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Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network

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 functio...

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Autores principales: Sandor, Cynthia, Beer, Nicola L., Webber, Caleb
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5667928/
https://www.ncbi.nlm.nih.gov/pubmed/29059180
http://dx.doi.org/10.1371/journal.pcbi.1005816
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author Sandor, Cynthia
Beer, Nicola L.
Webber, Caleb
author_facet Sandor, Cynthia
Beer, Nicola L.
Webber, Caleb
author_sort Sandor, Cynthia
collection PubMed
description 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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spelling pubmed-56679282017-11-18 Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network Sandor, Cynthia Beer, Nicola L. Webber, Caleb PLoS Comput Biol Research Article 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. Public Library of Science 2017-10-23 /pmc/articles/PMC5667928/ /pubmed/29059180 http://dx.doi.org/10.1371/journal.pcbi.1005816 Text en © 2017 Sandor et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Sandor, Cynthia
Beer, Nicola L.
Webber, Caleb
Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network
title Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network
title_full Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network
title_fullStr Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network
title_full_unstemmed Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network
title_short Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network
title_sort diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5667928/
https://www.ncbi.nlm.nih.gov/pubmed/29059180
http://dx.doi.org/10.1371/journal.pcbi.1005816
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