Expression and proteomic characterization of risk loci in type 1 diabetes

1型糖尿病遗传学联盟（T1DGC） （www.t1dgc.org）的成立是为了促进完成针对1型糖尿病（T1D）的大规模多学科基因定位项目，这些项目通常超出了单个实验室的范围。T1DGC最近完成了T1D的全基因组关联扫描（GWAS），并将结果与先前发表的两项研究结合起来进行荟萃分析，确定了41个不同的基因组位置，P < 10-6。在排除已经建立的与T1D相关的区域后，对剩余位点中最显著的snp在一组独立的T1D病例、对照和家族中进行了复制测试。其中18个在GWAS和复制数据的联合分析中具有P <0.01的全基因组显著性（P d 5 × 10-8）。总的来说，新的T1DGC全基因组关联研究为40多个已确认的非HLA T1D风险位点提供了证据。考虑到这些T1D相关区域中最强烈相关SNPs周围的连锁不平衡衰减，一个区域的基因中位数为4，但范围从0到27，这对识别相关基因和风险变异提出了重大挑战。高密度SNP定位和重测序有望进一步完善这些间隔，但T1D在非欧洲血统个体中的低发病率可能会限制遗传定位方法识别风险变异的能力。即使确定了风险变量，单独的精细映射也不能提供对其可能的作用机制的见解。在T1DGC的这一应用中，我们提出了一种互补的精细制图方法，一种综合生物学方法，我们将根据它们对中间表型的影响，在不同区域内确定一组有限的潜在致病变异。该方法包括蛋白质组学，以确定可能暗示与疾病相关的特定基因或途径的蛋白质-蛋白质相互作用网络，以及转录分析，以确定顺式（即影响该区域附近的基因）或反式（影响已知参与疾病病因学或相关区域的另一个基因）对mRNA水平的等位基因效应。这种定义基因表达调控的“系统遗传学”方法已被证明是动物模型中基因鉴定的有效工具，但在人类中应用较少。在这项应用中，我们建议应用这些互补的方法来鉴定目前与T1D发病机制有关的基因组区域内的snp，这些区域与转录组或蛋白质组的功能影响有关。我们的假设是，snp对基因表达或蛋白质功能或相互作用的表型影响的表征将提供一种比单独定位更有效的方法来识别这些区域的风险变异，并将提供对这些变异改变疾病风险的可能机制的见解。

DESCRIPTION (provided by applicant): The Type 1 Diabetes Genetics Consortium (T1DGC) (www.t1dgc.org) was established to facilitate the completion of large-scale multi-disciplinary gene mapping projects targeting type 1 diabetes (T1D) that are generally beyond the scope of individual laboratories. The T1DGC has recently completed a genome-wide association scan (GWAS) of T1D, and combined the results in a meta-analysis with two previously published studies, identifying 41 distinct genomic locations with P < 10-6. After excluding already established regions of association to T1D, the most significant SNPs in the remaining sites were tested for replication in an independent set of T1D cases, controls and families. Eighteen of these replicated with P <0.01 and provided genome-wide significance (P d 5 x 10-8) in a joint analysis of GWAS and replication data. Overall, the new T1DGC genome-wide association study of T1D provides evidence for more than 40 confirmed non- HLA T1D risk loci. Based on consideration of the decay of linkage disequilibrium around the most strongly associated SNPs in these T1D associated regions, the median number of genes in a region is 4 but ranges from 0 to 27 presenting a significant challenge for identifying the relevant genes and risk variants. High density SNP mapping and re-sequencing holds promise for further refining these intervals, but the low incidence of T1D in individuals of other than European ancestry may limit the power of genetic mapping approaches to identify risk variants. Even if risk variants are identified, fine mapping alone cannot provide insights into their possible mechanisms of action. In this application from the T1DGC, we propose a complementary approach to fine mapping, an integrative biology approach in which we will identify a limited set of potential causative variants within different regions based upon their effect on intermediate phenotypes. The approach includes proteomics to identify protein-protein interaction networks that might plausibly implicate a particular gene or pathway with disease relevance and transcript analysis to identify to identify allelic effects on mRNA levels either in cis (i.e., affecting a nearby gene in the region) or in trans (affecting another gene either already known to be involved in disease etiology, or in an associated region). Such "systems genetics" approaches to define regulation of gene expression have proven to be an effective tool for gene identification in animal models but have been somewhat less extensively applied in humans. In this application, we propose to apply these complementary approaches to identify SNPs within the genomic regions currently implicated in T1D pathogenesis that are associated with functional effects on the transcriptome or proteome. Our hypothesis is that characterization of the phenotypic effects of SNPs on gene expression or on protein function or interaction will provide a more efficient approach to the identification of risk variants in these regions than by mapping alone and will provide insights into possible mechanisms whereby these variants modify disease risk. PUBLIC HEALTH RELEVANCE: Type 1 diabetes (T1D) develops when the insulin-secreting cells in the pancreas are depleted by an autoimmune process of unknown origin. While insulin treatment for T1D is life-saving, development of effective preventive therapies could be enhanced by a better understanding of the underlying disease mechanism, particularly events occurring during the extended pre-clinical period. The proposed studies in this application will characterize newly discovered genetic risk loci for T1D which may serve as useful biomarkers for prediction of disease or as targets for therapy.