Genome-wide association study (GWAS) has revolutionized our understanding of complex traits genetics, gaining insight into the phenotypic biology, estimating their heritability, calculating genetic correlations, making clinical risk predictions, informing drug development, and inferring potential causal relationships between risk factors and health outcomes. Despite numerous algorithms and tools to explore the genetic architecture of phenotypes and investigate molecular mechanisms, the lack of an integrative and comprehensive platform for unifying these methodologies has constrained the efficiency and translational potential of post-GWAS analyses. To address this gap, we developed pGWAS-Portal, a unified one-stop platform that covers six progressive analytical modules: i) Heritability Estimate module, qualifying the contribution to trait variability; ii) Enrichment Pattern module, characterizing gene, tissue, cell type, and pathway enrichment patterns associated with phenotypic variation; iii) Cross-Trait Analysis module, integrating genetic data across multiple phenotypes; iv) Fine-Mapping module, identifying potentially causal variants within associated loci; v) Correlation and Causality module, calculating phenotypic correlations and putative causal relationships; and vi) Risk Factors Identification module, detecting potential trait-associated biological factors. Implementing over thirty well-established algorithms within a unified framework, pGWAS-Portal offers a user-friendly, freely available web server ( http://bio-computing.hrbmu.edu.cn/posgwaser ; https://bio-computing.hrbmu.edu.cn/posgwaser ) to streamline and enhance post-GWAS investigations. The pGWAS-Portal is a user-friendly, freely accessible web server that provides a comprehensive solution for various post-GWAS analyses. It features six advanced analytical modules, incorporating over thirty classic algorithms, designed to estimate heritability, characterize enrichment pattern, analyze cross-trait, fine-mapping, calculate correlation and causality, and identify risk factors.
Zhu et al. (Wed,) studied this question.