Identification of genetic variants associated with diabetic kidney disease in multiple Korean cohorts via a genome-wide association study mega-analysis

Table 6 KEGG pathway analysis results with the five genes

KEGG ID	Pathway description	Counts in network	Strength	FDR	Matching proteins in the network
hsa04612	Antigen processing and presentation	2/63	2.09	0.011	HLA-DQA1, HLA-A
hsa04940	Type I diabetes mellitus	2/39	2.30	0.011	HLA-DQA1, HLA-A
hsa05320	Autoimmune thyroid disease	2/48	2.21	0.011	HLA-DQA1, HLA-A
hsa05330	Allograft rejection	2/34	2.36	0.011	HLA-DQA1, HLA-A
hsa05332	Graft-versus-host disease	2/36	2.34	0.011	HLA-DQA1, HLA-A
hsa05416	Viral myocarditis	2/55	2.15	0.011	HLA-DQA1, HLA-A
hsa04145	Phagosome	2/142	1.74	0.024	HLA-DQA1, HLA-A
hsa04514	Cell adhesion molecules	2/137	1.76	0.024	HLA-DQA1, HLA-A
hsa05169	Epstein-Barr virus infection	2/193	1.61	0.036	HLA-DQA1, HLA-A
hsa05166	Human T cell leukemia virus 1 infection	2/211	1.57	0.039	HLA-DQA1, HLA-A

This measure describes how large the enrichment effect is. This measure describes how large the enrichment effect is. It is the ratio between the number of proteins in the network that are annotated with a term and the number of proteins that we can expect to be annotated with this term in a random network of the same size
KEGG Kyoto Encyclopedia of Genes and Genomes, FDR false discovery rate, Strength Log10(observed/expected)

ISSN: 1741-7015