Bioinformatics. 2024 Aug 10:btae505. doi: 10.1093/bioinformatics/btae505. Online ahead of print.
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: PyTWMR is available at github.com/soreshkov/pyTWMR.
CONTACT: Sergey.Oreshkov@chuv.ch; Federico.Santoni@chuv.ch.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
PMID:39128017 | DOI:10.1093/bioinformatics/btae505