Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes

Abstract

Type 1 diabetes (T1D) is a common autoimmune disorder that arises from the action of multiple genetic and environmental risk factors. We report the findings of a new genome-wide association study of T1D, combined in a meta-analysis with two previously published studies. The total sample set included 7,514 cases and 9,045 reference samples. Forty-one distinct genomic locations provided evidence for association to T1D in the meta-analysis (P < 10-6). After excluding previously reported associations, 27 regions were further tested in an independent set of 4,267 cases, 4,463 controls and 2,319 affected sib-pair (ASP) families. Of these, 18 regions were replicated (P < 0.01; overall P < 5 × 10-8) and four additional regions provided nominal evidence of replication (P < 0.05). The many new candidate genes suggested by these results include IL10, IL19, IL20, GLIS3, CD69 and IL27.

A unified approach for allele frequency estimation, SNP detection and association studies based on pooled sequencing data using EM algorithms

“…This SNP was not reported to be associated with any phenotypes according to the catalog of GWAS studies (http://www.genome.gov/gwastudies/) to date. Nevertheless, other SNPs within the IL2RA gene were found to be associated with T1D by Cooper et al [26] before the publication of [8] and by two other studies [27,28] after the publication of [8] using different approaches. Barrett et al [27] used genome-wide association studies giving a p-value of 1.0e-13 while Huang et al [28] used imputation of the genotypes based on the 1000 genome projects yielding a p-value of 5e-9.…”

The impact of genome‐wide association studies on the pathophysiology and therapy of cardiovascular disease

“…Besides the platelet phenotypes (Soranzo et al , 2009; Takizawa et al , 2010) and the also known association with blood pressure (Levy et al , 2009; Newton‐Cheh et al , 2009), SH2B3 seems to mainly influence inflammatory processes. From a genetic point of view, this is already noticeable from the associations of the locus with different inflammatory diseases, for example coeliac disease (Hunt et al , 2008), type 1 diabetes (Barrett et al , 2009; Concannon et al , 2009) and high eosinophil numbers (Gudbjartsson et al , 2009). An experimental study revealed that Sh2b3/Lnk influences the activation of dendritic cells, thereby modulating the immune response, that is dendritic cells from Lnk −/− mice are hyper‐responsive to IL‐15, resulting in an excessive production of IFN‐γ (Mori et al , 2014).…”

A strategy for combining minor genetic susceptibility genes to improve prediction of disease in type 1 diabetes

“…INS gene polymorphisms are also associated with type 1 diabetes risk and have hazard ratios that exceed those of the 12 genes examined in the current study. 2,4,11 We therefore added the INS genotype to the 12 SNPs and performed ROC analysis on the 8191 possible combinations. Remarkably, 1117 combinations had an AUC with Po0.0001 and 117 combinations had an AUC with Po10 À 5 (Supplementary Figure 3).…”

“…1,2 With the exception of the human leukocyte antigen (HLA) and the INS gene regions, the contribution of any single locus to type 1 diabetes susceptibility is relatively small. [3][4][5] Therefore, although informative with respect to disease mechanisms and drug targets, identification of these susceptibility genes has provided relatively little improvement in disease prediction.…”

“…[3][4][5] Therefore, although informative with respect to disease mechanisms and drug targets, identification of these susceptibility genes has provided relatively little improvement in disease prediction. [1][2][3][4][5] Moreover, it appears that there may be differences between cohorts in how much single gene regions contribute to risk and prediction. [6][7][8][9] It is conceivable that although contributions of single genes may be small and inconsistent, the sum of susceptible alleles from multiple gene regions could provide a more universal risk estimate and provide sufficient risk so as to be helpful for risk stratification.…”