Genome-wide association studies in type 1 diabetes

2009 ◽  
Vol 9 (2) ◽  
pp. 157-163 ◽  
Author(s):  
Struan F.A. Grant ◽  
Hakon Hakonarson
Keyword(s):  
Type 1 Diabetes ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Meta-genome-wide association studies identify a locus on chromosome 1 and multiple variants in the MHC region for serum C-peptide in type 1 diabetes

Diabetologia ◽  
2018 ◽  
Vol 61 (5) ◽  
pp. 1098-1111 ◽  
Author(s):  
Delnaz Roshandel ◽  
◽  
Rose Gubitosi-Klug ◽  
Shelley B. Bull ◽  
Angelo J. Canty ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Association Studies ◽  
Genome Wide Association ◽  
Chromosome 1 ◽  
Genome Wide Association Studies ◽  
C Peptide ◽  
Genome Wide ◽  
Multiple Variants

scholarly journals Integrative Pathway-Based Approach for Genome-Wide Association Studies: Identification of New Pathways for Rheumatoid Arthritis and Type 1 Diabetes

PLoS ONE ◽  
2013 ◽  
Vol 8 (10) ◽  
pp. e78577 ◽  
Author(s):  
Finja Büchel ◽  
Florian Mittag ◽  
Clemens Wrzodek ◽  
Andreas Zell ◽  
Thomas Gasser ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Type 1 Diabetes ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals From Disease Association to Risk Assessment: An Optimistic View from Genome-Wide Association Studies on Type 1 Diabetes

PLoS Genetics ◽  
2009 ◽  
Vol 5 (10) ◽  
pp. e1000678 ◽  
Author(s):  
Zhi Wei ◽  
Kai Wang ◽  
Hui-Qi Qu ◽  
Haitao Zhang ◽  
Jonathan Bradfield ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Type 1 Diabetes ◽  
Association Studies ◽  
Disease Association ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals New Genes and Emerging Mechanisms of Type 1 Diabetes

2021 ◽  
Vol 2 (2) ◽  
pp. 47-51
Author(s):  
Aysha Karim Kiani ◽  
Asima Zia ◽  
Parveen Akhtar ◽  
Sadaf Moeez ◽  
Attya Bhatti ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Diabetes Susceptibility ◽  
Multiple Loci ◽  
New Genes ◽  
Genome Wide ◽  
Gwa Studies

Type 1 Diabetes susceptibility depends upon the complex interaction between numerous genetic as well as environmental factors. 50% of the familial clustering of T1D is explained by HLA locus alleles. Other multiple loci contribute the rest of the susceptibility, in which very little were known since last few years. Four novel loci were found from the results of stage-I, genome wide association (GWA) studies which were carried out with high-density genotyping arrays. As the stage-II of the Genome Wide Association studies completed, hopefully, most of the genetic reasons of Type 1 Diabetes will be identified. 

scholarly journals Bootstrap Distillation: Non-parametric Internal Validation of GWAS Results by Subgroup Resampling

2017 ◽  
Author(s):  
David A. Eccles ◽  
Rodney A. Lea ◽  
Geoffrey K. Chambers
Keyword(s):  
Type 1 Diabetes ◽  
Association Studies ◽  
Sampling Technique ◽  
Genome Wide Association ◽  
Frequency Variation ◽  
Genome Wide Association Studies ◽  
Internal Validation ◽  
Genotype Frequencies ◽  
Genome Wide

AbstractGenome-wide Association Studies are carried out on a large number of genetic variants in a large number of people, allowing the detection of small genetic effects that are associated with a trait. Natural variation of genotypes within populations means that any particular sample from the population may not represent the true genotype frequencies within that population. This may lead to the observation of marker-disease associations when no such association exists.A bootstrap population sub-sampling technique can reduce the influence of allele frequency variation in producing false-positive results for particular samplings of the population. In order to utilise bioinformatics in the service of a serious disease, this sub-sampling method has been applied to the Type 1 Diabetes dataset from the Wellcome Trust Case Control Consortium in order to evaluate its effectiveness.While previous literature on Type 1 Diabetes has identified some DNA variants that are associated with the disease, these variants are not informative for distinguishing between disease cases and controls using genetic information alone (AUC=0.7284). Population sub-sampling filtered out noise from genome-wide association data, and increased the chance of finding useful associative signals. Subsequent filtering based on marker linkage and testing of marker sets of different sizes produced a 5-SNP signature set of markers for Type 1 Diabetes. The group-specific markers used in this set, primarily from the HLA region on chromosome 6, are considerably more informative than previously known associated variants for predicting T1D phenotype from genetic data (AUC=0.8395). Given this predictive quality, the signature set may be useful alone as a screening test, and would be particularly useful in combination with other clinical cofactors for Type 1 Diabetes risk.

scholarly journals Type 1 diabetes mellitus-associated genetic variants contribute to overlapping immune regulatory networks

2018 ◽  
Author(s):  
Denis M. Nyaga ◽  
Mark H. Vickers ◽  
Craig Jefferies ◽  
Jo K. Perry ◽  
Justin M. O’Sullivan
Keyword(s):  
Type 1 Diabetes ◽  
Regulatory Networks ◽  
Immune Cell ◽  
Association Studies ◽  
Pancreatic Beta Cell ◽  
Three Dimensional ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractType 1 diabetes (T1D) is a chronic metabolic disorder characterised by the autoimmune destruction of insulin-producing pancreatic islet beta cells in genetically predisposed individuals. Genome-wide association studies (GWAS) have identified over 60 risk loci across the human genome, marked by single nucleotide polymorphisms (SNPs), which confer genetic predisposition to T1D. There is increasing evidence that disease-associated SNPs can alter gene expression through spatial interactions that involve distal loci, in a tissue-and development-specific manner. Here, we used three-dimensional (3D) genome organization data to identify genes that physically co-localized with DNA regions that contained T1D-associated SNPs in the nucleus. Analysis of these SNP-gene pairs using the Genotype-Tissue Expression database identified a subset of SNPs that significantly affected gene expression. We identified 298 spatially regulated genes including HLA-DRB1, LAT, MICA, BTN3A2, CTLA4, CD226, NOTCH1, TRIM26, CLEC2B, TYK2, and FLRT3, which exhibit tissue-specific effects in multiple tissues. We observed that the T1D-associated variants interconnect through networks that form part of the immune regulatory pathways, including immune-cell activation, cytokine signalling, and programmed cell death protein-1 (PD-1). These pathways have been implicated in the pancreatic beta-cell inflammation and destruction as observed in T1D. Our results demonstrate that T1D-associated variants contribute to adaptive immune signalling, and immune-cell proliferation and activation through tissue and cell-type specific regulatory networks.Author SummaryAlthough genome-wide association studies have identified risk regions across the human genome that predispose individuals to the development of type 1 diabetes (T1D), the mechanisms through which these regions contribute to disease is unclear. Here, we used population-based genetic data from genome-wide association studies (GWAS) to understand how the three-dimensional (3D) organization of the DNA contributes to the differential expression of genes involved in immune system dysregulation as observed in T1D. We identified interconnected regulatory networks that affect immune pathways (adaptive immune signalling and immune-cell proliferation and activation) in a tissue and cell-type specific manner. Some of these pathways are implicated in the pancreatic beta-cell destruction. However, we observed other regulatory changes in tissues that are not typically considered to be central to the pathology of T1D, which represents a novel insight into the disease. Collectively, our data represent a novel resource for the hypothesis-driven development of diagnostic, prognostic and therapeutic interventions in T1D.

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