Identification of genetic modifiers of Alzheimer's disease in multiethnic cohorts

项目总结/摘要 我们已经开发出一种新颖而强大的统计方法来识别与以下疾病相关的遗传变异： 发病年龄（AAO）或事件发生时间特征。在这里，我们建议确定遗传修饰剂的AAO阿尔茨海默氏症 通过在代表不同祖先的两个大数据集中进行全基因组关联测试， 其次是独立数据集的重复研究。 拟议的研究在三个方面改进了以前的工作。首先，我们介绍一本小说 统计方法评估作为删失性状的AD的AAO的变化。通过分析AAO作为定量 删失性状，我们也提高了我们的研究力量相对于病例对照研究的数量性状是 更多信息。我们将最大限度地利用这些信息，减少遗传和表型变异造成的， 已知的来源（例如，APOE基因型和群体结构）。其次，我们改进了以前的工作， 纳入代表不同祖先的数据集，并根据人口结构和 和数据之间的关联性。种群间的亲缘关系越远，重组越多 变异之间，导致更小的共享单倍型，因此更精确的估计的位置， AAO改性剂。此外，许多已知的AD风险基因座是祖先信息，在不同的年龄组中差异显著。 在人群中的频率。由于检测关联的能力随着等位基因频率的增加而增加，我们 通过研究不同的人群来提高我们的研究能力。最后，我们将能够更精确地定位 AAO修饰符，通过插补合并更密集的标记数据。估算是一种具有成本效益的策略 用于从微阵列数据获得序列水平的基因型数据。估算方法的最新进展和 建立大而多样的基于序列的参考组使得有可能准确地 估算频率低至0.1%的变异，确保我们捕获大部分多态性变异 在这些数据中。通过鉴定AAO的遗传修饰剂，我们将进一步深入了解AAO的生物学特性。 AD并指定其他治疗靶点。

Project Summary/Abstract We have developed a novel and powerful statistical approach to identify genetic variants associated with age-at-onset (AAO) or time-to-event traits. Here, we propose to identify genetic modifiers of AAO of Alzheimer's disease (AD) through genome-wide association testing in two large data sets representing different ancestries, followed by replication studies in independent data sets. The proposed studies improve upon previous work in three areas. First and foremost, we introduce a novel statistical approach that evaluates variation in AAO of AD as a censored trait. By analyzing AAO as a quantitative censored trait, we also improve the power of our study relative to case-control studies as the quantitative trait is more informative. We will maximize that information by reducing the genetic and phenotypic variation caused by known sources (ex., APOE genotype and population structure). Secondly, we improve upon previous work by incorporating data sets representing diverse ancestries and adequately adjusting for both population structure and relatedness within the data. The more-distant relationships between populations involve more recombination between variants, resulting in smaller shared haplotypes and therefore more precise estimates of the location of AAO modifiers. Furthermore, many of the known AD risk loci are ancestry-informative, varying significantly in frequency across human populations. As the power to detect association increases with allele frequency, we improve the power of our study by studying diverse populations. Lastly, we will be able to more precisely locate AAO modifiers by incorporating denser marker data through imputation. Imputation is a cost-effective strategy for obtaining sequence-level genotype data from microarray data. Recent advances in imputation methods and the establishment of large and diverse sequence-based reference panels have made it possible to accurately impute variants with frequencies as low as 0.1%, ensuring that we capture much of the polymorphic variation within these data sets. By identifying genetic modifiers of AAO, we will provide further insight into the biology of AD and nominate additional therapeutic targets.