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

多种族群体中阿尔茨海默氏病遗传修饰的鉴定

• 批准号: 10219135
• 负责人: ELIZABETH ELOYCE BLUE
• 金额: $ 37.79万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10219135
• 关键词: Affect; Age; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease risk; Area; Base Sequence; Biology; Caribbean Hispanic; Case-Control Studies; Cause of Death; Cholesterol Homeostasis; Code; DNA Sequence; Data; Data Set; Development; Direct Costs; Disease; Distant; Endocytosis; Ensure; Epigenetic Process; Ethnic group; European; Event; Frequencies; Gene Frequency; Genes; Genetic; Genetic Recombination; Genetic study; Genome Scan; Genotype; Haplotypes; Healthcare; Heritability; Human; Immune response; Intensive Care; Location; Maps; Memory Loss; Methods; Nerve Degeneration; Nucleic Acid Regulatory Sequences; Pathogenesis; Patients; Persons; Phenotype; Population; Population Heterogeneity; Public Health; Research; Research Design; Risk; Role; Sampling; Senile Plaques; Signal Transduction; Source; Structure; Survival Analysis; Testing; Time; United States; Variant; Work; cohort; cost effective; database of Genotypes and Phenotypes; early onset; genetic variant; genome wide association study; genome-wide; human old age (65+); improved; insight; interest; large datasets; multi-ethnic; novel; novel strategies; presenilin-1; presenilin-2; prevent; risk variant; tau Proteins; tau aggregation; therapeutic target; trait

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.

项目摘要/摘要 我们已经开发了一种新的和强大的统计方法来识别与 发病年龄(AAO)或事件发生时间特征。在这里，我们建议确定阿尔茨海默氏病AAO的遗传修饰物 疾病(AD)通过在代表不同祖先的两个大型数据集中进行全基因组关联测试， 然后是在独立数据集中的复制研究。 拟议的研究在三个方面改进了以前的工作。首先，我们介绍一部小说 一种统计方法，将AD的AAO变异作为一个被审查的性状进行评估。通过将AAO作为一个定量的 与病例对照研究相比，我们也提高了研究的能力，因为数量性状是 信息量更大。我们将通过减少由以下因素引起的遗传和表型变异来最大化这些信息 已知来源(例如，APOE基因和种群结构)。其次，我们通过以下方式改进了以前的工作 整合代表不同祖先的数据集，并充分调整两个种群结构 以及数据中的关联性。种群之间越远的关系涉及到更多的重组 在变异体之间，导致更小的共享单倍型，从而更准确地估计 AAO修改器。此外，许多已知的AD风险基因座是具有血统信息的，在 在人类群体中出现的频率。随着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研究的能力。最后，我们将能够更精确地定位 通过归类结合更密集的标记数据来进行AAO修改器。归责是一种具有成本效益的策略 用于从微阵列数据中获得序列水平的基因数据。归一化方法和方法的最新进展 大型和多样化的基于序列的参考小组的建立使准确地 归因于频率低至0.1%的变异，确保我们捕捉到大部分多态变异 在这些数据集中。通过确定AAO的遗传修饰物，我们将进一步深入了解AAO的生物学 广告和提名额外的治疗靶点。