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Related Experiment Video

Updated: Jan 14, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

MRanalysis: a comprehensive online platform for integrated, multimethod Mendelian randomization and associated

Abao Xing¹, Tiantian Cai², Haofan Du³

¹Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Rua de Luís Gonzaga Gomes, Macao 999078, Macao SR.

|October 22, 2025

Summary

MRanalysis and GWASkit simplify Mendelian randomization (MR) and genome-wide association study (GWAS) data analysis. These tools enhance accessibility, reliability, and efficiency for genetic epidemiology research.

Keywords:

GWAS GWASkit MRanalysis Mendelian randomization SNP-to-gene enrichment online platform rs ID conversion visualization

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Last Updated: Jan 14, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Epidemiology
Genetic Epidemiology
Bioinformatics

Background:

Mendelian randomization (MR) infers causal relationships using genome-wide association study (GWAS) data.
Adoption of MR is hindered by data format inconsistencies, workflow standardization issues, and programming skill requirements.
MRanalysis and GWASkit were developed to address these limitations.

Purpose of the Study:

To develop user-friendly tools for integrated MR analysis and GWAS data preprocessing.
To lower barriers to entry for MR analysis, making it more accessible and efficient.
To accelerate discoveries in genetic epidemiology and inform public health strategies.

Main Methods:

MRanalysis offers a no-code, web-based platform for comprehensive MR analysis.
GWASkit provides a standalone tool for rapid GWAS data preprocessing, including rs ID conversion and format standardization.
Both tools feature intuitive interfaces and demonstrate high accuracy and efficiency.

Main Results:

MRanalysis supports univariable, multivariable, and mediation MR analyses with integrated quality assessment, power estimation, and visualization.
GWASkit significantly improves accuracy and efficiency in GWAS data preprocessing compared to existing tools.
Case studies confirm the practical utility and efficiency of MRanalysis and GWASkit.

Conclusions:

MRanalysis and GWASkit democratize MR analysis, enhancing its accessibility, reliability, and efficiency.
These tools can accelerate genetic discoveries, support public health initiatives, and guide targeted interventions.
MRanalysis and GWASkit represent a significant advancement in understanding gene-environment-health outcome relationships.