Using the Trimmed Means Estimator

Sensitivity analysis using the trimmed means estimator.

RMVMR - RMVMR

An R package for performing radial multivariable Mendelian randomization analyses.

MVMR - MVMR

An R package for performing multivariable Mendelian randomization analyses.

RadialMR - RadialMR

A package for implementing radial inverse variance weighted and MR-Egger methods.

CheckSumStats - CheckSumStats

CheckSumStats is an R package for checking the accuracy of meta- and summary-data from genome-wide association studies (GWAS) prior to their use in post-GWAS applications. For example, the package provides tools for checking that the reported effect allele and effect allele frequency columns are correct. It also checks for possible issues in the reported effect sizes that might introduce bias into downstream analyses.

GwasDataImport - Import EBI Data to OpenGWAS

Determine the new EBI data not present in OpenGWAS. Download dataset and import metadata. Upload processed data and metadata to OpenGWAS.

ivmodel - Statistical Inference and Sensitivity Analysis for Instrumental Variables Model

Carries out instrumental variable estimation of causal effects, including power analysis, sensitivity analysis, and diagnostics. See Kang, Jiang, Zhao, and Small (2021) <https://muse.jhu.edu/article/804372> for details.

journalclub - Journal club papers

Uses citations and PubMed queries to generate new publications for a journal club.

MRAID - MRAID(Mendelian Randomization with Automated Instrument Determination)

MRAID is an R package for efficient statistical inference of two-sample Mendelian Randomization. MRAID takes GWAS summary statistics as inputs to estimate causal effects of one trait on another. MRAID is able to model an initial set of candidate SNP instruments that are in high LD with each other and perform automated instrument selection to identify suitable SNPs to serve as instrumental variables. MRAID simultaneously accounts for both uncorrelated and correlated horizontal pleiotropy, relies on a scalable sampling-based inference algorithm to perform numerical integration, circumventing the difficulty in likelihood function, leading to calibrated p-values that enable reasonably large-scale exposure screening.

MR2 - Bayesian multi-response Mendelian randomisation

This package performs two-sample summary-level data Bayesian multi-response Mendelian randomisation with the selection of important exposures shared or distinct across responses. Post-processing and selection of exposures based on an in-house FDR implementation are also provided.

navmix - A package for performing directional clustering by fitting a noise-augmented von Mises-Fisher mixture model

A package for performing directional clustering by fitting a noise-augmented von Mises-Fisher mixture model

BESIDEMR - BayEsian Set IDEntification Mendelian randomization

The R package to perform BayEsian Set IDEntification Mendelian randomization (BESIDE-MR).

SlopeHunter - Slope-Hunter: A ROBUST METHOD FOR COLLIDER BIAS CORRECTION IN CONDITIONAL GENOME-WIDE ASSOCIATION STUDIES

Studying genetic associations with prognosis (e.g. survival, disability, subsequent disease events) is problematic due to selection bias - also termed index event bias or collider bias - whereby selection on disease status can induce associations between causes of incidence with prognosis. The Slope-Hunter approach adjusts genetic associations for this bias assuming that the contribution of the set of genetic variants affecting incidence only to the heritability of incidence is at least as large as the contribution of those affecting both incidence and prognosis.

varGWASR - Least Absolute Deviation Regression Brown Forsythe Test

Brown-Forsythe SNP test using LAD regression and variance effect estimate

epigraphdb - Interface Package for the 'EpiGraphDB' Platform

The interface package to access data from the 'EpiGraphDB' <https://epigraphdb.org> platform. It provides easy access to the 'EpiGraphDB' platform with functions that query the corresponding REST endpoints on the API <https://api.epigraphdb.org> and return the response data in the 'tibble' data frame format.

MRPC - PC Algorithm with the Principle of Mendelian Randomization

A PC Algorithm with the Principle of Mendelian Randomization. This package implements the MRPC (PC with the principle of Mendelian randomization) algorithm to infer causal graphs. It also contains functions to simulate data under a certain topology, to visualize a graph in different ways, and to compare graphs and quantify the differences. See Badsha and Fu (2019) <doi:10.3389/fgene.2019.00460>,Badsha, Martin and Fu (2021) <doi:10.3389/fgene.2021.651812>.

gwasglue - GWAS summary data sources connected to analytical tools

Many tools exist that use GWAS summary data for colocalisation, fine mapping, Mendelian randomization, visualisation, etc. This package is a conduit that connects R packages that can retrieve GWAS summary data to various tools for analysing those data.

cause - CAUSE: Causal Analysis Using Summary Effect Estimates

Identify causal patterns using pairs of GWAS summary statistics.

mrsensemakr - Sensitivity Analysis Tools for Mendelian Randomization

The R package mrsensemakr implements sensitivity analysis tools for Mendelian Randomization.

mrclust - Identifying Clustered Heterogeneity in Mendelian Randomization Analyses

Performs likelihood based clustering on univariate observations with known uncertainty (via standard error data), whilst accounting for possible null and junk components in the sample.

MR.Corr2 - What the Package Does Using Title Case

More details about what the package does. See <http://cran.r-project.org/doc/manuals/r-release/R-exts.html#The-DESCRIPTION-file> for details on how to write this part.

mrbayes - Bayesian Summary Data Models for Mendelian Randomization Studies

Bayesian estimation of inverse variance weighted (IVW), Burgess et al. (2013) <doi:10.1002/gepi.21758>, and MR-Egger, Bowden et al. (2015) <doi:10.1093/ije/dyv080>, summary data models for Mendelian randomization analyses.

MRchecks - Tools for detecting genetic variants whose effect changes with age and associated bias

This package is a collection of tools to detect SNPs whose effect varies with age whose. owoeosds sd sd. sdsfw. sdfsers s es .

MRcML - Mendelian randomization method with constrained maximum likelihood.

Perform MRcML method.

indexevent - Adjustment for index event bias

Adjusts association statistics for index event bias in the context of a genome-wide association study for a subsequent event.

tryx - MR-TRYX (treasure your exceptions)

Heterogeneity in MR analyses can arise due to horizontal pleiotropy. This package uses MR-Base to identify possible traits that can explain the heterogeneity, with a view to identifying novel putative associations, and adjusting for their influences to reduce heterogeneity and improve power.

CIVMR - Constrained Instrumental Variables in Mendelian Randomization with Pleiotropy

In Mendelian randomization (MR), genetic variants are used to construct instrumental variables that then enable inference about the causal relationship between a phenotype of interest and a response or disease outcome. However, valid MR inference requires several assumptions, including the assumption that the genetic variants only influence the response through the phenotype of interest.Pleiotropy occurs when a genetic variant has an effect on more than one different phenotypes, and therefore a pleiotropic genetic variant may be an invalid instrumental variable.Hence, a naive method for constructing an instrumental variables may lead to biased estimation of the association between the phenotype and the response. Here, we encode a new and intuitive method (Constrained Instrumental Variable method [CIV]) to construct valid instrumental variables and perform adjusted causal effect estimation when pleiotropic exists, focusing particularly on the situation where pleiotropic phenotypes have been measured. Our approach is theoretically guaranteed to perform an automatic and valid selection of genetic variants when building the instrumental variable. We also provide details of the features of many existing methods, together with a comparison of their performance in a large series of simulations. CIV performs robustly across many different pleiotropic violations of the MR assumptions.

MRInstruments - Data sources for genetic instruments to be used in MR

Datasets of eQTLs, GWAS catalogs, etc.

MRTool - Simulating Data for Mendelian Randomization Scenarios Based on Genotype Data

This package can be used to simulate data in R for Mendelian Randomization scenarios including confounder, exposure and outcome. Recommended MR package is MendelianRandomization.

MRMix - Mendelian Randomization Analysis Using Mixture Models (MRMix)

This package gives robust estimation of causal effects by conducting Mendelian randomization analysis using a mixture model approach.

sisVIVE - Some Invalid Some Valid Instrumental Variables Estimator

Selects invalid instruments amongst a candidate of potentially bad instruments. The algorithm selects potentially invalid instruments and provides an estimate of the causal effect between exposure and outcome.