KGGSum（Knowledge-based Genetic and Genomic analysis platform for GWAS Summary statistics）

Introduction

KGGSum (Knowledge-based Genetic and Genomic analysis platform for GWAS Summary statistics) is an open-source, Java-based platform tailored for interpreting LARGE-scale GWAS signals through comprehensive integrative analyses of omics data by advanced statistical models. It provides a cohesive framework that combines GWAS summary statistics with diverse omics layers—ranging from transcriptomic and proteomic data to perturbation expression datasets—across different cell types. Utilizing the robust GTB and CCF file systems, KGGSum effectively manages intermediate and resource data, supporting advanced models for prioritizing genes, cell types, gene networks, life exposures and even microbion driving complex phenotypes. With an intuitive command-line interface, extensive functionality, and thorough documentation, KGGSum is a versatile, one-stop solution for large-scale post-GWAS analyses.

Functionality

KGGSum currently supports the following function modules:

• association: a module that links genes, cell types, and gene networks to phenotypes based on the GWAS signals at variants. Various association analyses are driven by the corresponding resource data.

• causation: a module that infers causation from genes or exposures to phenotypes, mainly by various Mendelian randomization methods.

• annotation: a module that annotates variants with gene features and functional prediction scores.

Unique Advantages of KGGSum

• Fast and easy screening for multiple LARGE-scale GWAS summary datasets at a time
• Comprehensive with numerous high-quality resources for integrative analyses
• One-stop platform for a series of professional data mining

Citations

We kindly ask that you cite the relevant papers associated with the methods you utilize in the KGGSum platform. You can find the corresponding references under each method’s introduction.