ArticlePloS one2013
Systems biology approach reveals genome to phenome correlation in type 2 diabetes.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed, 63 citations in OpenAlex.
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- Identification and preliminary clinical validation of type 2 diabetes signature genes through machine learning analysis of scRNA-seq data.Frontiers in medicine · 2026Article
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- Identification and verification of ferroptosis-related genes in diabetic foot using bioinformatics analysis.International wound journal · 2023Article
- Identification and validation of core genes for type 2 diabetes mellitus by integrated analysis of single-cell and bulk RNA-sequencing.European journal of medical research · 2023Article
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- Diabetes mellitus susceptibility with varied diseased phenotypes and its comparison with phenome interactome networks.World journal of clinical cases · 2022Review
- Comprehensive analysis of key m5C modification-related genes in type 2 diabetes.Frontiers in genetics · 2022Article
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- Elucidate multidimensionality of type 1 diabetes mellitus heterogeneity by multifaceted information.Scientific reports · 2021Article
- qPCR Analysis Reveals Association of Differential Expression ofFrontiers in endocrinology · 2021Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic β cells and insulin signaling in target tissues. However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidates perturbation of insulin secretion. Also, the disease associated genes do not clearly converge on functional categories consistent with the known aspects of T2D pathophysiology. We used a systems biology approach to unravel genome to phenome correlation in T2D. We first examined enrichment of pathways in genes identified in T2D GWASs at genome-wide or lower levels of significance. Genes at lower significance threshold showed enrichment of insulin secretion related pathway. Notably, physical and genetic interaction network of these genes showed robust enrichment of insulin signaling and other T2D pathophysiology related pathways including insulin secretion. The network also overrepresented genes reported to interact with insulin secretion and insulin action targeting antidiabetic drugs. The drug interacting genes themselves showed overrepresentation of insulin signaling and other T2D relevant pathways. Next, we generated genome-wide expression profiles of multiple insulin responsive tissues from nondiabetic and diabetic patients. Remarkably, the differentially expressed genes showed significant overlap with the network genes, with the intersection showing enrichment of insulin signaling and other pathways consistent with T2D pathophysiology. Literature search led our genomic, interactomic, transcriptomic and toxicogenomic evidence to converge on TGF-beta signaling, a pathway known to play a crucial role in pancreatic islets development and function, and insulin signaling. Cumulatively, we find that GWAS genes relate directly to insulin secretion and indirectly, through collaborating with other genes, to insulin resistance. This seems to support the epidemiological evidence that environmentally triggered insulin resistance interacts with genetically programmed β cell dysfunction to precipitate diabetes.
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