Semantic networks for genome-wide CNV associated with AST and ALT in Korean cohorts

Abstract

Copy number variation (CNV) is an emerging approach to study about human health and diseases. Liver-related biochemical tests (aspartate aminotransferase: AST, alanine aminotransferase: ALT) are useful for diagnosing a patient with liver injury. We analyzed a CNV-based GWAS of AST and ALT in 407 Korean. Affymetrix Human 6.0 Array was used to identify CNV, and CNV segmentation was performed using CNV analysis software. Univariate linear regression was used for the GWAS using R package. We identified 64 CNVs associated with AST or ALT, and screened 228 genes located within our CNVs. In this study, we focused on semantic networks about liver disease using knowledge integration software. This semantic networks about liver disease contained entities like gene, disease, pathway, chemical, drug, and contained relationships between two entities like gene-pathway, gene-disease, pathway-chemical, disease-pathway, chemical-drug. Application of semantic networks shown three clusters, including four diseases (hepatocellular carcinoma, liver neoplasm, liver cell adenoma, drug-induced liver injury), one pathway (hepatitis C pathway), and seven drugs (acetaminophen, chlormezanone, stavudine, enflurane, isoniazid, mebendazole, nitisinone).