Understanding type 1 diabetes through genetics: Advances and prospects

Departments of Pediatrics and Human Genetics, McGill University, Montreal, Québec, Canada H3H 1P3. Constantin.
Nature Reviews Genetics (Impact Factor: 36.98). 11/2011; 12(11):781-92. DOI: 10.1038/nrg3069
Source: PubMed

ABSTRACT Starting with early crucial discoveries of the role of the major histocompatibility complex, genetic studies have long had a role in understanding the biology of type 1 diabetes (T1D), which is one of the most heritable common diseases. Recent genome-wide association studies (GWASs) have given us a clearer picture of the allelic architecture of genetic susceptibility to T1D. Fine mapping and functional studies are gradually revealing the complex mechanisms whereby immune self-tolerance is lost, involving multiple aspects of adaptive immunity. The triggering of these events by dysregulation of the innate immune system has also been implicated by genetic evidence. Finally, genetic prediction of T1D risk is showing promise of use for preventive strategies.

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    • "Simulations involved estimating h2 for subsets of cases and controls from a Type I Diabetes (T1D) dataset. We choose to use a T1D because the histocompatibility complex (MHC) region on chromosome 6 has major genetic contribution to risks of both DILI2 and T1D1118. Specifically, we used the Welcome Trust Case Control Consortium (WTCCC) T1D dataset (1963 cases and 2938 controls)18. "
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    ABSTRACT: Recent genome-wide association studies identified certain human leukocyote antigen (HLA) alleles as the major risk factors of drug-induced liver injuries (DILI). While these alleles often cause large relative risk, their predictive values are quite low due to low prevalence of idiosyncratic DILI. Finding additional risk factors is important for precision medicine. However, optimal design of further genetic studies is hindered by uncertain overall heritability of DILI. This is a common problem for low-prevalence pharmacological traits, since it is difficult to obtain clinical outcome data in families. Here we estimated the heritability (h(2)) of DILI from case-control genome-wide single nucleotide polymorphism data using a method based on random effect models. We estimated the proportion of h(2) captured by common SNPs for DILI to be between 0.3 and 0.5. For co-amoxiclav induced DILI, chromosome 6 explained part of the heritability, indicating additional contributions from common variants yet to be found. We performed simulations to assess the robustness of the h(2) estimate with limited sample size under low prevelance, a condition typical to studies on idiosyncratic pharmacological traits. Our findings suggest that common variants outside of HLA contribute to DILI susceptability; therefore, it is valuable to conduct further GWAS with expanded case collection.
    Scientific Reports 07/2014; 4:5762. DOI:10.1038/srep05762 · 5.58 Impact Factor
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    • "DN is a common complication of type 1 and type 2 diabetes , which have been associated with very distinct disease risk loci [Figure 2 and reviewed in Ntzani and Kavvoura (2012), Polychronakos and Li (2011)]. Results of genetics studies are extensively discussed in two recent reviews (Gu and Brismar, 2012; Palmer and Freedman, 2012), we therefore will focus only on the genetic association of ELMO1, CNDP1, and FRDM3 loci with DN risk, as they were detected in both GWAS and candidate gene approach studies. "
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    ABSTRACT: Genetic studies of human diseases have identified multiple genetic risk loci for various fibrotic diseases. This has provided insights into the myriad of biological pathways potentially involved in disease pathogenesis. These discoveries suggest that alterations in immune responses, barrier function, metabolism and telomerase activity may be implicated in the genetic risks for fibrotic diseases. In addition to genetic disease-risks, the identification of genetic disease-modifiers associated with disease complications, severity or prognosis provides crucial insights into the biological processes implicated in disease progression. Understanding the biological processes driving disease progression may be critical to delineate more effective strategies for therapeutic interventions. This review provides an overview of current knowledge and gaps regarding genetic disease-risks and genetic disease-modifiers in human fibrotic diseases.
    Frontiers in Pharmacology 12/2013; 4:159. DOI:10.3389/fphar.2013.00159 · 3.80 Impact Factor
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    • "Type 1 diabetes (T1D) is a T cell-mediated autoimmune disease that afflicts a million persons in the USA [1, 2]. It is a polygenic disorder resulting from the interaction of multiple gene variants [3] and environmental factors [4]. No approved methods are currently available for its prevention or reversal [5]. "
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    ABSTRACT: Type 1 diabetes (T1D) is a T cell-mediated disease. It is strongly associated with susceptibility haplotypes within the major histocompatibility complex, but this association accounts for an estimated 50% of susceptibility. Other studies have identified as many as 50 additional susceptibility loci, but the effect of most is very modest (odds ratio (OR) <1.5). What accounts for the "missing heritability" is unknown and is often attributed to environmental factors. Here we review new data on the cognate ligand of MHC molecules, the T cell receptor (TCR). In rats, we found that one allele of a TCR variable gene, V β 13A, is strongly associated with T1D (OR >5) and that deletion of V β 13+ T cells prevents diabetes. A role for the TCR is also suspected in NOD mice, but TCR regions have not been associated with human T1D. To investigate this disparity, we tested the hypothesis in silico that previous studies of human T1D genetics were underpowered to detect MHC-contingent TCR susceptibility. We show that stratifying by MHC markedly increases statistical power to detect potential TCR susceptibility alleles. We suggest that the TCR regions are viable candidates for T1D susceptibility genes, could account for "missing heritability," and could be targets for prevention.
    Journal of Diabetes Research 03/2013; 2013:737485. DOI:10.1155/2013/737485 · 2.16 Impact Factor
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