pyTWMR: transcriptome-wide Mendelian randomization in python

Sergey Oreshkov; Kaido Lepik; Federico Santoni

doi:10.1093/bioinformatics/btae505

pyTWMR: transcriptome-wide Mendelian randomization in python

Bioinformatics. 2024 Aug 2;40(8):btae505. doi: 10.1093/bioinformatics/btae505.

Authors

Sergey Oreshkov^{1

2}, Kaido Lepik^{3

4

5}, Federico Santoni^{1

2

6}

Affiliations

¹ Endocrine, Diabetes and Metabolism Service, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne 1005, Switzerland.
² Faculty of Biology and Medicine, University of Lausanne, Lausanne 1005, Switzerland.
³ Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland.
⁴ University Center for Primary Care and Public Health, University of Lausanne, Lausanne 1005, Switzerland.
⁵ Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.
⁶ Institute for Genetic and Biomedical Research (IRGB) - CNR, Monserrato 09042, Italy.

Abstract

Motivation: Mendelian randomization (MR) is a widely used approach to estimate causal effect of variation in gene expression on complex traits. Among several MR-based algorithms, transcriptome-wide summary statistics-based Mendelian Randomization approach (TWMR) enables the uses of multiple SNPs as instruments and multiple gene expression traits as exposures to facilitate causal inference in observational studies.

Results: Here we present a Python-based implementation of TWMR and revTWMR. Our implementation offers GPU computational support for faster computations and robust computation mode resilient to highly correlated gene expressions and genetic variants.

Availability and implementation: pyTWMR is available at github.com/soreshkov/pyTWMR.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Humans
Mendelian Randomization Analysis* / methods
Polymorphism, Single Nucleotide*
Software*
Transcriptome* / genetics

Abstract

Publication types

MeSH terms

Grants and funding