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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Published inPLoS computational biology Vol. 13; no. 10; p. e1005816
Main Authors Sandor, Cynthia, Beer, Nicola L., Webber, Caleb
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 01.10.2017
Public Library of Science (PLoS)
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ISSN1553-7358
1553-734X
1553-7358
DOI10.1371/journal.pcbi.1005816

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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.
AbstractList 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.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.
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.
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. Type 2 Diabetes (T2D) has been the subject of a large number of genetic studies that have identified multiple and heterogeneous risk variants. However, our knowledge of the underlying perturbed molecular pathways influenced by these variants remains poor. Exploiting our prior knowledge of T2D-relevant mammalian phenotypes, we developed a novel network-based approach focused on bringing sources of information together about gene function that are most relevant to T2D. This approach is able to identify convergent biology amongst variants representing different classes of T2D genetic risk, including low-effect common variants, high-effect rare variants, and variants drawn from recent population-based exome studies. Our study shows that a broad range of genetic variation influences T2D-risk through a limited number of biological pathways.
Audience Academic
Author Beer, Nicola L.
Webber, Caleb
Sandor, Cynthia
AuthorAffiliation Mount Sinai School of Medicine, UNITED STATES
2 Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
1 Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
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ContentType Journal Article
Copyright COPYRIGHT 2017 Public Library of Science
2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Sandor C, Beer NL, Webber C (2017) Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network. PLoS Comput Biol13(10): e1005816. https://doi.org/10.1371/journal.pcbi.1005816
2017 Sandor et al 2017 Sandor et al
2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Sandor C, Beer NL, Webber C (2017) Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network. PLoS Comput Biol13(10): e1005816. https://doi.org/10.1371/journal.pcbi.1005816
Copyright_xml – notice: COPYRIGHT 2017 Public Library of Science
– notice: 2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Sandor C, Beer NL, Webber C (2017) Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network. PLoS Comput Biol13(10): e1005816. https://doi.org/10.1371/journal.pcbi.1005816
– notice: 2017 Sandor et al 2017 Sandor et al
– notice: 2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Sandor C, Beer NL, Webber C (2017) Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network. PLoS Comput Biol13(10): e1005816. https://doi.org/10.1371/journal.pcbi.1005816
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Snippet Type 2 Diabetes (T2D) constitutes a global health burden. Efforts to uncover predisposing genetic variation have been considerable, yet detailed knowledge of...
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StartPage e1005816
SubjectTerms Acids
Anatomy & physiology
Biology
Biology and Life Sciences
Computer and Information Sciences
Convergence
Diabetes
Diabetes mellitus
Diabetes Mellitus, Type 2 - epidemiology
Diabetes Mellitus, Type 2 - genetics
Disease
Ethnic factors
Funding
Gene expression
Gene Regulatory Networks - genetics
Genes
Genetic aspects
Genetic diversity
Genetic Markers - genetics
Genetic Predisposition to Disease - epidemiology
Genetic Predisposition to Disease - genetics
Genetic Variation - genetics
Genome-Wide Association Study - methods
Genomes
Global health
Health risks
Humans
Loci
Medical research
Medicine and Health Sciences
Ontology
Open source software
Pathogenesis
Pathways
Physiology
Polymorphism, Single Nucleotide - genetics
Prevalence
Proteome - genetics
Reproducibility of Results
Research and Analysis Methods
Risk analysis
Risk Factors
Sensitivity and Specificity
Studies
Type 2 diabetes
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Title Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network
URI https://www.ncbi.nlm.nih.gov/pubmed/29059180
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https://www.proquest.com/docview/1955060517
https://pubmed.ncbi.nlm.nih.gov/PMC5667928
https://doaj.org/article/743b4573209e4c3aa59747309cb8d803
http://dx.doi.org/10.1371/journal.pcbi.1005816
Volume 13
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