The role of copy number variants (CNV) in type 1 diabetes

描述(申请人提供)：1型糖尿病(T1D)是一种复杂的自身免疫性疾病，由多种遗传和环境风险因素的作用引起。1型糖尿病遗传学联合会(T1DGC)成立于2001年，目的是收集进行大规模T1D基因研究所需的资源。T1DGC最近报告了对2,496个多重T1D家系进行全基因组连锁扫描的结果，其中包含2,658个受影响的同胞对(ASP)，其中包含超过6,000个SNP的信息，以及对7,698例患者和9,068名对照中超过800,000个SNP的全基因组关联扫描(GWAS)荟萃分析的结果。从连锁扫描中获得了支持T1D易感基因的证据，分别位于6p21.3(HLA)、6q、2q32.3(CTLA4)、11p15.5(INS)和19号染色体上的两个新区域。在4,267例患者、4,463名对照和2,319个患病同胞对(ASP)家系中，从GWAS中发现了27个新区域，其中18个区域以P&lt；0.01重复。在这些样本中名义上复制了另外四个区域。T1DGC一直专注于基因组中可能改变T1D风险的单核苷酸变化的作用；然而，越来越明显的是，拷贝数变化(CNV)导致了许多人类疾病的风险，包括自闭症、精神分裂症、骨质疏松症、早期心肌梗死和克罗恩病。尽管CNV显然是人类遗传变异的一个重要来源，但还没有文献评估CNV对T1D风险的贡献。这个DP3应用程序建议在2,496个ASP家族的T1DGC集合中表征CNV在T1D风险中的作用，并在T1D ASP、三个家族、病例和对照的单独集合中复制这些发现。T1DGC建议对CNV进行全基因组分析，这将：(A)使用Agilent 8x60K CNV基因分型芯片对2,496个T1DGC ASP家族(T1D家族性病例)中的常见CNV进行全基因组分型，并进行统计遗传分析，以确定与T1D关联最强的CNV；(B)使用特定于基因座的分析，在其他T1D ASP家族、三联体和病例/对照中复制上述假定的关联；(C)使用来自T1DGC Gwas数据的电子数据挖掘方法对罕见的大型CNV进行关联测试；以及(D)使用使用1M安捷伦CNV芯片的384个T1DGC GWAS样本的子集，识别从挖掘T1DGC Gwas数据中无法识别的罕见LOF CNV的比例。我们的假设是，CNV与T1D的遗传风险有关。此外，CNV的一个子集可能驻留在T1DGC GWASMeta分析已经确定的区域中，因此增加了候选基因可能具有特定调节功能的可能性，从而识别潜在的治疗靶点。重要的是以整个基因组为靶点，识别潜在的受CNV影响的T1D风险基因座。 公共卫生相关性： 1型糖尿病(T1D)是一种复杂的自身免疫性疾病，由多种遗传和环境风险因素引起，通过并发症(眼、肾、心脏、神经)及其相关的发病率和死亡率对美国公众健康造成重大负担。这项研究建议扫描人类基因组，以确定与T1D风险相关的结构变量(拷贝数变量，CNV)。识别T1D的遗传危险因素是风险预测、干预和开发疾病治疗(预防)的第一步。

DESCRIPTION (provided by applicant): Type 1 diabetes (T1D) is a complex autoimmune disorder that arises from the action of multiple genetic and environmental risk factors. The Type 1 Diabetes Genetics Consortium (T1DGC) was established in 2001 to assemble the resources necessary for conducting large-scale genetic studies of T1D. The T1DGC recently reported the findings of a genome-wide linkage scan in 2,496 multiplex T1D families containing 2,658 affected sib-pairs (ASPs) with information on over 6,000 SNPs, and a genome-wide association scan (GWAS) meta- analysis of over 800,000 SNPs in 7,698 cases and 9,068 controls. From the linkage scan, evidence was obtained supporting T1D susceptibility genes in 6p21.3 (HLA), 6q, 2q32.3 (CTLA4), 11p15.5 (INS) and two novel regions on chromosome 19. From the GWAS, 27 novel regions were identified and 18 replicated with P < 0.01 in an independent set of 4,267 cases, 4,463 controls and 2,319 affected sib-pair (ASP) families. Four additional regions were nominally replicated in these samples. The T1DGC has focused on the role of single nucleotide changes in the genome that may modify risk of T1D; however, it is becoming increasingly apparent that copy-number variation (CNV) contributes to the risk for a number of human diseases, including autism, schizophrenia, osteoporosis, early myocardial infarction, and Crohn's disease. Although CNV is clearly an important source of human genetic variation, there have been no publications evaluating the contribution of CNVs to T1D risk. This DP3 application proposes to characterize the role of CNVs in T1D risk in the T1DGC collection of 2,496 ASP families and replicate the findings in a separate collection of T1D ASPs, trio families, cases and controls. The T1DGC proposes a genome-wide analysis of CNVs that will (a) perform genome-wide typing of common CNVs in 2,496 T1DGC ASP families (familial cases of T1D) using the Agilent 8x60K CNV genotyping chip and conduct statistical genetic analysis to identify the most strongly associated CNVs with T1D; (b) replicate the putative associations above in additional T1D ASP families, trios and cases/controls using locus-specific assays; (c) conduct association testing of rare, large CNVs using an in silico data mining approach from T1DGC GWAS data; and (d) identify the proportion of rare LOF CNVs not identifiable from mining T1DGC GWAS data using a subset of 384 T1DGC GWAS samples using a 1M Agilent CNV chip. Our hypothesis is that CNVs contribute to the genetic risk for T1D. Further, a subset of the CNVs may reside in regions already identified by the T1DGC GWAS meta-analysis and, therefore, increase the likelihood that the candidate gene may have a specific function in regulation, thereby identifying potential therapeutic targets. It is important to target the entire genome to identify potential CNV-influenced T1D risk loci. PUBLIC HEALTH RELEVANCE: Type 1 diabetes (T1D) is a complex autoimmune disorder that arises from the action of multiple genetic and environmental risk factors with significant burden to the public health of the United States through the complications (eye, kidney, heart, nerves) and its associated morbidity and mortality. This research proposes to scan the human genome in order to identify structural variants (copy number variants, CNVs) are associated with risk of T1D. Identification of genetic risk factors for T1D is the first step in risk prediction, intervention and developing disease therapeutics (prevention).