{
"question": [
"Is any of the genes SH2B3, IFIH1 or ERBB3 related to diabetes?",
"How can I use genenetwork to find genes related with diabetes in humans?",
"How can I use the GeneNetwork tool to find genes related with diabetes in humans?"
],
"answer": [
"Yes, the gene IFIH1 is identified as a contributor to susceptibility to type 1 diabetes. However, the text does not mention any direct relation of SH2B3 or ERBB3 to diabetes.",
"GeneNetwork can be used to find genes related to diabetes in humans by analyzing Genome-Wide Association Study (GWAS) data. This involves integrating this data with the human gene network, which can boost the performance of recovering validated type 2 diabetes genes. The network can also strongly implicate certain genes in type 2 diabetes. Additionally, the DisGeNET database can be used to collate gene-disease information, which can contribute to understanding the biology of type 2 diabetes. This approach can identify",
"You can use the GeneNetwork tool to find genes related to diabetes in humans by navigating to genenetwork.org and using the global search bar at the top of the page. You can search for genes, mRNAs, or proteins across all of the datasets. Use standard gene symbols containing more than two characters in the name for best results. You can also switch to phenotypes and search for any phenotype of interest. Additionally, you can use the Select and search pull-down menus to choose a population of interest."
],
"contexts": [
[
"Figure 8 Molecular changes in the islets of patients with T2D mirror the processes altered in NOD mice.mRNA expression in human pancreatic islets from healthy individuals (n = 105) and those diagnosed with T2D (n = 14) was assessed through RNA-seq analysis. (a) Relationship between GLIS3 and MANF expression in healthy individuals (Spearman correlation P value = 0.043), individuals with T2D (Spearman correlation P value = 0.075) and all individuals (Spearman correlation P value = 0.028). (b-e) Expression of XRCC4 (b), LIG4 (c), H2AFX (d) and CDKN1A (e) in healthy islets as compared to i slets from patients withT2D (P values shown after multiple-testing correction).The median and interquartile range (IQR; box) are shown, with error bars indicating 1.5 times the IQR.Individual values are shown if beyond 1.5 times the IQR. (f) Relationship between H2AFX and LIG4 expression in human islets (Spearman correlation P value = 5 10 9 ).Parallel transcriptional regulation in human isletsTo determine whether the findings observed in mice were applicable to humans, we investigated whether the pathway identified in NOD mice also demonstrated genetic linkage to diabetes or glucose regulation traits in humans.GLIS3 polymorphisms have previously been associated with altered glucose regulation; we additionally identified nominally significant associations for MANF, XRCC4 and LIG4 polymorphisms (Supplementary Table 2).In an independent approach that takes into account environmental effects, we analyzed RNA-seq data from human pancreatic islets isolated from 119 donors, including 14 diagnosed with T2D 28 .To assess the validity of the Glis3-Manf relationship observed in mice, we investigated the relationship of these two genes in human islets.A trend toward reduced GLIS3 expression was observed in T2D islets, whereas MANF expression appeared unchanged (Supplementary Fig. 13).Critically, a significant positive relationship was observed between GLIS3 and MANF levels in human islets (Fig. 8a).Next, we investigated whether patients with T2D might exhibit reduced XRCC4 expression, analogous to the NOD polymorphisms.We found no change in XRCC4 expression in T2D islets (Fig. 8b); however, the levels of the obligate binding partner encoded by LIG4 were significantly reduced (Fig. 8c).In mice, Xrcc4 polymorphisms were associated with increased senescence; likewise, in patients with T2D, the levels of the senescence markers H2AFX (Fig. 8d) and CDKN1A (Fig. 8e) were increased.Finally, a direct relationship was observed between reduced LIG4 and increased H2AFX levels (Fig. 8f).Although the cause of coregulation cannot be assessed in ex vivo human islets, the parallel with NOD mice strongly supports a conservation of diabetes susceptibility mechanisms across species.3,500,000 3,000,000 2,500,000 2,000,000 1,500,000 1,000,000 500,000 0 Fluorescence",
"All the genes involved in these pathways, as well as the genes involved in b-cells development and turnover, may be considered candidate genes for T2DM with predominant insulin deficiency.",
"One method of searching for the cause of NIDDM is via the candidate gene approach.Possible candidates for NIDDM include genes involved in specifying pancreatic islet (3-cell phenotype and in directing fj-cell development and (3-cell responses of glucose-mediated insulin synthesis and secretion.The transcription factor islet-1 (Isl-1) has been shown to be a unique protein that binds to the mini-enhancer or Far-FLAT region (nucleotide -247 to -198) of the rat insulin I gene (7).Isl-1, a protein comprised of 349 residues (38 kD), is a member of the LIM/homeodomain family of proteins, named for the first three members described: lin-11, isl-1, and mec-3 (8,9).These proteins are comprised of three putative regulatory regions, two LIM domains (cysteine-rich motifs) in the amino terminus of the protein, a homeobox domain near the middle, and a glutamine-rich transcriptional activation domain at the carboxyl end (7,9).With the use of an antibody to Isl-1, expression was shown to be restricted to a subset of endocrine cells, including islets, neurons involved in autonomic and endocrine control, and selected other tissues in the adult rat (10)(11)(12).",
"ResultsImpairment or alteration of the insulin-signaling pathway is a commonly recognized feature of type 2 diabetes.It is therefore notable that the IS-HD gene set (Dataset S4) was not detected to be significantly transcriptionally altered by application of either hypergeometric enrichmentt test, DEA or GSEA.In particular, applying GSEA to the transcriptional profile dataset of diabetic and normal glucose-tolerant skeletal muscle described in Mootha et al. [10] did not identify a significant level of alteration in the IS-HD gene set (p 0.536), while DEA produced a comparably weak enrichment score (p 0.607).The failure to detect a significant transcriptional alteration in IS-HD may be explained by a number of factors.The enrichment results depended on the specific choice of the IS-HD gene set, and it is possible that an alternatively defined insulin-signaling gene set would be determined as significantly enriched.Additionally, expression changes in a few critical genes in IS-HD may be sufficient to substantially alter insulin signaling, and running DEA on the large IS-HD set may miss the contributions from these few genes.",
"35ABSTRACT 11A GENE EXPRESSION NETWORK MODEL OF TYPE 2 DIABETESESTABLISHES A RELATIONSHIP BETWEEN CELL CYCLEREGULATION IN ISLETS AND DIABETES SUSCEPTIBILITYMP Keller, YJ Choi, P Wang, DB Davis, ME Rabaglia, AT Oler, DS Stapleton,C Argmann, KL Schueler, S Edwards, HA Steinberg, EC Neto, R Klienhanz, STurner, MK Hellerstein, EE Schadt, BS Yandell, C Kendziorski, and AD AttieDepts.",
"Second, we performed an extensive manual curation according to a previously described b-cell-targeted annotation (Kutlu et al, 2003;Ortis et al, 2010).In partial agreement with the IPA, we found these genes to fall into three broad categories: (1) genes related to b-cell dysfunction and death, (2) genes potentially facilitating the adaptation of the pancreatic islets to the altered metabolic situation in T2D and (3) genes whose role in disease pathogenesis remains to be unearthed (Figure 6B).The adaptation-related gene category contains few metabolism-associated genes (e.g., HK1, FBP2; Figure 6B, right part, Figure 7) and many more genes involved in signal transduction or encoding hormones, growth factors (e.g., EGF, FGF1, IGF2/IGF2AS; Figure 7), or transcription factors involved in important regulatory networks (for instance, FOXA2/HNF3B, PAX4 and SOX6) (Figure 6B, right part, Figure 7).In the b-cell dysfunction and death category, there were hypomethylated genes related to DNA damage and oxidative stress (e.g., GSTP1, ALDH3B1; Figure 7), the endoplasmic reticulum (ER) stress response (NIBAN, PPP2R4, CHAC1), and apoptosis (CASP10, NR4A1, MADD; Figure 6B, left part, Figure 7).Some genes of interest from the highlighted categories are depicted in Figure 7. Their annotated functions provide possible explanations of how the epigenetic dysregulation of these genes in diabetic islets is connected to T2D pathogenesis.Numerous genes that were identified by our methylation profiling approach have been functionally implicated in insulin secretion.Examination of the available literature on the function of these genes revealed three aspects of insulin secretion with which they interfere: some of these genes influence the expression of the insulin gene, like MAPK1 and SOX6, or its post-translational maturation, like PPP2R4 (cf. Figure 7 and references therein).Others can deregulate the process of insulin secretion itself (SLC25A5, Ahuja et al, 2007;RALGDS, Ljubicic et al, 2009) or influence synthesis as well as secretion (vitronectin, Kaido et al, 2006).A third group of differentially methylated genes affects (i) signalling processes in the b-cell leading to insulin secretion or (ii) glucose homeostasis in b-cells, thereby modulating insulin response upon stimulation.GRB10 (Yamamoto et al, 2008), FBP2 and HK1 (Figure 7) are examples for these genes.Additional genes found in our study have been implicated in the b-cells' capability to secrete insulin, though the mechanisms have not yet been fully established.The putative functions of these genes indicate a potential epigenetic impact on insulin secretion at multiple levels, namely signalling, expression/synthesis and secretion.",
"In summary, we have associated mutations in the SLC29A3 gene with diabetes mellitus in humans and the insulin signaling pathway in Drosophila.The mechanistic basis of these findings remains to be determined.This is strong evidence supporting the investment of resources to further investigate the role of SLC29A3 and its orthologs in diabetes and glucose metabolism in model systems.DISCUSSIONWe have identified mutations in the equilibrative nucleoside transporter 3 protein that are associated with an inherited syndrome of insulin-dependent DM, and provide prima facie evidence that the Drosophila ortholog of this protein interacts with the insulin signaling pathway.This is the first evidence that mutations in the human SLC29A3 gene can be associated with a diabetic phenotype.",
"These observations taken together suggest that molecules involved in innate immunity could serve as candidate genes that determine the susceptibility of sensitive strains of mice to virusinduced diabetes.Interestingly, deficiency of the Tyk2 gene results in a reduced antiviral response 24 .In addition, the human TYK2 gene was mapped to the possible type 1 diabetes susceptibility locus 25 .",
"A recent sequencing study provides an example of detection of rare variants in type 1 diabetes.Targeted sequencing in a series of candidate coding regions resulted in IFIH1 being identified as the causal gene in a region associated with type 1 diabetes by GWA studies (58).IFIH1 encodes a cytoplasmic helicase that mediates induction of the interferon response to viral RNA.The discovery of IFIH1 as a contributor to susceptibility to type 1 diabetes has strengthened the hypothesis (70) about a mechanism of disease pathogenesis involving virusgenetic interplay and raised type 1 interferon levels as a cofactor in -cell destruction.Nonetheless, it should be recognized that a component of the missing heritability (familial aggregation) in type 1 diabetes could well be due to unrecognized intra-familial environmental factors.Disease pathogenesis.Contemporary models of pathogenesis of type 1 diabetes support the involvement of two primary dramatis personae: the immune system and the -cell.The known and newly identified genetic risk factors for type 1 diabetes present exciting opportunities to build on to the current cast of disease mechanisms and networks.Most of the listed genes of interest (Table 2) and those in extended regions are assumed to regulate immune function.Some of these genes, however, may also have roles in the -cell (insulin being the most obvious example).Another gene, PTPN2, encoding a protein tyrosine phosphatase, was identified as affecting the risk for type 1 diabetes as well as for Crohn disease (47,71).PTPN2 is expressed in immune cells, and its expression is highly regulated by cytokines.However, PTPN2 is expressed also in -cells, where it modulates interferon (IFN)- signal transduction and has been shown to regulate cytokineinduced apoptosis (72).Other candidate genes, such as NOS2A, IL1B, reactive oxygen species scavengers, and candidate genes, identified in large GWA studies of type 2 diabetes, have not been found to be significant contributors to the susceptibility of type 1 diabetes (73).",
"Differential Expression Analyses of Type 1 Diabetes Mellitus Associated GenesFor the aforementioned 171 'novel' genes, we used t-test to compare ribonucleic acid expression signals in PBMCs or monocytes between type 1 diabetes mellitus patients and healthy controls.We found that 37 genes, including 21 non-HLA genes (e.g.FAM46B, OLFML3 and HIPK1), were differentially expressed between type 1 diabetes mellitus patients and controls (Table 2).For the differential expression study, the significance level of P < 5.0E-02 was used.",
"In this study, we have correlated the function and genotype of human islets obtained from diabetic and nondiabetic (ND) donors.We have analyzed a panel of 14 gene variants robustly associated with T2D susceptibility identified by recent genetic association studies.We have identified four genetic variants that confer reduced b-cell exocytosis and six variants that interfere with insulin granule distribution.Based on these observations, we calculate a genetic risk score for islet dysfunction leading to T2D that involves decreased docking of insulin-containing secretory granules, impaired insulin exocytosis, and reduced insulin secretion.",
"At present, insulin [15], glucokinase [16], amylin [17], mitochondrial DNA [18], and several transcriptional factors [19][20][21][22] are recognized as diabetogenic genes in pancreatic b-cells.In the present study we used the candidate gene approach in the examination of genomic variation in the a 1D and Kir6.2 channel genes in type 2 diabetic patients.",
"In summary, we report AEIs that are consistent with type 2 diabetes-associated variation regulating the expression of cis-linked genes in human islets.For some of the genes where significant AEI was identified (e.g., SLC30A8, WFS1), there is strong evidence from human genetics that small changes in gene dosage may have significant consequences for the pancreatic b-cell.For other genes with significant AEI (e.g., ANPEP, HMG20A), their role is less well defined, and hence this study should provide a platform for further work examining the effects of carefully manipulating the expression of these genes in human islets.",
"The authors then used mouse liver and adipose expressiondata from several mouse crosses to construct causal expression networks for the ERBB3 andRPS26 orthologs in the mouse. They then showed that ERBB3 is not associated with anyknown Type I diabetes genes whereas RPS26 is associated a network of several genes thatare part of the KEGG Type I diabetes pathway (Schadt et al. 2008). This type of analysisdemonstrates the power of combining human and mouse data with a network basedapproach that has been proposed for use in drug discovery (Schadt et al.",
"Genome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of 2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D.Genome-wide association studies in human type 2 diabetes (T2D) have renewed interest in the pancreatic islet as a contributor to T2D risk.Chronic low-grade inflammation resulting from obesity is a risk factor for T2D and a possible trigger of -cell failure.In this study, microarray data were collected from mouse islets after overnight treatment with cytokines at concentrations consistent with the chronic low-grade inflammation in T2D.Genes with a cytokine-induced change of 2-fold were then examined for associations between single nucleotide polymorphisms and the acute insulin response to glucose (AIRg) using data from the Genetics Underlying Diabetes in Hispanics (GUARDIAN) Consortium.Significant evidence of association was found between AIRg and single nucleotide polymorphisms in Arap3 (5q31.3),F13a1 (6p25.3),Klhl6 (3q27.1),Nid1 (1q42.3),Pamr1 (11p13), Ripk2 (8q21.3),and Steap4 (7q21.12).To assess the potential relevance to islet function, mouse islets were exposed to conditions modeling low-grade inflammation, mitochondrial stress, endoplasmic reticulum (ER) stress, glucotoxicity, and lipotoxicity.RT-PCR revealed that one or more forms of stress significantly altered expression levels of all genes except Arap3.Thapsigargininduced ER stress up-regulated both Pamr1 and Klhl6.Three genes confirmed microarray predictions of significant cytokine sensitivity: F13a1 was down-regulated 3.3-fold by cytokines, Ripk2 was up-regulated 1.5-to 3-fold by all stressors, and Steap4 was profoundly cytokine sensitive (167-fold up-regulation).Three genes were thus closely associated with low-grade inflammation in murine islets and also with a marker for islet function (AIRg) in a diabetes-prone human population.This islet-targeted genome-wide association scan identified several previously unrecognized candidate genes related to islet dysfunction during the development of T2D.In conclusion, GWAS studies focusing on the causes of T2D have implicated islet dysfunction as a major contributing factor (18,71).By examining isolated islets for stress responses and cross-referencing gene hits with genes associated with glucose-stimulated insulin release in human populations with T2D, we identified 7 genes that may play a role in promoting or preventing islet decline in T2D.By further examining stress-induced expression changes in each of these genes, we identified 5 genes that stood out: F13a1 as a novel stress-inhibited gene in islets, Klhl6 and Pamr1 as induced genes specific to ER stress, Ripk2 as a broadly stress-induced gene, and Steap4 as an exceptionally cytokine-sensitive gene.These genes provide promising leads in elucidating islet stress responses and islet dysfunction during the development of T2D.",
"Finally, several of the linking nodes introduced into this islet network through their PPI connections represent interesting candidates for a role in T2D pathogenesis, and there are several examples where external data provides validation of those assignments.An interesting example involves the gene GINS4 which maps at the ANK1 locus.Though this gene generated a low PCS [0.03] and was not included in the set of seed genes for this locus, GINS4 knock-down has an impact in a human beta-cell line [14].In addition, cyclin-dependent kinase 2 (CDK2) has been shown to influence beta-cell mass in a compensatory mechanism related to age-and diet-induced stress, connecting beta-cell dysfunction and progressive beta-cell mass deterioration [54].YHWAG is a member of the 14-3-3 family, known to be signalling hubs for beta-cell survival [55], and disruption of SMAD4 drives islet hypertrophy [56]."
],
[
"Beyond new gene discovery in the field of research, an important challenge in the next coming years is how to set up a more open population-level and high-quality genetic screening strategy aiming to improve etiological diagnosis in almost all of cases with early-onset diabetes.",
"In briefGardner et al. queried the genomes of over 400,000 individuals and identified novel genes associated with type 2 diabetes risk.The biological function of these genes highlights potentially new therapeutic avenues for treatment of type 2 diabetes.",
"Results: Here we report on a meta-analysis approach that integrates data of heterogeneous origin in the domain of type-2 diabetes mellitus (T2DM).Different data sources such as DNA microarrays and, complementing, qualitative data covering several human and mouse tissues are integrated and analyzed with a Bootstrap scoring approach in order to extract disease relevance of the genes.The purpose of the meta-analysis is two-fold: on the one hand it identifies a group of genes with overall disease relevance indicating common, tissue-independent processes related to the disease; on the other hand it identifies genes showing specific alterations with respect to a single study.Using a random sampling approach we computed a core set of 213 T2DM genes across multiple tissues in human and mouse, including well-known genes such as Pdk4, Adipoq, Scd, Pik3r1, Socs2 that monitor important hallmarks of T2DM, for example the strong relationship between obesity and insulin resistance, as well as a large fraction ( 128) of yet barely characterized novel candidate genes.Furthermore, we explored functional information and identified cellular networks associated with this core set of genes such as pathway information, protein-protein interactions and gene regulatory networks.Additionally, we set up a web interface in order to allow users to screen T2DM relevance for any -yet non-associated -gene.DiscussionThe first part of our study was devoted to the identification of genes related to T2DM using different heterogeneous data sources in different organisms.Genes have been scored in each individual study according to their disease relevance and an overall score across the different studies has been computed that reflects their total disease relevance.By this approach we were able to identify 213 genes that have a general disease relevance showing high scores in many different studies as well as genes that have a specific disease relevance expressing high scores in only a few studies.",
"GENE DISCOVERY IN T2DWhy?",
"Genetic approaches to studying type 1 diabetesTwo approaches have been used to identify diabetes susceptibility genes: genome-wide linkage studies and candidate gene association studies [see also Field (57) for a discussion of these approaches as applied to type 1 diabetes].These approaches have definitively shown that the major histocompatibility complex (MHC) locus, also called human leukocyte antigen or HLA, contains the major inherited factor(s) that determines diabetes risk.At least two other genes contain variants that almost certainly affect risk: the insulin gene (INS) and CTLA4.We will review the merits of these two genetic approaches used to identify diabetes susceptibility genes and the results obtained thus far.We also discuss the possible impact of genetic and genomic advances on future genetic studies.",
"Received: 7 May 2009 Accepted: 25 February 2010Published: 25 February 2010References1. Sieberts SK, Schadt EE: Moving toward a system genetics view of disease. Mamm Genome 2007, 18:389-401. 2. Keller MP, Choi Y, Wang P, Davis DB, Rabaglia ME, Oler AT, Stapleton DS,Argmann C, Schueler KL, Edwards S, Steinberg HA, Chaibub Neto E,Kleinhanz R, Turner S, Hellerstein MK, Schadt EE, Yandell BS, Kendziorski C,Attie AD: A gene expression network model of type 2 diabetes links cellcycle regulation in islets with diabetes susceptibility. Genome Res 2008,18:706-716. 3.",
"In conclusion, the findings presented in our study suggest high power for gene-based association analyses in detecting disease-susceptibility genes across the human genome.Our findings point to the involvement of new pathways in the pathogenesis of type 1 diabetes mellitus, and provide more insights into the genetic basis of type 1 diabetes mellitus.",
"A systematic genomewide search for type 2 diabetes-susceptibility genes was performed on a subset of 440 participants in the 27 most informative extended families.Of the 440 individuals, 116 are diabetics (including probands), giving a prevalence of 26.4%.There are 3,745 relative pairs, with varying degrees of genetic",
"Genome-wide association studies (GWAS) have discovered germline genetic variation associated with type 2 diabetes risk (1)(2)(3)(4).One of the largest GWAS, involving DNA taken from individuals of European descent and conducted by the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis) consortium, identified 65 loci associated with type 2 diabetes risk (1).However, for most of these loci, the precise identity of the affected gene and the molecular mechanisms underpinning the altered risk are not known.",
"Figure5.Consideration of the human gene network boosts recovery of validated type 2 diabetes genes from GWAS analysis of 2000 patients and 3000 controls. (A,B) Plotted using the same conventions as in Figure4, analyzing WTCCC GWAS data (Wellcome Trust Case Control Consortium 2007) for type 2 diabetes alone and in combination with HumanNet and measuring performance as AUC (<5% FPR) for recovering the top 20 genes from a type 2 diabetes meta-analysis of 4549 cases and 5579 controls(Zeggini et al. 2008).As for Crohn's disease, consideration of the network boosts performance across a wide range of parameter values.Notably, consideration of the network strongly implicates the genes CTNNB1 and BACH2 in type 2 diabetes; CTNNB1 is well studied in connection with type 2 diabetes and BACH2 has been previously implicated in type 1 diabetes and celiac disease (e.g.,Cooper et al. 2008;Madu et al. 2009), but not type 2 diabetes.",
"A new generation of genetic studies of diabetes is underway.Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes.Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk.Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants.We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.",
"One attractive methodology to circumvent the puzzle of choosing either a hypothesis-driven or an exploratory research may be the strategy of gene prioritization offered by the new bioinformatics tools based on the biological plausibility of a gene-disease association and on knowledge of the protein function. 6e propose an approach for expanding the selection of genes or loci of interest and prioritizing associations over GWAs related with genetic susceptibility to type 2 diabetes.The proposal profits from the recent initiatives of data sharing of the genome scan results that make the information publicly available as soon as they are generated and checked for quality.Both the DGI and the WTCCC are committed to embracing these principles as they made available all the phenotype-genotype data for type 2 diabetes.",
"In this review, however, we focus on a different route from human genetics to translation, one that derives estimates of an individual's predisposition to diabetes and its subtypes (in the form of polygenic scores) from the patterns of individual geneticvariation at sites known to influence diabetes predisposition.",
"Family-based studies of the genetic determinants of type 2 diabetes and related precursor quantitative traits (QTs, e.g.plasma insulin and glucose levels) and GWA studies have now provided an abundance of evidence for potentially causative genes.These results have been drawn together onto a single map of the human genome sequence [86].The goal is to look for genomic locations where the presence of a potential underlying type 2 diabetes gene has been attested to repeatedly-diabetes genetic 'hot spots'.Such replication increases our confidence of the presence of an underlying gene.While GWA studies look for diabetes genes using a different approach to linkage analysis, the ultimate goal is the same-to find the genetic determinants of the disease.Therefore, the results of linkage and association must eventually match each other.The current analysis identifies multiple linkage locations that differ from those found in the recent GWA studies [87-89], and suggests the location of additional major type 2 diabetes susceptibility genes.",
"INTRODUCTIONMultiple genome-wide association studies (GWASs) have correlated type 2 diabetes mellitus (T2DM) with genetic variants, yielding a large number of loci and associated gene products that are linked to the disease phenotype-often with little or no insight into the mechanism underlying that link (Hivert et al., 2014).The current challenge is to establish robust systems to systematically evaluate the role of these loci using disease-relevant cells.Previous studies have used patient samples, cell lines, or animal models to seek mechanistic insight but with significant limitations.Large variation is observed in primary patient samples, perhaps due to genetic heterogeneity, whereas animal models present major physiological and metabolic differences that hamper understanding of the precise function of human genes in T2DM.Therefore, a robust system to systematically evaluate the role of T2DM-associated genes using disease-relevant human cells will provide an important tool for diabetes research and spur the development of precision (allele-specific) therapies, exemplified by the use of sulfonylurea drugs to treat patients carrying certain KCNJ11 mutations (Gloyn et al., 2004).",
"Genomic information associated with Type 2 diabetes.",
"To gain insights into how the linking nodes of our final network contribute to T2D biology, we used the DisGeNET database [37], which collates gene-disease information from public data as well as from literature via natural language processing tools.We focused on the 274 linking nodes included in our model to avoid circularity arising from using the seeds, and identified 92 (~33%) with known links to T2D (Additional file 1: Table S2).Examples include as follows: (a) NEUROD1 which encodes a transcription factor that is involved in the development of the endocrine cell lineage and has been implicated in monogenic diabetes [38], (b) PRKCB involved in insulin resistance [39] and (c) GNAS, implicated in beta-cell proliferation [40].For this last gene, mouse knockouts have been shown to produce phenotypes concordant with diabetes [41].These examples demonstrate the potential of these analyses to draw in \"linking\" nodes as related to T2D even when they are not located within genome-wide association signals.",
" Human Genome Project -its Implications in Diabetes GeneticsThe USA coordinator of the Human Genome Project at the National Institute of Health (NIH), Francis Collins (Bethesda, MD), expects the entire human genome to be sequenced by 2002, the complete sequence of chromosomes 22 and 7 already being available in 1999.The NIH will invest US$ 75 million to identify another 500 000 SNPs genome wide.The USA SNP mapping will be based on 500 cell lines and would have to be followed by linkage mapping in all major populations.The other global players of the Human Genome Project, including the SNP consortium and several private companies, are also putting major efforts into the identification of genes encoding type 2 diabetes.Extensive international collaborations will be crucial in order to carry the enormous financial and manpower burden needed to achieve these goals.Therefore, the data generated must be freely accessible throughout the scientific community.As diabetes will become a WHO priority in 2000, this might foster more investment into the research of the genetics of diabetes.",
"Genetic predisposition to diabetes mellitus type 2: will large collaborative efforts be able to overcome the geneticist's nightmare?"
],
[
"This approach requires the accumulation and integration of many types of data,and also requires the use of many types of statistical tools to extract relevant patterns ofcovariation and causal relations as a function of genetics, environment, stage, and treatment. Inthis protocol we explain how to use the GeneNetwork web service, a powerful and free onlineresource for systems genetics. We provide workflows and methods to navigate massive multiscalardata sets and we explain how to use an extensive systems genetics toolkit for analysis andsynthesis.",
"GeneNetwork is an interactive software (Geisert et al. , 2009), which enables usersreadily to reconstruct genetic network based on microarraydata without being intimately involved in complicatedmathematical computation. Materials and methodsMiceOne pair of heterozygous (lew/ ) mice was purchasedfrom the Mouse Mutant Stock Resource colonies at TheJackson Laboratory (TJL). A breeding colony was thenestablished by mating them at the University of TennesseeHealth Science Center (UTHSC).",
"T2DM-GeneMiner web toolIn order to allow users to screen the disease potential of any given gene of interest we developed T2DM-GeneMiner, a web interface summarizing the results of our work (Figure 1, [35]).The user interface is shown for the wellknown Adipoq and the resulting bar plots for two other genes, Pdk4 and Cfd, with lower content of available infor-mation.The resource is searchable by gene or protein IDs (for example Ensembl ID or gene symbol).The score distribution is shown as a bar plot and, where available, functional information is displayed.The two rightmost bars show the entropy, indicating uniform or specific score distribution, and the score.The red line at the score bar indicates the cut-off.Background: Multiple functional genomics data for complex human diseases have been published and made available by researchers worldwide.The main goal of these studies is the detailed analysis of a particular aspect of the disease.Complementary, meta-analysis approaches try to extract supersets of disease genes and interaction networks by integrating and combining these individual studies using statistical approaches.Results: Here we report on a meta-analysis approach that integrates data of heterogeneous origin in the domain of type-2 diabetes mellitus (T2DM).Different data sources such as DNA microarrays and, complementing, qualitative data covering several human and mouse tissues are integrated and analyzed with a Bootstrap scoring approach in order to extract disease relevance of the genes.The purpose of the meta-analysis is two-fold: on the one hand it identifies a group of genes with overall disease relevance indicating common, tissue-independent processes related to the disease; on the other hand it identifies genes showing specific alterations with respect to a single study.Using a random sampling approach we computed a core set of 213 T2DM genes across multiple tissues in human and mouse, including well-known genes such as Pdk4, Adipoq, Scd, Pik3r1, Socs2 that monitor important hallmarks of T2DM, for example the strong relationship between obesity and insulin resistance, as well as a large fraction ( 128) of yet barely characterized novel candidate genes.Furthermore, we explored functional information and identified cellular networks associated with this core set of genes such as pathway information, protein-protein interactions and gene regulatory networks.Additionally, we set up a web interface in order to allow users to screen T2DM relevance for any -yet non-associated -gene. Conclusion:In our paper we have identified a core set of 213 T2DM candidate genes by a metaanalysis of existing data sources.We have explored the relation of these genes to disease relevant information and -using enrichment analysis -we have identified biological networks on different layers of cellular information such as signaling and metabolic pathways, gene regulatory networks and protein-protein interactions.The web interface is accessible via http://t2dmgeneminer.molgen.mpg.de.",
"Exploring genes, molecules, and phenotypes is easily accomplished using GeneNetwork. In thismanuscript we will outline some simple use cases, and show how a small number of plausiblecandidate genes can be identified for an immune phenotype. 1. DataOnce you have navigated to genenetwork.org, there are two ways to search for data in GN. Thefirst is to use the global search bar located at the top of the page (Figure 1). This is a newfeature in GN that allows researchers to search for genes, mRNAs, or proteins across all of thedatasets.Similarly, by using the dropdown menu on the left (Figure 1), a user can switch to phenotypes,and search for any phenotype of interest in the same way. Figure 1: The global search bar, also called the Search All function, is a good area to start exploringgenes, mRNA, and proteins within GeneNetwork. To best use this new tool, use standard gene symbolscontaining more than two characters in the name. Another area to acquire data is the Select and search pull-down menus (Figure 2). To getstarted, the user has to choose a population of interest.",
"This approach requires the accumulation and integration of many types of data,and also requires the use of many types of statistical tools to extract relevant patterns ofcovariation and causal relations as a function of genetics, environment, stage, and treatment. Inthis protocol we explain how to use the GeneNetwork web service, a powerful and free onlineresource for systems genetics. We provide workflows and methods to navigate massive multiscalardata sets and we explain how to use an extensive systems genetics toolkit for analysis andsynthesis.",
"The Web tool G2D (Genes to Diseases) prioritizesgenes across a user-entered chromosomal region according to their possible relationto an inherited disease by a combination of data mining of OMIM, PubMed MESH9.6 IDENTIFICATION OF POTENTIALLY FUNCTIONAL POLYMORPHISMS211terms and Gene Ontology (GO) classification. The tool allows users to inspect anyregion of the human genome to find candidate genes related to a genetic disease orphenotype defined in OMIM. It does this by identifying GO terms that match MESHterms for an OMIM record.",
"Researchers, however, have thepossibility to fully explore the results by altering the thresholds on the open web resource. Although onlyprotein-coding genes were included in our analysis, the same approach can be applied to non-coding genes63to reveal their potential functions. Similarly, GeneBridge can also be utilized to identify novel gene-diseaseassociations based on known disease-associated genes from databases, such as the Human DiseaseOntology (DO) [207] or DisGeNET [208]. The GeneBridge toolkit could also be applied to large-scaleproteomics datasets after correcting for the background of all measured proteins.",
"Protein interaction networksWe searched for protein networks spanning the regions shown to interact genetically (P values < 0.05; Table 2).This was performed using a high-confidence human protein inter- Markers of predictive value for T1D identified by decision tree analysis on T1D genome scan data from 1321 affected sib pair families.Markers identified in the total data set are ranked according to significance level (P < 0.05).Markers from data subsets are 'selected markers' and were selected on basis of whether they confirm loci from the latest T1D genome scan [25] or other references [26; 27].D.f. = degrees of freedom.",
"Exploring genes, molecules, and phenotypes is easily accomplished using GeneNetwork. In thismanuscript we will outline some simple use cases, and show how a small number of plausiblecandidate genes can be identified for an immune phenotype. 1. DataOnce you have navigated to genenetwork.org, there are two ways to search for data in GN. Thefirst is to use the global search bar located at the top of the page (Figure 1). This is a newfeature in GN that allows researchers to search for genes, mRNAs, or proteins across all of thedatasets.Similarly, by using the dropdown menu on the left (Figure 1), a user can switch to phenotypes,and search for any phenotype of interest in the same way. Figure 1: The global search bar, also called the Search All function, is a good area to start exploringgenes, mRNA, and proteins within GeneNetwork. To best use this new tool, use standard gene symbolscontaining more than two characters in the name. Another area to acquire data is the Select and search pull-down menus (Figure 2). To getstarted, the user has to choose a population of interest.",
"Users begin by selecting one or more human diseases andclicking on Compare. The genes associated with the selected diseaseare tested for enrichment against all sets of known associated genes forworm phenotypes. The result reveals functionally coherent, evolutionarily conserved gene networks. Alternatively, users can also start by selecting worm phenotypes,which are tested against human diseases. In addition to cross-speciestesting, results of within-species disease enrichment are also available(e.g. to nd the closest related human disease for another input humandisease).",
"GeneNetwork is an interactive software (Geisert et al. , 2009), which enables usersreadily to reconstruct genetic network based on microarraydata without being intimately involved in complicatedmathematical computation. Materials and methodsMiceOne pair of heterozygous (lew/ ) mice was purchasedfrom the Mouse Mutant Stock Resource colonies at TheJackson Laboratory (TJL). A breeding colony was thenestablished by mating them at the University of TennesseeHealth Science Center (UTHSC).",
"Genome Biol 8(2):R25Hubner N, Wallace CA, Zimdahl H, Petretto E, Schulz H et al (2005)Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nat Genet 37(3):243253Ihaka R, Gentleman RC (1996) R: a language for data analysis andgraphics. J Comput Graph Stat 5:299314Keller MP, Choi Y, Wang P, Davis DB, Rabaglia ME et al (2008) Agene expression network model of type 2 diabetes links cellcycle regulation in islets with diabetes susceptibility.",
"We next constructed protein-protein interaction networks.To do this, we selected 76 genes known from monogenic forms of diabetes, obesity, and hypertension or GWAS hits (type 2 diabetes, obesity, and hypertension) for which the lead association lies within the protein-coding part of the gene (Table S3).",
"First, we describe the construction of a functional network for human genes.This network spans 87% of validated protein coding genes, and provides strong predictive power for a majority of currently known genetic diseases.We evaluate six alternate approaches for prioritizing candidate disease genes using this network, and demonstrate the strongest overall performance with algorithms related to Google's PageRank.We then show that this network, in conjunction with genome-wide association data for Type 2 diabetes and Crohn's disease, boosts the identification of disease-associated genes that were discovered in later meta-analyses.This work suggests both a specific strategy and a general path to future improvements for the interpretation of GWAS data.Taken together, our work demonstrates that a high-quality functional network for human genes can provide a powerful resource for identifying causal genes in human disease.A new functional gene network for human genesIn order to test the general ability of a gene network to prioritize disease genes, particularly in conjunction with GWAS studies, we constructed a genome-scale functional network of human genes, incorporating diverse expression, protein interaction, genetic interaction, sequence, literature, and comparative genomics data, including both data collected directly from human genes, as well as that from orthologous genes of yeast, worm, and fly.The resulting HumanNet gene network can be accessed through a web interface (http://www.functionalnet.org/humannet).Using this interface, researchers can easily search the network using a set of ''seed'' Network-guided genome-wide association mining genes of interest.The interface returns a list of genes ranked according to their connections to the seed genes, together with the evidence used to identify each coupling.The interactions and evidence can be downloaded, and a network visualization tool has been incorporated.All linkages can also be downloaded for independent analysis.",
"Construction and analysis of the T2D risk genes networkTo further sift the results and explore functional connections, we also mapped genes onto known gene sets.For this purpose, we used DAVID [22,23] to search for enriched KEGG [24] pathways.We also used GARNET [25] to identify enriched Gene Ontology categories and their relationships.",
"GeneNetwork.org also offers a powerful statistical platform foronline network analyses and mapping, enabling numerous molecular questions to be probed in one centralized location(Chesler et al. , 2003, 2005; Li et al. , 2010; Mulligan et al. , 2012,2017, 2019). Most data are from groups of animals or humanswho have been fully genotyped or even sequenced. As a result, itcan be used to model causal networks that link DNA differencesto traits such as differences in expression, cell number, volumes,and behavior using real-time computation and graphing."
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