College of Life Sciences, Central China Normal University, Wuhan, Hubei, China.
PLoS One. 2018 Jul 2;13(7):e0199987. doi: 10.1371/journal.pone.0199987. eCollection 2018.
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.
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.
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.
Our results provided the targets for follow-up experimental testing and further shed new light on obesity pathophysiology.
全基因组关联研究（GWAS）已经发现了许多 SNP 和基因与肥胖之间的关联。然而，这些 SNP 和基因影响肥胖易感性的潜在分子机制仍不完全清楚。我们研究的目的是通过计算方法全面描述肥胖 GWAS SNP 和基因。
对于肥胖 GWAS 鉴定的 SNP，通过 RegulomeDB 和 3DSNP、miRNASNP 和 PhosSNP 1.0 数据库分别进行功能注释、对 miRNA 结合的影响和对蛋白质磷酸化的影响。对于肥胖相关基因，通过 STRING、PANTHER 和 STRING 分别进行蛋白质-蛋白质相互作用网络构建、基因本体论和途径富集分析。
共有 445 个 SNP 在阈值 P < 5×10-8 时与肥胖相关表型显著相关。一些 SNP 是肥胖相关基因的 eQTL，一些 SNP 位于肥胖相关转录因子的结合位点。确定了可能影响 miRNA 结合和蛋白质磷酸化的 SNP。蛋白质-蛋白质相互作用网络分析确定了高度相互连接的“枢纽”基因。肥胖相关基因主要参与代谢过程和催化活性，并且在 15 个信号通路中显著富集。
我们的结果为后续实验测试提供了目标，并进一步阐明了肥胖的病理生理学。
