ArticleGenome medicine2019
Developing a network view of type 2 diabetes risk pathways through integration of genetic, genomic and functional data.
Article in Genome medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 50 citations in OpenAlex.
- Integrated Genomic-Metabolomic Analysis for Tri-Categorical Classification of Type 2 Diabetes Status in the Korean Ansan-Ansung Cohort.International journal of molecular sciences · 2025Article
- Variant-to-function approaches for adipose tissue: Insights into cardiometabolic disorders.Cell genomics · 2025Review
- Shared transcriptional regulators and network rewiring identify therapeutic targets linking type 2 diabetes mellitus and hypertension.Frontiers in molecular biosciences · 2025Article
- Protein interaction networks in the vasculature prioritize genes and pathways underlying coronary artery disease.Communications biology · 2024Article
- Extending protein interaction networks using proteoforms and small molecules.Bioinformatics (Oxford, England) · 2023Article
- Is 14-3-3 the Combination to Unlock New Pathways to Improve Metabolic Homeostasis and β-Cell Function?Diabetes · 2023Review
- Panax notoginseng alleviates oxidative stress through miRNA regulations based on systems biology approach.Chinese medicine · 2023Article
- Genome-wide analyses of 200,453 individuals yield new insights into the causes and consequences of clonal hematopoiesis.Nature genetics · 2022Article
- TSABL: Trait Specific Annotation Based Locus predictor.BMC genomics · 2022Article
- Diabetes risk loci-associated pathways are shared across metabolic tissues.BMC genomics · 2022Article
- Enlightening the molecular mechanisms of type 2 diabetes with a novel pathway clustering and pathway subnetwork approach.Turkish journal of biology = Turk biyoloji dergisi · 2022Article
- A Final Frontier in Environment-Genome Interactions? Integrated, Multi-Omic Approaches to Predictions of Non-Communicable Disease Risk.Frontiers in genetics · 2022Review
- Gaining insight into metabolic diseases from human genetic discoveries.Trends in genetics : TIG · 2021Review
- Insulin's Discovery: New Insights on Its Hundredth Birthday: From Insulin Action and Clearance to Sweet Networks.International journal of molecular sciences · 2021Review
- DOMINO: a network-based active module identification algorithm with reduced rate of false calls.Molecular systems biology · 2021Article
- miR-140-5p Aggravates Insulin Resistance via Directly Targeting GYS1 and PPP1CC in Insulin-Resistant HepG2 Cells.Diabetes, metabolic syndrome and obesity : targets and therapy · 2021Article
- CONQUER: an interactive toolbox to understand functional consequences of GWAS hits.NAR genomics and bioinformatics · 2020Article
- Bioinformatic Reconstruction and Analysis of Gene Networks Related to Glucose Variability in Diabetes and Its Complications.International journal of molecular sciences · 2020Article
- Charting the life course: Emerging opportunities to advance scientific approaches using life course research.Journal of clinical and translational science · 2020Article
- Patch-Seq Links Single-Cell Transcriptomes to Human Islet Dysfunction in Diabetes.Cell metabolism · 2020Article
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Authors and funding
9 authors at 6 institutions in 3 countries.
Funding
Abstract
backgroundGenome-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.
methodsHere, 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.
resultsWe 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
conclusionsThese 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.