Dataset Information
Computational analyses of obesity associated loci generated by genome-wide association studies.
Ontology highlight
ABSTRACT: Objectives
Genome-wide association studies (GWASs) have discovered associations of numerous SNPs and genes with obesity. However, the underlying molecular mechanisms through which these SNPs and genes affect the predisposition to obesity remain not fully understood. Aims of our study are to comprehensively characterize obesity GWAS SNPs and genes through computational approaches.Methods
For obesity GWAS identified SNPs, functional annotation, effects on miRNAs binding and impact on protein phosphorylation were performed via RegulomeDB and 3DSNP, miRNASNP, and the PhosSNP 1.0 database, respectively. For obesity associated genes, protein-protein interaction network construction, gene ontology and pathway enrichment analyses were performed by STRING, PANTHER and STRING, respectively.Results
A total of 445 SNPs are significantly associated with obesity related phenotypes at threshold P < 5×10-8. A number of SNPs were eQTLs for obesity associated genes, some SNPs located at binding sites of obesity related transcription factors. SNPs that might affect miRNAs binding and protein phosphorylation were identified. Protein-protein interaction network analysis identified the highly-interconnected "hub" genes. Obesity associated genes mainly involved in metabolic process and catalytic activity, and significantly enriched in 15 signal pathways.Conclusions
Our results provided the targets for follow-up experimental testing and further shed new light on obesity pathophysiology.
SUBMITTER: Cheng M
PROVIDER: S-EPMC6028139 | biostudies-literature | 2018
REPOSITORIES: biostudies-literature
Publications
Computational analyses of obesity associated loci generated by genome-wide association studies.
PloS one 20180702 7
<h4>Objectives</h4>Genome-wide association studies (GWASs) have discovered associations of numerous SNPs and genes with obesity. However, the underlying molecular mechanisms through which these SNPs and genes affect the predisposition to obesity remain not fully understood. Aims of our study are to comprehensively characterize obesity GWAS SNPs and genes through computational approaches.<h4>Methods</h4>For obesity GWAS identified SNPs, functional annotation, effects on miRNAs binding and impact ...[more]