Expression and proteomic characterization of risk loci in type 1 diabetes

描述（由申请人提供）：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图谱项目，这些项目通常超出了各个实验室的范围。 T1DGC 最近完成了 T1D 的全基因组关联扫描 (GWAS)，并将荟萃分析结果与之前发表的两项研究相结合，确定了 41 个 P < 10-6 的不同基因组位置。在排除已经确定的与 T1D 相关的区域后，对剩余位点中最重要的 SNP 在一组独立的 T1D 病例、对照和家庭中的复制进行了测试。其中 18 个重复的 P <0.01，并在 GWAS 和重复数据的联合分析中提供了全基因组显着性 (P d 5 x 10-8)。总体而言，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。基于对这些 T1D 相关区域中最强相关 SNP 周围连锁不平衡衰减的考虑，一个区域中基因的中位数为 4 个，但范围为 0 到 27 个，这对识别相关基因和风险变异提出了重大挑战。高密度 SNP 作图和重测序有望进一步细化这些区间，但欧洲血统以外的个体中 T1D 的发病率较低，可能会限制遗传作图方法识别风险变异的能力。即使识别出风险变异，仅靠精细绘图也无法深入了解其可能的作用机制。在 T1DGC 的这项应用中，我们提出了一种精细绘图的补充方法，这是一种综合生物学方法，我们将根据其对中间表型的影响来识别不同区域内一组有限的潜在致病变异。该方法包括蛋白质组学，以识别可能与疾病相关的特定基因或通路有关的蛋白质-蛋白质相互作用网络，以及转录本分析，以识别顺式（即影响该区域附近的基因）或反式（影响已知涉及疾病病因或相关区域的另一个基因）对 mRNA 水平的等位基因影响。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，但在人类中的应用却不太广泛。在本申请中，我们建议应用这些互补方法来识别目前与 T1D 发病机制有关的基因组区域内的 SNP，这些 SNP 与转录组或蛋白质组的功能影响相关。我们的假设是，表征 SNP 对基因表达或蛋白质功能或相互作用的表型影响将为识别这些区域中的风险变异提供比单独作图更有效的方法，并将提供对这些变异改变疾病风险的可能机制的见解。 公共卫生相关性： 当胰腺中的胰岛素分泌细胞因不明原因的自身免疫过程而耗尽时，就会出现 1 型糖尿病 (T1D)。虽然胰岛素治疗 T1D 可以挽救生命，但通过更好地了解潜在的疾病机制，特别是在延长的临床前期间发生的事件，可以促进有效预防疗法的开发。本申请中提出的研究将描述新发现的 T1D 遗传风险位点的特征，这些位点可以作为预测疾病的有用生物标志物或作为治疗靶标。

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.