GenomicSEM - Structural equation modeling based on GWAS summary statistics

Later

Last updated 22 days ago

7.08 score 205 stars 111 scripts

MVMR - MVMR

An R package for performing multivariable Mendelian randomization analyses.

Last updated 27 days ago

6.42 score 42 stars 1 packages 140 scripts

RadialMR - RadialMR

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

Last updated 25 days ago

5.91 score 33 stars 3 packages 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

5.29 score 2 stars 163 scripts

hdi - High-Dimensional Inference

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

Last updated 3 years ago

4.57 score 2 stars 7 packages 134 scripts 1.3k downloads

tmsens - Sensitivity Analysis Using the Trimmed Means Estimator

Sensitivity analysis using the trimmed means estimator.

Last updated 3 months ago

missing-datasensitivity-analysistrimmed-means

2.70 score 1 stars 1 scripts 186 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

finemapr - R wrapper to fine-mappers

R wrapper to fine-mappers.

Last updated 10 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