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MRanalysis: a comprehensive online platform for integrated, multimethod Mendelian randomization and associated post-GWAS analyses

  • Abao Xing
  • , Tiantian Cai
  • , Haofan Du
  • , Zhifan Li
  • , Hoiman Ng
  • , Junrong Li
  • , Guanmin Jiang
  • , Lijun Chen
  • , Kefeng Li

研究成果: Article同行評審

摘要

Background: Mendelian randomization (MR) is a powerful epidemiological method for inferring causal relationships between exposures and outcomes using genome-wide association study (GWAS) data. However, its adoption is limited by inconsistent data formats, lack of standardized workflows, and the need for programming expertise. To address these challenges, we developed MRanalysis, a user-friendly, web-based platform for integrated MR analysis, and GWASkit, a standalone tool for GWAS data preprocessing. Results: MRanalysis provides a comprehensive, no-code workflow for MR analysis, including data quality assessment, power estimation, single-nucleotide polymorphism to gene enrichment, and visualization. It supports univariable, multivariable, and mediation MR analyses through an intuitive interface. GWASkit facilitates rapid GWAS data preprocessing, such as rs ID conversion and format standardization, with significantly higher accuracy and efficiency than existing tools. Case studies demonstrate the utility and efficiency of both tools in real-world scenarios. Conclusions: MRanalysis and GWASkit lower barriers to MR analysis, making it more accessible, reliable, and efficient. By democratizing MR, these tools can accelerate discoveries in genetic epidemiology, inform public health strategies, and guide targeted interventions. MRanalysis is freely available at https://mranalysis.cn, and GWASkit can be accessed at https://github.com/Li-OmicsLab-MPU/GWASkit. Together, they represent a significant advance in understanding the complex relationships between genes, exposures, and health outcomes.

原文English
文章編號giaf131
期刊GigaScience
14
DOIs
出版狀態Published - 2025

UN SDG

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  1. Good health and well being
    Good health and well being

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