Deep Resequencing of Candidate Gene Regions in Late-onset Alzheimer's Disease

描述(申请人提供)：阿尔茨海默病(AD)，特别是晚发性(LOAD)是一种复杂的多因素神经退行性疾病，可能涉及多个基因。在2010年前，载脂蛋白E是唯一确定的负荷风险因素。然而，最近的五项大型全基因组关联研究(GWAS)发现，除CR1和CD2AP外，其他9个基因座(包括ABCA7、MS4A4、EPHA1、CLU、CR1、PICALM、BIN1、CD2AP和CD33)以及ALL中的SNPs与负载显著相关。尽管GWAS在发现LOAD的额外基因方面做出了重大贡献，但他们不太可能识别所有的遗传贡献，因为商业GWAS阵列的设计只捕获具有低外显率的常见变异，以检验常见疾病/常见变异假说。另一方面，与未被GWAS捕获的常见变种相比，具有更高个体外显率的罕见变种可能占常见和复杂疾病人群归因风险的三分之一，多个稀有变种可能解释了许多观察到的GWAS信号。此外，Gwas阵列使用依赖于连锁不平衡的间接关联方法来检测关联信号，并且很少识别出显著的变体是 因果变种。这可能解释了与观测到的GWAS信号相关的小效应尺寸。在这里，我们建议对最近GWA中涉及的并在我们的样本中复制的七个基因区域进行深度重新测序，并选择参与这七个基因网络的额外基因，使用下一代测序在1,000例AD患者和对照中识别常见和罕见的SNPs，并在独立的样本中复制它们。识别这些基因中的因果变异将对理解负荷的潜在生物学机制有重要贡献。

DESCRIPTION (provided by applicant): Alzheimer's disease (AD), especially late-onset (LOAD) is a complex multifactorial neurodegenerative disease with the possible involvement of several genes. Until 2010, APOE was the only established risk factor for LOAD. However, recent five large genomewide association studies (GWAS) have identified significant associations of LOAD with SNPs in nine additional loci, including, ABCA7, MS4A4, EPHA1, CLU, CR1, PICALM, BIN1, CD2AP and CD33 and all, but CR1 and CD2AP, have been replicated in our GWAS sample. Although GWAS have made significant contribution in uncovering additional genes for LOAD, they are unlikely to identify all the genetic contribution because the commercial GWAS arrays are designed to capture only the common variants with low penetrance to test common disease/common variant hypothesis. On the other hand, rare variants having a higher individual penetrance than common variants that are not captured by GWAS may account for 1/3 of the population attributable risk for common and complex diseases and multiple rare variants may account for many of the observed GWAS signals. Furthermore, GWAS arrays use an indirect approach of association that relies on linkage disequilibrium to detect association signals and rarely the identified significant variants are the causal variants. This may explain the small effect sizes associated with the observed GWAS signals. Here we propose to perform deep resequencing of the seven gene regions implicated in recent GWAS and replicated in our sample and selected additional genes involved in the networks of these seven genes using next-generation sequencing in 1,000 AD cases and controls to identify both common and rare SNPs and replicate them in independent samples. The identification of causal variants in these genes would make a significant contribution in understanding the underlying biological mechanism of LOAD.