KGG:A systematic biological Knowledge-based mining system for Genome-wide Genetic studies

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KGG4.0 Demo Video

KGG3.0 Demo Video

KGG 2.5 Demo Video

Miaoxin Li

PMG Lab

MX Li's tools:

KGG

KGGSeq

wKGGSeq

GEC

SnpTracker

IGG

Note: KGG is no longer maintained since January 2022. The functions (gene-based association, DESE and gene-set-based association) in KGG have been inherited to its daughter tool KGGSEE. KGGSEE also has multiple new cool functions, including gene-based Mendelian randomisation and heritability estimation.

KGG (Knowledge-based mining system for Genome-wide Genetic studies) is a software tool to perform knowledge-based secondary analyses of p-values from genome-wide association studies (GWAS). The knowledge-based secondary analyses include gene-based, gene-pair-based and gene-set based association analysis.It is implemented by Java with a user-friendly graphic interface to facilitate data analysis and result visualization. Build on advanced algorithms, it is able to process up to 10 million variants in several hours with 15GB RAM on a workstation.
KGG4 currently provides 6 types of secondary analyses:

Gene-based association;

Conditional gene-based association;

Multivariate gene-based association;

Gene-pair-based association;

Gene-set-based association;

Driver-tissue estimation.
In addtion, it also provides hyperlinks to several useful bioinformatics annotation databases on sequence variants (GWASrap), genes (GeneCards) and pathways (MsigDB).

Please read the user-manual of KGG4 for details.

References & Citations:

    1. Li MX, Kwan JS, Sham PC. HYST: A HYbrid Set-based Test for genome-wide association studies, with application to protein-protein interaction-based association analysis. Am J Hum Genet. 2012 Sep 7;91(3):478-88. PubMed      AJHG
    2. Li MX, Gui HS, Kwan JS, Sham PC. GATES: A rapid and powerful gene-based association test using extended Simes procedure. Am J Hum Genet. 2011 Mar 11;88(3):283-293. PubMed      AJHG
    3. Li MX, Sham PC, Cherny SS, Song YQ.(2010) A knowledge-based weighting framework to boost the power of genome-wide association studies. PLoS One Dec 31;5(12):e14480. PubMed      Plos One
    4. Van der Sluis S, Dolan CV, Li J, Song Y, Sham P, Posthuma D, Li MX. MGAS: a powerful tool for multivariate gene-based genome-wide association analysis. Bioinformatics 2015 Apr 1;31(7):1007-15. PubMed      Bioinformatics
    5. Li et al. A powerful conditional gene-based association approach implicated functionally important genes for schizophrenia. Bioinformatics 2019 Feb 15;35(4):628-635. PubMed      Bioinformatics
    6. Gui et al. Sharing of Genes and Pathways Across Complex Phenotypes: A Multilevel Genome-Wide Analysis. Genetics2017 Jul;206(3):1601-1609. PubMed      Genetics
    7. Jiang L, Xue C, Dai S, Chen S, Chen P, Sham PC, Wang H, Li M. DESE: estimating driver tissues by selective expression of genes associated with complex diseases or traits. Genome Biol. 2019 Nov 6;20(1):233. PubMed      Genome Biology