Work

pyTWMR: transcriptome-wide Mendelian randomization in python

Year: 2024

Type: article

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.

Source: Bioinformatics

DOI: 10.1093/bioinformatics/btae505

Authors: Sergey Oreshkov, Kaido Lepik, Federico Santoni

Institutions: Centre Hospitalier Universitaire Vaudois, University of Lausanne, SIB Swiss Institute of Bioinformatics, Institute of Genetic and Biomedical Research, National Research Council

Language: English

Topic: Genetic Associations and Epidemiology

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $4,048

Funders: National Science Foundation, Novartis Foundation, European Commission, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung