Treffer: pyTWMR: transcriptome-wide Mendelian randomization in python.
Title:
pyTWMR: transcriptome-wide Mendelian randomization in python.
Authors:
Source:
Bioinformatics, vol. 40, no. 8
Publication Year:
2024
Collection:
Université de Lausanne (UNIL): Serval - Serveur académique lausannois
Subject Terms:
Document Type:
Fachzeitschrift
article in journal/newspaper
File Description:
application/pdf
Language:
English
Relation:
info:eu-repo/semantics/altIdentifier/pmid/39128017; info:eu-repo/semantics/altIdentifier/eissn/1367-4811; info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:serval-BIB_975C14D52EEC7; https://serval.unil.ch/notice/serval:BIB_975C14D52EEC; https://serval.unil.ch/resource/serval:BIB_975C14D52EEC.P001/REF.pdf
DOI:
10.1093/bioinformatics/btae505
Availability:
Rights:
info:eu-repo/semantics/openAccess ; CC BY 4.0 ; https://creativecommons.org/licenses/by/4.0/
Accession Number:
edsbas.C67E8DC3
Database:
BASE
Weitere Informationen
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. 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. pyTWMR is available at github.com/soreshkov/pyTWMR.