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

描述（由申请人提供）：几项迟发性阿尔茨海默病（LOAD）的全基因组关联研究（GWAS）现已发表。虽然所有这些研究都检测到了APOE 54与LOAD风险的关联，但只有两项规模最大的研究（超过10,000例病例和对照）为任何新的位点提供了全基因组的重要证据。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盟（ADGC）。我们还开发了一项创新计划，使用脑脊液生物标志物水平作为我们LOAD基因研究的内表型。我们的初步数据证明了这种方法在识别改变生物标志物水平和改变LOAD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。这种内表型方法还具有针对相关变异效应的特定生物学假设的优势，这些变异可以通过简单的细胞培养试验进行测试。该提案的目标是结合和分析现有的LOAD GWAS数据，然后使用一种结合定量中间性状、重测序、生物信息学、表达和功能研究的新方法，以促进识别和表征调节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)使用一种新颖的方法，基因组信息网络，系统地整合生物学信息，优先考虑单核苷酸多态性（snp），以进行后续研究，3)检查LOAD GWAS的顶级snp与脑脊液淀粉样蛋白- β和tau水平的关联，以建立特定的机制假设。4)使用新的遗传和生物信息学方法从复制的SNPs中识别假定的因果变异，5)使用重新测序来识别复制SNPs周围区域的新变异，6)检查顶部命中对基因表达的影响。最后，我们将利用这些努力的信息来测试细胞培养模型中复制snp的特异性淀粉样蛋白- β或tau相关假设。本提案将我们研究团队的独特资源和技能与大量公共资源相结合，形成一种识别和表征LOAD遗传风险因素的新方法。

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.