MOCR-DB: The Multi-Omics Causal Resource Database for Genetic Correlation, Causal Inference, and Functional Interpretation.
7 affiliations
- 1School of Public Health (Shenzhen), Shenzhen Key Laboratory of Pathogenic Microbes and Biosafety, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107 Guangdong China.
- 2School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510275 Guangdong China.
- 3Department of CICU, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623 Guangdong China.
- 4Center for Molecular Medicine, Children's Hospital of Fudan University, Shanghai, 201102 China.
- 5Guangdong Provincial Key Laboratory of Infection Immunity and Inflammation, School of Basic Medical Sciences, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060 China.
- 6Key Laboratory of Pathogen Infection Prevention and Control (MOE), State Key Laboratory of Respiratory Health and Multimorbidity, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 102629 China.
- 7Department of Experimental Medical Science, Lund University, Lund, 22184 Sweden.
Abstract
Genome-wide association studies (GWAS) have revealed extensive polygenic signals and overlapping genetic architectures across human traits, creating a need for resources that connect trait-level genetic relationships with gene-level functional evidence. Here, we developed the Multi-Omics Causal Resource Database (MOCR-DB), an interactive platform that integrates large-scale GWAS summary statistics from UK Biobank, FinnGen, and the COVID-19 Host Genetics Initiative with molecular quantitative trait locus (QTL) datasets. In total, 613 traits with significant heritability were retained and harmonized using the Unified Medical Language System. MOCR-DB integrates phenotype-to-phenotype analyses, including genetic correlation and Mendelian randomization, with phenotype-to-gene analyses based on QTL-informed summary-data-based Mendelian randomization analysis (SMR) within a single searchable and interactive framework. The platform supports exploration of cross-trait genetic correlations, putative causal relationships, and candidate functional gene associations. An AI-assisted module provides concise plain-language summaries to help contextualize statistical findings. As a case study, we examined obesity and COVID-19 severity, where genetically predicted obesity showed a stronger association with critical COVID-19 and lung eQTL-based SMR analyses revealed distinct immune- and neuronal-related molecular patterns across severity groups. MOCR-DB thus provides a unified and accessible resource for investigating shared genetic architectures and prioritized functional gene candidates across complex traits, supporting the generation of reproducible and biologically interpretable hypotheses. The database is publicly available at https://chenhongwei.net/public/MOCRdb/. Data resources, analytical framework, and interpretation in MOCR-DB The Multi-Omics Causal Resource Database (MOCR-DB) integrates large-scale GWAS summary statistics and molecular QTL datasets to provide a unified framework for genetic correlation, causal inference, and functional mediation. Data resources include GWAS summary statistics from UK Biobank, FinnGen, and the COVID-19 Host Genetics Initiative, together with 53 xQTL datasets across 49 tissues (eQTL, mQTL, sQTL, and caQTL). The analytical framework combines linkage disequilibrium score regression (LDSC) for estimating heritability and cross-trait genetic correlation, Mendelian randomization (MR) to infer potential causal relationships between traits, and summary-data-based Mendelian randomization (SMR) to identify tissue-specific functional genes. Results are presented through interactive genetic network searches that link diseases, biomarkers, lifestyle factors, and molecular traits via correlation, causality, and functional annotation. An AI-assisted module further facilitates causal and functional interpretation by summarizing complex results from LDSC, MR, and SMR analyses into accessible biological insights. Together, MOCR-DB provides systematic exploration of shared genetic architectures and functional mediators across complex human traits. The online version contains supplementary material available at https://doi.org/10.1007/s43657-026-00338-w.
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BibTeX
@article{chen2026mocrdb,
title = {{MOCR-DB: The Multi-Omics Causal Resource Database for Genetic Correlation, Causal Inference, and Functional Interpretation.}},
author = {Hongwei Chen and Bingjie Fan and Huiyao Chen and Yongkun Chen and Yuelong Shu and Haoyang Zhang},
journal = {Phenomics (Cham, Switzerland)},
year = {2026},
month = aug,
doi = {10.1007/s43657-026-00338-w},
url = {https://europepmc.org/article/MED/42999447},
note = {CC BY},
}
RIS
TY - JOUR
TI - MOCR-DB: The Multi-Omics Causal Resource Database for Genetic Correlation, Causal Inference, and Functional Interpretation.
AU - Hongwei Chen
AU - Bingjie Fan
AU - Huiyao Chen
AU - Yongkun Chen
AU - Yuelong Shu
AU - Haoyang Zhang
PY - 2026
DA - 2026/08/28
JO - Phenomics (Cham, Switzerland)
DO - 10.1007/s43657-026-00338-w
UR - https://europepmc.org/article/MED/42999447
AB - Genome-wide association studies (GWAS) have revealed extensive polygenic signals and overlapping genetic architectures across human traits, creating a need for resources that connect trait-level genetic relationships with gene-level functional evidence. Here, we developed the Multi-Omics Causal Resource Database (MOCR-DB), an interactive platform that integrates large-scale GWAS summary statistics from UK Biobank, FinnGen, and the COVID-19 Host Genetics Initiative with molecular quantitative trait locus (QTL) datasets. In total, 613 traits with significant heritability were retained and harmonized using the Unified Medical Language System. MOCR-DB integrates phenotype-to-phenotype analyses, including genetic correlation and Mendelian randomization, with phenotype-to-gene analyses based on QTL-informed summary-data-based Mendelian randomization analysis (SMR) within a single searchable and interactive framework. The platform supports exploration of cross-trait genetic correlations, putative causal relationships, and candidate functional gene associations. An AI-assisted module provides concise plain-language summaries to help contextualize statistical findings. As a case study, we examined obesity and COVID-19 severity, where genetically predicted obesity showed a stronger association with critical COVID-19 and lung eQTL-based SMR analyses revealed distinct immune- and neuronal-related molecular patterns across severity groups. MOCR-DB thus provides a unified and accessible resource for investigating shared genetic architectures and prioritized functional gene candidates across complex traits, supporting the generation of reproducible and biologically interpretable hypotheses. The database is publicly available at https://chenhongwei.net/public/MOCRdb/. Data resources, analytical framework, and interpretation in MOCR-DB The Multi-Omics Causal Resource Database (MOCR-DB) integrates large-scale GWAS summary statistics and molecular QTL datasets to provide a unified framework for genetic correlation, causal inference, and functional mediation. Data resources include GWAS summary statistics from UK Biobank, FinnGen, and the COVID-19 Host Genetics Initiative, together with 53 xQTL datasets across 49 tissues (eQTL, mQTL, sQTL, and caQTL). The analytical framework combines linkage disequilibrium score regression (LDSC) for estimating heritability and cross-trait genetic correlation, Mendelian randomization (MR) to infer potential causal relationships between traits, and summary-data-based Mendelian randomization (SMR) to identify tissue-specific functional genes. Results are presented through interactive genetic network searches that link diseases, biomarkers, lifestyle factors, and molecular traits via correlation, causality, and functional annotation. An AI-assisted module further facilitates causal and functional interpretation by summarizing complex results from LDSC, MR, and SMR analyses into accessible biological insights. Together, MOCR-DB provides systematic exploration of shared genetic architectures and functional mediators across complex human traits. The online version contains supplementary material available at https://doi.org/10.1007/s43657-026-00338-w.
SN - 2730-583X
SN - 2730-5848
ER -