Rare Variants and Risk of Type 1 Diabetes

描述（由申请人提供）：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，可影响高达1 / 300的儿童。2001年，1型糖尿病遗传学联盟（T1DGC）成立，其目标是开展大规模的遗传研究，以确定基因组中导致1型糖尿病风险的区域，并将收集到的数据和生物标本提供给更广泛的科学界。T1DGC已经非常成功地利用(a)候选基因，(b) MHC变异，(c)全基因组连锁，(d)全基因组关联扫描（GWAS）进行了最强有力的T1D研究。从我们对超过16000例病例和对照组的GWAS荟萃分析中，18个新发现的位点具有全基因组显著性（P = 5 x 10-8），包括在4267例病例、4463例对照和2319个家庭的独立组中的复制证据。这使得T1D基因座总数达到42个。我们现在对新发现的18个基因座进行了后续的基因分型，并对所有已知的T1D基因座进行了密集的SNP定位（使用免疫芯片）。我们在大多数这些基因座中发现了一个单一的T1D候选基因。通过分析常见遗传变异发现的GWAS基因座解释了不到15%的T1D总遗传倾向性（在MHC基因贡献的50%之后）。剩余的遗传风险（“缺失的遗传能力”）可能是由于在基因编码区域发现的罕见变异。这些罕见的编码变体可能对风险有很大的影响。与常见变异不同，这些编码和调控区域的DNA测序需要发现罕见的功能变异。测序工作，甚至是编码区域（“外显子组”）的测序工作都花费巨大。为了检测T1D与罕见变异的关联，从12000多个人类外显子组中设计了一个基因分型阵列（ExomeChip）。这种定制阵列将允许以较低的成本通过对大量样本进行基因分型来检测罕见的变异。ExomeChip包含约200,000个非同义、无义和剪接位点变体，以及100,000个附加内容变体（包括MHC变体）。该资助申请的主要目的是通过以下方法发现导致T1D风险的新的遗传风险因素：1)在2500个T1DGC影响的兄弟姐妹对（ASP）家族的特征良好的集合中进行ExomeChip基因分型；2)对ExomeChip数据进行分析（包括单SNP和负担测试），以发现功能显著变异影响T1D风险的新基因；3)通过靶向测序在约7000例T1D病例和约7000例对照中复制这些新结果。我们将整合现有数据（HLA分型、免疫芯片、CNV、ExomeChip），以提供与T1D风险相关的罕见、常见和结构变异的综合分析。这些发现将为T1D的预测、预防和治疗提供新的病因学途径和途径。

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 and can affect up to 1 in 300 children. In 2001, the Type 1 Diabetes Genetics Consortium (T1DGC) was established with the goals of conducting large-scale genetic studies to identify regions in the genome that contribute to T1D risk and making available the assembled data and biospecimens to the broader scientific community. The T1DGC has been highly successful conducting the most robustly powered studies of T1D using (a) candidate genes, (b) variants in the MHC, (c) genome-wide linkage, and (d) genome-wide association scan (GWAS). From our GWAS meta-analysis of over 16,000 cases and controls, 18 newly identified loci were reported at genome-wide significance (P d 5 x 10-8), including replication evidence in an independent set of 4,267 cases, 4,463 controls and 2,319 families. This brought the total number of T1D loci to 42. We have now conducted follow-up genotyping of the novel 18 loci, and conducted dense SNP mapping (using the ImmunoChip) in all known T1D loci. We have discovered a single T1D candidate gene in the majority of these loci. The loci from GWAS discovered through analysis of common genetic variants explain less than 15% of the total genetic liability of T1D (after the 50% contributed by genes in the MHC). The residual genetic risk ("missing heritability") may be due to rare variants that are found in coding regions of genes. These rare coding variants are likely to be functional with large effects on risk. Unlike common variants, DNA sequencing of these coding and regulatory regions is required for discovery of rare functional variants. The sequencing effort, even of the coding regions (the "exome") is performed at significant cost. For testing association of T1D with rare variants, a genotyping array (the ExomeChip) has been designed from over 12,000 human exomes. This custom array will permit testing of rare variants by genotyping large numbers of samples at reduced cost. The ExomeChip contains ~200,000 non-synonymous, non-sense, and splice-site variants, as well as 100,000 variants for additional content (including MHC variants). The primary aims of this grant application are to discover novel genetic risk factors that contribute to risk of T1D by 1) conducting ExomeChip genotyping in a well-characterized collection of 2,500 T1DGC affected sib pair (ASP) families; 2) performing analyses of the ExomeChip data (both single SNP and burden tests) in order to discover new genes whose functionally significant variants influence risk to T1D; and 3) replicating these novel results by targeted sequencing in ~7,000 T1D cases and ~7,000 controls. We will integrate existing data (HLA typing, ImmunoChip, CNV, ExomeChip) to provide a comprehensive analysis of rare, common, and structural variation associated with T1D risk. These findings should lead to novel pathways of etiology and avenues for T1D prediction, prevention and therapy.