Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Genome-wide association studies in type 2 diabetes.

ABSTRACT: Despite numerous candidate gene and linkage studies, the field of type 2 diabetes (T2D) genetics had until recently succeeded in identifying few genuine disease-susceptibility loci. The advent of genome-wide association (GWA) scans has transformed the situation, leading to an expansion in the number of established, robustly replicating T2D loci to almost 20. These novel findings offer unique insights into the pathogenesis of T2D and in the main point toward the etiologic importance of disorders of beta-cell development and function. All associated variants have common allele frequencies in the discovery populations, and exert modest to small effects on the risk of disease, characteristics that limit their prognostic and diagnostic potential. However, ongoing studies focusing on the role of copy number variation and targeting low-frequency polymorphisms should identify additional T2D susceptibility loci, some of which may have larger effect sizes and offer better individual prediction of disease risk.

SUBMITTER: McCarthy MI

PROVIDER: S-EPMC2694564 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Genome-wide association studies in type 2 diabetes.

McCarthy Mark I MI Zeggini Eleftheria E

Current diabetes reports 20090401 2

Despite numerous candidate gene and linkage studies, the field of type 2 diabetes (T2D) genetics had until recently succeeded in identifying few genuine disease-susceptibility loci. The advent of genome-wide association (GWA) scans has transformed the situation, leading to an expansion in the number of established, robustly replicating T2D loci to almost 20. These novel findings offer unique insights into the pathogenesis of T2D and in the main point toward the etiologic importance of disorders ...[more]

PMID: 19323962

Similar Datasets

Risk Prediction Using Genome-Wide Association Studies on Type 2 Diabetes.

Project description:The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the "large p and small n" problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC) for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

| S-EPMC5287117 | biostudies-literature

Type 1 diabetes genome-wide association studies: not to be lost in translation.

Project description:Genetic studies have identified >60 loci associated with the risk of developing type 1 diabetes (T1D). The vast majority of these are identified by genome-wide association studies (GWAS) using large case-control cohorts of European ancestry. More than 80% of the heritability of T1D can be explained by GWAS data in this population group. However, with few exceptions, their individual contribution to T1D risk is low and understanding their function in disease biology remains a huge challenge. GWAS on its own does not inform us in detail on disease mechanisms, but the combination of GWAS data with other omics-data is beginning to advance our understanding of T1D etiology and pathogenesis. Current knowledge supports the notion that genetic variation in both pancreatic ? cells and in immune cells is central in mediating T1D risk. Advances, perspectives and limitations of GWAS are discussed in this review.

| S-EPMC5750451 | biostudies-literature

From disease association to risk assessment: an optimistic view from genome-wide association studies on type 1 diabetes.

Project description:Genome-wide association studies (GWAS) have been fruitful in identifying disease susceptibility loci for common and complex diseases. A remaining question is whether we can quantify individual disease risk based on genotype data, in order to facilitate personalized prevention and treatment for complex diseases. Previous studies have typically failed to achieve satisfactory performance, primarily due to the use of only a limited number of confirmed susceptibility loci. Here we propose that sophisticated machine-learning approaches with a large ensemble of markers may improve the performance of disease risk assessment. We applied a Support Vector Machine (SVM) algorithm on a GWAS dataset generated on the Affymetrix genotyping platform for type 1 diabetes (T1D) and optimized a risk assessment model with hundreds of markers. We subsequently tested this model on an independent Illumina-genotyped dataset with imputed genotypes (1,008 cases and 1,000 controls), as well as a separate Affymetrix-genotyped dataset (1,529 cases and 1,458 controls), resulting in area under ROC curve (AUC) of approximately 0.84 in both datasets. In contrast, poor performance was achieved when limited to dozens of known susceptibility loci in the SVM model or logistic regression model. Our study suggests that improved disease risk assessment can be achieved by using algorithms that take into account interactions between a large ensemble of markers. We are optimistic that genotype-based disease risk assessment may be feasible for diseases where a notable proportion of the risk has already been captured by SNP arrays.

| S-EPMC2748686 | biostudies-literature

Predicting type 2 diabetes based on polymorphisms from genome-wide association studies: a population-based study.

Project description:Prediction of type 2 diabetes based on genetic testing might improve identification of high-risk subjects. Genome-wide association (GWA) studies identified multiple new genetic variants that associate with type 2 diabetes. The predictive value of genetic testing for prediction of type 2 diabetes in the general population is unclear.We investigated 18 polymorphisms from recent GWA studies on type 2 diabetes in the Rotterdam Study, a prospective, population-based study among homogeneous Caucasian individuals of 55 years and older (genotyped subjects, n = 6,544; prevalent cases, n = 686; incident cases during follow-up, n = 601; mean follow-up 10.6 years). The predictive value of these polymorphisms was examined alone and in addition to clinical characteristics using logistic and Cox regression analyses. The discriminative accuracy of the prediction models was assessed by the area under the receiver operating characteristic curves (AUCs).Of the 18 polymorphisms, the ADAMTS9, CDKAL1, CDKN2A/B-rs1412829, FTO, IGF2BP2, JAZF1, SLC30A8, TCF7L2, and WFS1 variants were associated with type 2 diabetes risk in our population. The AUC was 0.60 (95% CI 0.57-0.63) for prediction based on the genetic polymorphisms; 0.66 (0.63-0.68) for age, sex, and BMI; and 0.68 (0.66-0.71) for the genetic polymorphisms and clinical characteristics combined.We showed that 9 of 18 well-established genetic risk variants were associated with type 2 diabetes in a population-based study. Combining genetic variants has low predictive value for future type 2 diabetes at a population-based level. The genetic polymorphisms only marginally improved the prediction of type 2 diabetes beyond clinical characteristics.

| S-EPMC2570410 | biostudies-literature

Genome-wide association studies in the Japanese population identify seven novel loci for type 2 diabetes.

Project description:Genome-wide association studies (GWAS) have identified more than 80 susceptibility loci for type 2 diabetes (T2D), but most of its heritability still remains to be elucidated. In this study, we conducted a meta-analysis of GWAS for T2D in the Japanese population. Combined data from discovery and subsequent validation analyses (23,399 T2D cases and 31,722 controls) identify 7 new loci with genome-wide significance (P<5 × 10(-8)), rs1116357 near CCDC85A, rs147538848 in FAM60A, rs1575972 near DMRTA1, rs9309245 near ASB3, rs67156297 near ATP8B2, rs7107784 near MIR4686 and rs67839313 near INAFM2. Of these, the association of 4 loci with T2D is replicated in multi-ethnic populations other than Japanese (up to 65,936 T2Ds and 158,030 controls, P<0.007). These results indicate that expansion of single ethnic GWAS is still useful to identify novel susceptibility loci to complex traits not only for ethnicity-specific loci but also for common loci across different ethnicities.

| S-EPMC4738362 | biostudies-literature

Persistent homology analysis of type 2 diabetes genome-wide association studies in protein-protein interaction networks.

Project description:Genome-wide association studies (GWAS) involving increasing sample sizes have identified hundreds of genetic variants associated with complex diseases, such as type 2 diabetes (T2D); however, it is unclear how GWAS hits form unique topological structures in protein-protein interaction (PPI) networks. Using persistent homology, this study explores the evolution and persistence of the topological features of T2D GWAS hits in the PPI network with increasing p-value thresholds. We define an n-dimensional persistent disease module as a higher-order generalization of the largest connected component (LCC). The 0-dimensional persistent T2D disease module is the LCC of the T2D GWAS hits, which is significantly detected in the PPI network (196 nodes and 235 edges, P<0.05). In the 1-dimensional homology group analysis, all 18 1-dimensional holes (loops) of the T2D GWAS hits persist over all p-value thresholds. The 1-dimensional persistent T2D disease module comprising these 18 persistent 1-dimensional holes is significantly larger than that expected by chance (59 nodes and 83 edges, P<0.001), indicating a significant topological structure in the PPI network. Our computational topology framework potentially possesses broad applicability to other complex phenotypes in identifying topological features that play an important role in disease pathobiology.

| S-EPMC10562725 | biostudies-literature

Impact of type 2 diabetes variants identified through genome-wide association studies in early-onset type 2 diabetes from South Indian population.

Project description:The prevalence of early-onset type 2 diabetes (EOT2D) is increasing in Asian countries. Genome-wide association studies performed in European and various other populations have identified associations of numerous variants with type 2 diabetes in adults. However, the genetic component of EOT2D which is still unexplored could have similarities with late-onset type 2 diabetes. Here in the present study we aim to identify the association of variants with EOT2D in South Indian population. Twenty-five variants from 18 gene loci were genotyped in 1,188 EOT2D and 1,183 normal glucose tolerant subjects using the MassARRAY technology. We confirm the association of the HHEX variant rs1111875 with EOT2D in this South Indian population and also the association of CDKN2A/2B (rs7020996) and TCF7L2 (rs4506565) with EOT2D. Logistic regression analyses of the TCF7L2 variant rs4506565(A/T), showed that the heterozygous and homozygous carriers for allele 'T' have odds ratios of 1.47 (95% confidence interval [CI], 1.17 to 1.83; p = 0.001) and 1.65 (95% CI, 1.18 to 2.28; p = 0.006) respectively, relative to AA homozygote. For the HHEX variant rs1111875 (T/C), heterozygous and homozygous carriers for allele 'C' have odds ratios of 1.13 (95% CI, 0.91 to 1.42; p = 0.27) and 1.58 (95% CI, 1.17 to 2.12; p = 0.003) respectively, relative to the TT homozygote. For CDKN2A/2B variant rs7020996, the heterozygous and homozygous carriers of allele 'C' were protective with odds ratios of 0.65 (95% CI, 0.51 to 0.83; p = 0.0004) and 0.62 (95% CI, 0.27 to 1.39; p = 0.24) respectively, relative to TT homozygote. This is the first study to report on the association of HHEX variant rs1111875 with EOT2D in this population.

| S-EPMC7560451 | biostudies-literature

Validation of Type 2 Diabetes Risk Variants Identified by Genome-Wide Association Studies in Northern Han Chinese.

Project description:BackgroundMore than 60 genetic susceptibility loci associated with type 2 diabetes mellitus (T2DM) have been established in populations of Asian and European ancestry. Given ethnic differences and environmental factors, validation of the effects of genetic risk variants with reported associations identified by Genome-Wide Association Studies (GWASs) is essential. The study aims at evaluating the associations of T2DM with 29 single nucleotide polymorphisms (SNPs) from 19 candidate genes derived from GWASs in a northern Han Chinese population.MethodIn this case-control study, 461 T2DM-diagnosed patients and 434 controls were recruited at the Jidong oil field hospital (Hebei, China) from January 2009 to October 2013. A cumulative genetic risk score (cGRS) was calculated by summation of the number of risk alleles, and a weight GRS (wGRS) was calculated as the sum of risk alleles at each locus multiplied by their effect sizes for T2DM, using the independent variants selected.ResultThe allelic frequency of the "A" allele at rs17106184 (Fas-associated factor 1, FAF1) was significantly higher in the T2DM patients than that of the healthy controls (11.7% vs. 6.4%, p < 0.001). Individuals in the highestquartile of wGRS had an over three-fold increased risk for developing T2DM compared with those in the lowest quartile (odds ratio = 3.06, 95% CI = 1.92-4.88, p < 0.001) adjusted for age, sex, BMI, total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), systolic blood pressure (SBP) and diastolic blood pressure (DBP). The results were similar when analyzed with the cGRS.ConclusionsWe confirmed the association between rs17106184 (FAF1) and T2DM in a northern Han Chinese population. The GRS calculated based on T2DM susceptibility variants may be a useful tool for predicting the T2DM susceptibility.

| S-EPMC5036696 | biostudies-literature

Genetic susceptibility to type 2 diabetes and obesity: follow-up of findings from genome-wide association studies.

Project description:Elucidating the underlying genetic variations influencing various complex diseases is one of the major challenges currently facing clinical genetic research. Although these variations are often difficult to uncover, approaches such as genome-wide association studies (GWASs) have been successful at finding statistically significant associations between specific genomic loci and disease susceptibility. GWAS has been especially successful in elucidating genetic variants that influence type 2 diabetes (T2D) and obesity/body mass index (BMI). Specifically, several GWASs have confirmed that a variant in transcription factor 7-like 2 (TCF7L2) confers risk for T2D, while a variant in fat mass and obesity-associated protein (FTO) confers risk for obesity/BMI; indeed both of these signals are considered the most statistically associated loci discovered for these respective traits to date. The discovery of these two key loci in this context has been invaluable for providing novel insight into mechanisms of heritability and disease pathogenesis. As follow-up studies of TCF7L2 and FTO have typically lead the way in how to follow up a GWAS discovery, we outline what has been learned from such investigations and how they have implications for the myriad of other loci that have been subsequently reported in this disease context.

| S-EPMC3955626 | biostudies-literature

Genome-wide association study of type 2 diabetes in Africa.

Project description:Aims/hypothesisGenome-wide association studies (GWAS) for type 2 diabetes have uncovered >400 risk loci, primarily in populations of European and Asian ancestry. Here, we aimed to discover additional type 2 diabetes risk loci (including African-specific variants) and fine-map association signals by performing genetic analysis in African populations.MethodsWe conducted two type 2 diabetes genome-wide association studies in 4347 Africans from South Africa, Nigeria, Ghana and Kenya and meta-analysed both studies together. Likely causal variants were identified using fine-mapping approaches.ResultsThe most significantly associated variants mapped to the widely replicated type 2 diabetes risk locus near TCF7L2 (p = 5.3 × 10-13). Fine-mapping of the TCF7L2 locus suggested one type 2 diabetes association signal shared between Europeans and Africans (indexed by rs7903146) and a distinct African-specific signal (indexed by rs17746147). We also detected one novel signal, rs73284431, near AGMO (p = 5.2 × 10-9, minor allele frequency [MAF] = 0.095; monomorphic in most non-African populations), distinct from previously reported signals in the region. In analyses focused on 100 published type 2 diabetes risk loci, we identified 21 with shared causal variants in African and non-African populations.Conclusions/interpretationThese results demonstrate the value of performing GWAS in Africans, provide a resource to larger consortia for further discovery and fine-mapping and indicate that additional large-scale efforts in Africa are warranted to gain further insight in to the genetic architecture of type 2 diabetes.

| S-EPMC6560001 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data