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 genetic 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 (MASL vs Control, MASH vs Control, MASH vs MASL).
  • 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.

Genetic evidence and evidence aggregation support candidate prioritization but do not by themselves establish causality.

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 uses dataset-specific differential-expression workflows, within-cohort correlation analyses, 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.

Contact

For the accompanying study and its bioRxiv reference, see Citation on the Home page.

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

fengzhaode@stu.xjtu.edu.cn