ArticlePLoS computational biology2017
Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Deep phenotyping for precision medicine in Parkinson's disease.Disease models & mechanisms · 2022Article
- Integration of functional genomics data to uncover cell type-specific pathways affected in Parkinson's disease.Biochemical Society transactions · 2021Review
- Review
- Scientific Advances in Diabetes: The Impact of the Innovative Medicines Initiative.Frontiers in medicine · 2021Review
- The genomic basis of mood instability: identification of 46 loci in 363,705 UK Biobank participants, genetic correlation with psychiatric disorders, and association with gene expression and function.Molecular psychiatry · 2020Article
- Computational Analysis of the Global Effects ofGenes · 2020Article
- A network embedding model for pathogenic genes prediction by multi-path random walking on heterogeneous network.BMC medical genomics · 2019Article
- Phenotypic characterization of a novel type 2 diabetes animal model in a SHANXI MU colony of Chinese hamsters.Endocrine · 2019Article
- Protein ensembles link genotype to phenotype.PLoS computational biology · 2019Review
- Altered Brain Functional Hubs and Connectivity in Type 2 Diabetes Mellitus Patients: A Resting-State fMRI Study.Frontiers in aging neuroscience · 2018Article
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3 authors.
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Abstract
Type 2 Diabetes (T2D) constitutes a global health burden. Efforts to uncover predisposing genetic variation have been considerable, yet detailed knowledge of the underlying pathogenesis remains poor. Here, we constructed a T2D phenotypic-linkage network (T2D-PLN), by integrating diverse gene functional information that highlight genes, which when disrupted in mice, elicit similar T2D-relevant phenotypes. Sensitising the network to T2D-relevant phenotypes enabled significant functional convergence to be detected between genes implicated in monogenic or syndromic diabetes and genes lying within genomic regions associated with T2D common risk. We extended these analyses to a recent multiethnic T2D case-control exome of 12,940 individuals that found no evidence of T2D risk association for rare frequency variants outside of previously known T2D risk loci. Examining associations involving protein-truncating variants (PTV), most at low population frequencies, the T2D-PLN was able to identify a convergent set of biological pathways that were perturbed within four of five independent T2D case/control ethnic sets of 2000 to 5000 exomes each. These same pathways were found to be over-represented among both known monogenic or syndromic diabetes genes and genes within T2D-associated common risk loci. Our study demonstrates convergent biology amongst variants representing different classes of T2D genetic risk. Although convergence was observed at the pathway level, few of the contributing genes were found in common between different cohorts or variant classes, most notably between the exome variant sets which suggests that future rare variant studies may be better focusing their power onto a single population of recent common ancestry.
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