Contact

Developed by Haoyang Zhang (Lund University), Hongwei Chen (Sun Yat-sen University), and Huiyao Chen (Fudan University).
If you have any questions, please contact Haoyang (haoyang.zhang@med.lu.se) or Hongwei (chenhw66@mail2.sysu.edu.cn).

Update

MOCR-DB (Release 2025.12) — Last updated: December 8, 2025. All datasets and results shown in this version are based on the data processing workflow described above.

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Data and Analysis Workflow

MOCR-DB is built on large-scale GWAS summary statistics from UK Biobank, FinnGen, and the COVID-19 Host Genetics Initiative, together with 53 xQTL datasets across 49 tissues. All GWAS datasets are processed through a uniform pipeline, including removal of very rare binary traits, harmonization of trait terminology using UMLS, basic quality control of summary statistics, alignment to the 1000 Genomes reference, and exclusion of MHC variants and traits with unclear phenotype definitions. After these steps, 613 GWAS datasets are retained and combined with eQTL, sQTL, mQTL, and caQTL resources to support downstream analyses of genetic correlation (LDSC), causal inference (MR), and functional gene mapping (SMR).

Web Interface and Core Modules

The web interface of MOCR-DB provides access to these analyses through several modules. Users can browse basic data summaries on the homepage, explore MR-based causal networks in the Genetic Causality module, and inspect LDSC-derived correlation patterns and trait networks in the Genetic Correlation module. The Functional Genes module allows users to view tissue-specific SMR results together with gene set enrichment based on GO, KEGG, and Reactome. An optional AI-assisted panel offers brief text summaries of selected MR, LDSC, and SMR outputs to help users review large result sets without replacing the underlying statistical analyses.

Operation Guide for Genetic Causality and Genetic Correlation Modules

The Genetic Causality and Genetic Correlation modules allow users to explore phenotype-to-phenotype relationships in MOCR-DB. (A) In the Genetic Causality module, users can select a trait of interest, adjust network and significance parameters, and inspect MR-based results through causal networks, effect-size plots, pleiotropy tests, and downloadable result tables. (B) In the Genetic Correlation module, users can examine LDSC-derived heritability and genetic correlation results, visualize genetically related traits as networks, and review detailed correlation estimates in the table panel. Both modules are supported by AI-assisted summaries to help users interpret complex statistical outputs in plain language.

Operation Guide for the Functional Gene Analysis Module

The Functional Gene Analysis module helps users explore candidate phenotype-to-gene associations based on SMR analysis. Users can select a trait and QTL tissue source, review candidate gene-trait associations across tissues, download detailed SMR results, and examine enrichment results based on Gene Ontology (GO) and Reactome pathways. The module also includes an AI-assisted interpretation panel, which provides plain-language summaries of enriched biological processes and helps users understand potential functional patterns among SMR-prioritized genes.