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Genome Med. 2019 Mar 26;11(1):19. doi: 10.1186/s13073-019-0628-8.

Developing a network view of type 2 diabetes risk pathways through integration of genetic, genomic and functional data.

Author information

1
Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
2
Department of Pediatrics, University of California, San Diego, CA, USA.
3
Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.
4
Present Address: Department of Bioinformatics and Data Mining, Novo Nordisk A/S, Maaloev, Denmark.
5
Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK.
6
Department of Surgery, Massachusetts, General Hospital, Boston, MA, USA.
7
Broad Institute of MIT and Harvard, Cambridge, MA, USA.
8
Harvard Medical School, Boston, MA, USA.
9
Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK. mark.mccarthy@drl.ox.ac.uk.
10
Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK. mark.mccarthy@drl.ox.ac.uk.
11
Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK. mark.mccarthy@drl.ox.ac.uk.

Abstract

BACKGROUND:

Genome-wide association studies (GWAS) have identified several hundred susceptibility loci for type 2 diabetes (T2D). One critical, but unresolved, issue concerns the extent to which the mechanisms through which these diverse signals influencing T2D predisposition converge on a limited set of biological processes. However, the causal variants identified by GWAS mostly fall into a non-coding sequence, complicating the task of defining the effector transcripts through which they operate.

METHODS:

Here, we describe implementation of an analytical pipeline to address this question. First, we integrate multiple sources of genetic, genomic and biological data to assign positional candidacy scores to the genes that map to T2D GWAS signals. Second, we introduce genes with high scores as seeds within a network optimization algorithm (the asymmetric prize-collecting Steiner tree approach) which uses external, experimentally confirmed protein-protein interaction (PPI) data to generate high-confidence sub-networks. Third, we use GWAS data to test the T2D association enrichment of the "non-seed" proteins introduced into the network, as a measure of the overall functional connectivity of the network.

RESULTS:

We find (a) non-seed proteins in the T2D protein-interaction network so generated (comprising 705 nodes) are enriched for association to T2D (p = 0.0014) but not control traits, (b) stronger T2D-enrichment for islets than other tissues when we use RNA expression data to generate tissue-specific PPI networks and (c) enhanced enrichment (p = 3.9 × 10- 5) when we combine the analysis of the islet-specific PPI network with a focus on the subset of T2D GWAS loci which act through defective insulin secretion.

CONCLUSIONS:

These analyses reveal a pattern of non-random functional connectivity between candidate causal genes at T2D GWAS loci and highlight the products of genes including YWHAG, SMAD4 or CDK2 as potential contributors to T2D-relevant islet dysfunction. The approach we describe can be applied to other complex genetic and genomic datasets, facilitating integration of diverse data types into disease-associated networks.

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