Expression and proteomic characterization of risk loci in type 1 diabetes

1 型糖尿病风险位点的表达和蛋白质组学特征

• 批准号: 7797933
• 负责人: Stephen S. Rich
• 金额: $ 661.86万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-25 至 2014-06-25
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7797933
• 关键词: Affect; Animal Model; Autoimmune Process; Beta Cell; Biological Markers; Biology; Chromosome Mapping; Data; Development; Disease; Etiology; European; Event; Family; Gene Expression; Gene Expression Profile; Gene Expression Regulation; Genes; Genetic; Genetic Risk; Genomics; Human; Incidence; Individual; Insulin; Insulin-Dependent Diabetes Mellitus; Joints; Laboratories; Life; Linkage Disequilibrium; Location; Maps; Messenger RNA; Meta-Analysis; Pancreas; Pathogenesis; Pathway interactions; Phenotype; Preventive; Proteome; Proteomics; Publishing; Research Design; Risk; Single Nucleotide Polymorphism Map; Site; System; Testing; Transcript; Variant; base; case control; density; diabetes mellitus genetics; diabetes risk; disorder risk; genome wide association study; genome-wide; insight; pre-clinical; protein function; protein protein interaction; public health relevance; tool

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.

描述（由申请人提供）：1型糖尿病遗传学联盟（T1 DGC）（www.t1dgc.org）的成立是为了促进针对1型糖尿病（T1 D）的大规模多学科基因图谱项目的完成，这些项目通常超出了单个实验室的范围。T1 DGC最近完成了T1 D的全基因组关联扫描（GWAS），并将荟萃分析的结果与先前发表的两项研究相结合，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T1 D相关的区域后，在一组独立的T1 D病例、对照和家族中测试剩余位点中最显著的SNP的复制。在GWAS和复制数据的联合分析中，其中18个重复P <0.01，并提供全基因组显著性（P d 5 x 10-8）。总的来说，新的T1 DGC全基因组关联研究为40多个经证实的非HLA T1 D风险位点提供了证据。基于对这些T1 D相关区域中最强相关SNP周围连锁不平衡衰减的考虑，区域中基因的中位数为4，但范围为0至27，这对鉴定相关基因和风险变体提出了重大挑战。高密度SNP定位和重新测序有望进一步细化这些区间，但欧洲血统以外的个体中T1 D的低发病率可能会限制遗传定位方法识别风险变体的能力。即使确定了风险变量，仅靠精细绘图也无法深入了解其可能的作用机制。在T1 DGC的这一应用中，我们提出了一种精细定位的补充方法，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，在不同区域内识别一组有限的潜在致病变体。该方法包括蛋白质组学，以鉴定蛋白质-蛋白质相互作用网络，其可能合理地暗示特定基因或途径与疾病相关，以及转录本分析，以鉴定顺式（即，影响该区域中的邻近基因）或反式（影响已知参与疾病病因学的另一个基因，或影响相关区域中的另一个基因）。这种定义基因表达调控的“系统遗传学”方法已被证明是在动物模型中进行基因鉴定的有效工具，但在人类中的应用有些不太广泛。在本申请中，我们建议应用这些互补的方法来鉴定目前与T1 D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效应有关。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效应的表征将提供一种比单独作图更有效的方法来识别这些区域中的风险变体，并将提供对这些变体改变疾病风险的可能机制的见解。 公共卫生相关性： 1型糖尿病（T1 D）是由于胰腺中的胰岛素分泌细胞被未知原因的自身免疫过程耗尽而发生的。虽然胰岛素治疗T1 D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阶段发生的事件，可以促进有效预防治疗的开发。本申请中的拟议研究将表征新发现的T1 D遗传风险基因座，其可作为预测疾病的有用生物标志物或作为治疗靶点。