OmicsGWAS: A multi-omics integrative analysis platform for genome-wide association studies
image: The pipeline takes GWAS summary statistics from two or more traits as input. The omics_format_data module first performs quality control, format standardization, and allele harmonization to ensure cross-trait and cross-population consistency. For dual-trait analyses, the workflow proceeds through several sequential modules: (1) Genetic correlation analysis, including global genetic correlation (LDSC, HDL, GNOVA) and local genetic correlation (LAVA, SUPERGNOVA, HDL-L, HESS, GWAS-PW); (2) Genetic pleiotropy analysis, integrating MTAG, CPASSOC, PLACO, pleioFDR, MiXeR, and CCGWAS to identify shared genetic loci and pleiotropic architecture, with significant results from MTAG and CPASSOC further filtered via the CPASSOC_mtag_sig module; (3) Gene prioritization, employing FUMA, MAGMA, POPS, fine-mapping, XWAS, and fastBAT-GCTA for candidate gene annotation and signal refinement; and (4) Downstream functional interpretation, leveraging FLAMES, FOCUS, FUSION, UTMOST, MetaXcan, xQTL-GWAS SMR/MR/COLOC, multi-omic MR/SMR, and miRNA-xQTL MR to systematically prioritize causal genes and molecular pathways through expression regulation, tissue-specific transcriptomic association, colocalization, and causal inference. Beyond dual-trait analysis, the framework supports multi-GWAS extensions: METAL for cross-population meta-analysis of the same trait, and GenomicSEM for modeling shared genetic factors across multiple traits. The get_full_* functions (denoted by asterisks) enable direct retrieval of database-level xQTL resources, including MacroMap, pancell, dynamic CD4, ImmuNexUT, OASIS, tenk10k, and eQTL Catalogue.
Credit: Xu zhang，Kailai Li, Yasi Zhang, Cenwen Liu, Shunyi Huang, Yingzhao Jin, Yugeng Gao, Quan Cheng，Yunyun Mei
With the rapid advancement of genome-wide association studies (GWAS), downstream analyses of GWAS data have become essential for elucidating the genetic mechanisms that underlie complex diseases. However, current post-GWAS analyses face numerous challenges, including heterogeneous data formats, challenges in multi-omics integration, and increasingly complex analytical workflows. As a comprehensive post-GWAS analysis software platform, OmicsGWAS provides researchers with an automated, end-to-end solution spanning data preparation through results visualization by integrating functional modules such as data standardization, Mendelian randomization, multi-omics joint analysis, comorbidity mechanism exploration, and drug target discovery. The platform supports conversion of GWAS summary statistics across diverse data sources, integrates multidimensional omics data including expression quantitative trait loci, protein quantitative trait loci, and methylation quantitative trait loci, and systematically investigates causal relationships between genotypes and phenotypes using methods such as Mendelian randomization, colocalization analysis, and summary-data-based Mendelian randomization. The modular design of OmicsGWAS not only significantly improves analytical efficiency and the reproducibility of results but also offers robust technical support for precision medicine research and drug target development.