Use of Endophenotypes in the Search for Alzheimer's Disease Risk Genes

描述(由申请人提供)：针对晚发性阿尔茨海默病(LOAD)的几项全基因组关联研究(GWAS)现已发表。虽然所有这些研究都检测到APOE 54与LOAD风险的关联，但只有两项最大的研究，超过10,000个病例和对照为任何新的基因座提供了全基因组范围的重要证据。为了开发更大的数据集，我们和其他人组成了大型合作小组，如阿尔茨海默病遗传学联合会(ADGC)。我们还开发了一个创新的项目，使用脑脊液生物标记物水平作为我们的LOAD遗传学研究的内表型。我们的初步数据显示了这种方法在识别改变生物标记物水平和改变负荷风险、发病年龄或疾病进展速度的基因方面的力量和新颖性。这种内表型方法还有一个优点，即可以针对相关变异的影响提出特定的生物学假设，这些变异可以用简单的细胞培养试验进行测试。这项建议的目标是结合和分析现有的LOAD GWAS数据，然后使用一种新的方法，该方法结合了量化中间性状、重新测序、生物信息学、表达和功能研究，以便于识别和表征调节LOAD风险、发病年龄或疾病进展速度的遗传变异。为了实现这一目标，我们将1)结合和分析LOAD GWAs数据，2)使用一种新的方法，即基因组信息网络，来系统地结合生物信息来确定后续工作的单核苷酸多态(SNPs)的优先顺序，3)检查LOAD GWAs中的顶级SNPs与脑脊液淀粉样β蛋白和tau水平的关系，以建立具体的机制假说，4)使用新的遗传和生物信息学方法来从复制的SNPs中识别可能的因果变体，5)使用重新测序来确定复制SNPs周围区域的新变种，6)研究对基因表达影响的顶级SNPs。最后，我们将使用这些努力中的信息来测试细胞培养模型中复制的SNP的特定淀粉样β蛋白或tau相关假说。这一建议将我们研究团队的独特资源和技能与大量公开可用的资源结合在一起，形成了一种识别和表征负荷遗传风险因素的新方法。 公共卫生相关性：阿尔茨海默病是一种非常常见的神经退行性疾病，没有有效的预防或治疗手段。本研究的目标是确定阿尔茨海默病的遗传风险因素，从长远来看，这可能导致新的药物靶点和改进的疾病治疗/预防。

DESCRIPTION (provided by applicant): Several genome-wide association studies (GWAS) for late-onset Alzheimer's disease (LOAD) have now been published. While all of these studies detected the association of APOE 54 with risk for LOAD only the two largest studies, with over 10,000 cases and controls provided genome-wide significant evidence for any novel loci. To develop larger datasets we and others have formed large collaborative groups, such as the Alzheimer's Disease Genetics Consortium (ADGC). We have also developed an innovative program using cerebrospinal fluid biomarker levels as endophenotypes for our genetic studies of LOAD. Our preliminary data demonstrate the power and novelty of this approach in identifying genes that alter biomarker levels and modify LOAD risk, age at onset or rate of disease progression. This endophenotype approach also has the advantage of pin-pointing specific biological hypotheses regarding the effects of associated variants that can be tested using simple cell culture assays. The goal of this proposal is to combine and analyze existing LOAD GWAS data, then use a novel approach that incorporates quantitative intermediate traits, re-sequencing, bioinformatics, expression and functional studies to facilitate the identification and characterization of genetic variants that modulate risk for LOAD, age at onset or rate of disease progression. To accomplish this we will 1) combine and analyze LOAD GWAS data, 2) use a novel method, the Genomic Information Network, to systematically incorporate biological information to prioritize single nucleotide polymorphisms (SNPs) for follow-up, 3) examine top SNPs from the LOAD GWAS for association with cerebrospinal fluid amyloid-beta and tau levels to establish specific hypotheses of mechanism, 4) use novel genetic and bioinformatic methods to identify putative causal variants from the replicated SNPs, 5) use re-sequencing to identify novel variants in the regions surrounding replicated SNPs, 6) examine the top hits for effects on gene expression. Finally, we will use information from these efforts to test specific amyloid-beta or tau related hypotheses for replicated SNPs in cell culture models. This proposal combines the unique resources and skills of our research team with the vast wealth of publicly available resources into a novel approach to the identification and characterization of genetic risk factors for LOAD. PUBLIC HEALTH RELEVANCE: Alzheimer's disease is a very common neurodegenerative disease with no effective means of prevention or treatment. The goal of the present study is to identify genetic risk factors for Alzheimer's disease, which in the longer term may lead to novel drug targets and improved treatment/prevention of the disease.