Genome-wide meta-analysis of genetic susceptible genes for Type 2 Diabetes

Hale, Paul; López-Yunez, Alfredo M; Chen, Jake Y.

doi:10.1186/1752-0509-6-s3-s16

Cited by 35 publications

(24 citation statements)

References 39 publications

(38 reference statements)

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“…The study included postmenopausal women who were enrolled in the Genomics and Randomized Trials Network (GARNET) study, a substudy of the Women's Health Initiative (WHI), which aims to identify genetic variants associated with response to treatments for conditions of clinical or public health significance (Hale, Lopez-Yunez, & Chen, 2012;Haskell, Lee, & Pate, 2007). Details of that study's rationale and design have been described elsewhere (Hong, Chung, & Cho, 2014;Howe, Subbaramaiah, Hudis & Dannenberg, 2013;The Women's Health Initiative Study Group, 1998).…”

Section: Study Populationmentioning

confidence: 99%

Obesity and associated lifestyles modify the effect of glucose metabolism‐related genetic variants on impaired glucose homeostasis among postmenopausal women

Jung

Sobel

Papp

et al. 2016

Genetic Epidemiology

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Purpose Impaired glucose metabolism-related genetic variants likely interact with obesity-modifiable factors in response to glucose intolerance, yet their interconnected pathways have not been fully characterized. Methods With data from 1,027 postmenopausal participants of the Genomics and Randomized Trials Network study and 15 single-nucleotide polymorphisms (SNPs) associated with glucose homeostasis, we assessed whether obesity, physical activity, and high dietary fat intake interact with the SNP–glucose variations. We used regression analysis plus stratification and graphic approaches. Results Across carriers of the 15 SNPs, fasting levels of glucose, insulin, and homeostatic model assessment-insulin resistance (HOMA-IR) were higher in obese, inactive, and high fat-diet women than in their respective counterparts. Carriers within subgroups differently demonstrated the direction and/or magnitude of the variants’ effect on glucose-relevant traits. Variants in GCKR, GCK, DGKB/TMEM195 (P for interactions = 0.02, 0.02, and 0.01), especially, showed interactions with obesity: obese, inactive, and high fat-diet women had greater increases in fasting glucose, insulin, and HOMA-IR levels. Obese carriers at TCF7L2 variant had greater increases in fasting glucose levels than nonobese carriers (P for interaction = 0.04), whereas active women had greater decreases in insulin and HOMA-IR levels than inactive women (P for interaction = 0.02 in both levels). Conclusions Our data support the important role of obesity in modifying glucose homeostasis in response to glucose metabolism–relevant variants. These findings may inform research on the role of glucose homeostasis in the etiology of chronic disease and the development of intervention strategies to reduce risk in postmenopausal women.

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Section: Study Populationmentioning

confidence: 99%

Obesity and associated lifestyles modify the effect of glucose metabolism‐related genetic variants on impaired glucose homeostasis among postmenopausal women

Jung

Sobel

Papp

et al. 2016

Genetic Epidemiology

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“…To assign the initial score W 0 to each edge through the edge-to-edge network, we utilize the formula described in Ref. [34]:…”

Section: Rank Edges From Xmentioning

confidence: 99%

WIPER: Weighted in‐Path Edge Ranking for biomolecular association networks

et al. 2019

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Background: In network biology researchers generate biomolecular networks with candidate genes or proteins experimentally-derived from high-throughput data and known biomolecular associations. Current bioinformatics research focuses on characterizing candidate genes/proteins, or nodes, with network characteristics, e.g., betweenness centrality. However, there have been few research reports to characterize and prioritize biomolecular associations ("edges"), which can represent gene regulatory events essential to biological processes. Method: We developed Weighted In-Path Edge Ranking (WIPER), a new computational algorithm which can help evaluate all biomolecular interactions/associations ("edges") in a network model and generate a rank order of every edge based on their in-path traversal scores and statistical significance test result. To validate whether WIPER worked as we designed, we tested the algorithm on synthetic network models. Results: Our results showed WIPER can reliably discover both critical "well traversed in-path edges", which are statistically more traversed than normal edges, and "peripheral in-path edges", which are less traversed than normal edges. Compared with other simple measures such as betweenness centrality, WIPER provides better biological interpretations. In the case study of analyzing postanal pig hearts gene expression, WIPER highlighted new signaling pathways suggestive of cardiomyocyte regeneration and proliferation. In the case study of Alzheimer's disease genetic disorder association, WIPER reports SRC:APP, AR:APP, APP:FYN, and APP:NES edges (gene-gene associations) both statistically and biologically important from PubMed co-citation. Conclusion: We believe that WIPER will become an essential software tool to help biologists discover and validate essential signaling/regulatory events from high-throughput biology data in the context of biological networks. Availability: The free WIPER API is described at the website (discovery.informatics.uab.edu/wiper/).

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“…The reduced expression of PGC-1α and 1β is also co-regulated with the reduced expression of nuclear respiratory factor (NRF) regulated oxidative phosphorylation genes in both insulin resistant and diabetic subjects [66]; this was also demonstrated in a parallel study in Mexican Americans. TCF7L2, a commonly studied T2D gene in genome-wide association studies (GWAS) [67], is observed to have a strong association in diverse ethnic groups of T2D cases [68]. Recently, in an interaction analysis, TCF7L2 was reported to interact with 11 proteins.…”

Section: Protein-to-protein Interactions and Impaired Insulin Secretionmentioning

confidence: 99%

“…Recently, in an interaction analysis, TCF7L2 was reported to interact with 11 proteins. Furthermore, two new pathways (PPAR and adipocytokine signaling pathway) were shown to be associated with T2D, and PPARG was observed to be one of the top-ranking T2D risk genes in the protein interaction network [67].…”

Section: Protein-to-protein Interactions and Impaired Insulin Secretionmentioning

confidence: 99%

Understanding Genetic Heterogeneity in Type 2 Diabetes by Delineating Physiological Phenotypes:SIRT1and its Gene Network in Impaired Insulin Secretion

et al. 2016

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■ AbstractType 2 diabetes (T2D) is a chronic metabolic disease which shows an exponential increase in all parts of the world. However, the disease is controllable by early detection and modified lifestyle. A series of factors have been associated with the pathogenesis of diabetes, and genes are considered to play a critical role. The individual risk of developing T2D is determined by an altered genetic background of the enzymes involved in several metabolism-related biological mechanisms, including glucose homeostasis, insulin metabolism, the glucose and ion transporters involved in glucose uptake, transcription factors, signaling intermediates of insulin signaling pathways, insulin production and secretion, pancreatic tissue development, and apoptosis. However, many candidate genes have shown heterogeneity of associations with the disease in different populations. A possible approach to resolving this complexity and understanding genetic heterogeneity is to delineate the physiological phenotypes one by one as studying them in combination may cause discrepancies in association studies. A systems biology approach involving regulatory proteins, transcription factors, and microRNAs is one way to understand and identify key factors in complex diseases such as T2D. Our earlier studies have screened more than 100 single nucleotide polymorphisms (SNPs) belonging to more than 60 globally known T2D candidate genes in the Indian population. We observed that genes invariably involved in the activity of pancreatic β-cells provide susceptibility to type 2 diabetes (T2D). Encouraged by these results, we attempted to delineate in this review one of the commonest physiological phenotypes in T2D, namely impaired insulin secretion, as the cause of hyperglycemia. This review is also intended to explain the genetic basis of the pathophysiology of insulin secretion in the context of variations in the SIRT1 gene, a major switch that modulates insulin secretion, and a set of other genes such as HHEX, PGC-α, TCF7L2, UCP2, and ND3 which were found to be in association with T2D. The review aims to look at the genotypic and transcriptional regulatory relationships with the disease phenotype.

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Genome-wide meta-analysis of genetic susceptible genes for Type 2 Diabetes

Cited by 35 publications

References 39 publications

Obesity and associated lifestyles modify the effect of glucose metabolism‐related genetic variants on impaired glucose homeostasis among postmenopausal women

Obesity and associated lifestyles modify the effect of glucose metabolism‐related genetic variants on impaired glucose homeostasis among postmenopausal women

WIPER: Weighted in‐Path Edge Ranking for biomolecular association networks

Understanding Genetic Heterogeneity in Type 2 Diabetes by Delineating Physiological Phenotypes:SIRT1and its Gene Network in Impaired Insulin Secretion

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