GenomicSEM - Structural equation modeling based on GWAS summary statistics

Later

Last updated 1 days ago

7.09 score 210 stars 112 scripts

MVMR - MVMR

An R package for performing multivariable Mendelian randomization analyses.

Last updated 2 months ago

6.49 score 44 stars 1 dependents 155 scripts

RadialMR - RadialMR

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

Last updated 2 months ago

5.91 score 33 stars 3 dependents 110 scripts

CAMeRa - CAMeRa (Cross Ancestral Mendelian Randomisation)

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

Last updated 8 months ago

causal-inferencegwas-summary-statisticsmendelian-randomisationmulti-ancestry

5.30 score 2 stars 165 scripts

hdi - High-Dimensional Inference

Implementation of multiple approaches to perform inference in high-dimensional models.

Last updated 4 years ago

4.50 score 2 stars 7 dependents 134 scripts 1.1k downloads

tmsens - Sensitivity Analysis Using the Trimmed Means Estimator

Sensitivity analysis using the trimmed means estimator.

Last updated 4 months ago

missing-datasensitivity-analysistrimmed-means

2.70 score 1 stars 1 scripts 177 downloads

varGWASR - Least Absolute Deviation Regression Brown Forsythe Test

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

Last updated 2 years ago

geneticsheteroscedasticityheteroskedasticitystatisticsvariance

2.70 score 1 stars

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

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

Last updated 1 years ago

openblascppopenmp

2.30 score 4 stars 10 scripts

MRPATH - R Package for MR-PATH

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

Last updated 9 months ago

openblascppopenmp

1.70 score 1 scripts

finemapr - R wrapper to fine-mappers

R wrapper to fine-mappers.

Last updated 11 months ago

1.70 score 6 scripts

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.

Last updated 4 years ago

1.70 score 1 stars