MASLD Portal

About the MASLD Portal

The MASLD Portal is a research-oriented web platform focusing on metabolic dysfunction-associated steatotic liver disease (MASLD). It integrates transcriptomic meta-analysis, gene/pathway–clinical correlation meta-analysis, and human genetics evidence to help researchers prioritize candidate genes and pathways for functional follow-up.

What this portal provides

This portal is designed as a single entry point for several MASLD-focused analyses:

  • MASLD stage differential meta-analysis (MAFL vs Control, MASH vs Control, MASH vs MAFL).
  • Fibrosis differential meta-analysis (Fib 1–2 vs 0, Fib 3–4 vs 1–2, Fib 3–4 vs 0).
  • Gene–clinical correlation meta-analysis, visualized as radar plots across liver-related phenotypes.
  • Pathway–clinical correlation meta-analysis, summarizing disease-relevant pathways across clinical indices.
  • Genetics panel that summarizes GWAS/TWAS/PWAS/SMR/PheWAS evidence for individual genes.

Data and methods (overview)

The MASLD Portal is built on large collections of publicly available datasets, including:

  • Human liver transcriptome datasets (MASLD/fibrosis cohorts) from GEO and related repositories.
  • Gene- and pathway–clinical correlation meta-analysis results using aggregated phenotype information.
  • Summary-level genetics resources (GWAS, TWAS, PWAS, SMR, PheWAS) collected for MASLD related traits.

Methodologically, the portal makes extensive use of standard transcriptome-wide differential expression workflows , correlation analysis, and random-effects meta-analysis. For details on datasets, please refer to the Datasets page.

Related resources

Members of the same research environment also develop and maintain GeneBridge, a systems genetics resource for cross-species and cross-cohort integration:

https://www.systems-genetics.org/genebridge

GeneBridge and the MASLD Portal share a common philosophy: leveraging large public datasets and robust statistics to generate hypotheses that can be tested in experimental systems.

Citation and contact

If you find the MASLD Portal useful in your work, please consider citing the corresponding manuscript (preprint/manuscript details will be added once available) and acknowledging the portal in your methods or acknowledgements section.

For feedback, bug reports, or collaboration ideas, please contact:

fengzhaode@stu.xjtu.edu.cn