Dissecting the Genomic Etiology of non-Mendelian Early-Onset Alzheimer Disease and Related Phenotypes

剖析非孟德尔早发性阿尔茨海默病的基因组病因学及相关表型

基本信息

批准号：
10667461
负责人：
Gary Wayne Beecham
金额：
$ 112.39万
依托单位：
UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-15 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10667461
关键词：
Accounting Address Affect Age Age of Onset Alzheimer&apos s Disease Alzheimer&apos s disease risk Blood Vessels Cardiovascular Diseases Cardiovascular system Caribbean Hispanic Categories Cognitive Collection Data Data Set Dementia Development Disease Disparate Early Onset Alzheimer Disease Early identification Elderly Ethnic Population Etiology Failure Family Frequencies Genes Genetic Genetic Diseases Genetic Variation Genome Genomic approach Genomics Genotype Heritability Impairment Individual Infrastructure Inheritance Patterns Investigation Joints Language Link Memory Mental Depression Methods Minority Groups Molecular Mutation Neurocognitive Neuropsychology Not Hispanic or Latino Participant Pathway Analysis Pathway interactions Phenotype Protocols documentation Race Research Resources Risk Role Sampling Symptoms Technology Universities Variant Visuospatial Washington admixture mapping advanced analytics analytical method cognitive control cohort comorbidity early onset endophenotype experimental study genetic architecture genetic linkage analysis genetic risk factor genetic variant genome resource genome sequencing genomic data improved member neuropsychiatry new therapeutic target novel novel therapeutics phenome polygenic risk score presenilin-1 presenilin-2 racial population risk variant segregation sex trait vascular risk factor whole genome

项目摘要

PROJECT SUMMARY Genomic studies of Alzheimer's disease (AD) have primarily focused on non-Hispanic White (NHW) participants affected by the late-onset form of the disease (LOAD; onset age: >65), or the study of early onset AD (EOAD; onset age <=65) cases from families showing Mendelian inheritance patterns associated with mutations in the APP, PSEN1 and PSEN2 genes. However, mutations in these three genes explain ~10% of EOAD cases. There are no large-scale efforts to collect and study EOAD cases not explained by these genes, despite the fact that this unexplained EOAD category accounts for ~90% of cases. The few smaller studies that have been conducted suggest that the genetic architecture of EOAD overlaps with the late-onset form only partially. Thus, studying EOAD in subjects without APP, PSEN1 and PSEN2 mutations is a critical gap that provides a unique opportunity for discovering novel therapeutic targets and molecular pathways. To address this issue we aim to identify additional EOAD-associated variants through a large-scale whole- genome sequencing (WGS) study of unexplained EOAD. We will include cases from several well-established AD cohorts including the Resource for Early-onset Alzheimer Disease Research (READR), the Knight-ADRC at Washington University, the Alzheimer's Disease Genetics Consortium (ADGC), and others. Generating and harmonizing a dataset of 200 non-Hispanic White (NHW) and Caribbean Hispanic (CH) multiplex EOAD families, over 4,000 EOAD singletons and over 13,000 unrelated, cognitive controls, all with WGS, this project will yield the largest EOAD genomics dataset to-date, improving statistical power for variant identification and allowing us to assess the impact of specific factors such as APOE genotype, vascular risk factors, and neuropsychiatric comorbidities. The inclusion of a large set of CH families and singletons will allow the examination of EOAD risk in a significantly understudied but fast-growing minority population. Analyses will comprise both linkage and association-based approaches, analyses of polygenic and ancestry effects, and a thorough examination of neurocognitive, neuropsychiatric and cardiovascular endophenotypes. We expect that when successfully completed, this study will point to novel genetic contributors to EOAD, shed light on the mechanisms of AD and facilitate the development of novel therapeutics. Sampling, phenotyping and sequencing analysis protocols will be complementary to and compatible with the existing LOAD genomics resources, such as the Alzheimer Disease Sequencing Project (ADSP) and related studies. This phenotypic and genomic consistency, together with the use of existing AD infrastructure (NIAGADS), allows for immediate integration with the leading efforts on LOAD, enabling rapid large-scale investigation of a variety of additional critical AD genomics hypotheses.

项目概要阿尔茨海默病 (AD) 的基因组研究主要集中在非西班牙裔白人 (NHW) 参与者受晚发疾病（LOAD；发病年龄：>65）或早发 AD（EOAD；发病年龄：>65）研究的影响发病年龄<=65）来自家庭的病例显示出与基因突变相关的孟德尔遗传模式 APP、PSEN1 和 PSEN2 基因。然而，这三个基因的突变可以解释大约 10% 的 EOAD 病例。那里没有大规模的努力来收集和研究 EOAD 病例，这些病例不是由这些基因解释的，尽管事实上这种无法解释的 EOAD 类别约占病例的 90%。已经进行的少数小型研究表明 EOAD 的遗传结构与晚发型仅部分重叠。因此，学习没有 APP、PSEN1 和 PSEN2 突变的受试者中的 EOAD 是一个关键缺口，提供了独特的机会发现新的治疗靶点和分子途径。为了解决这个问题，我们的目标是通过大规模的整体研究来识别其他与 EOAD 相关的变体。无法解释的 EOAD 的基因组测序 (WGS) 研究。我们将包括来自几个成熟的案例 AD 队列包括早发性阿尔茨海默病研究资源 (READR)、Knight-ADRC 华盛顿大学、阿尔茨海默病遗传学联盟 (ADGC) 等。生成和协调 200 个非西班牙裔白人 (NHW) 和加勒比西班牙裔 (CH) 多重 EOAD 家庭的数据集，超过 4,000 个 EOAD 单例和超过 13,000 个不相关的认知控制，全部通过 WGS，该项目将产生迄今为止最大的 EOAD 基因组数据集，提高了变异识别的统计能力，使我们能够评估 APOE 基因型、血管危险因素和神经精神等特定因素的影响合并症。纳入大量 CH 家庭和单身人士将有助于检查 EOAD 风险在一个未被充分研究但快速增长的少数民族人口中。分析将包括链接和基于关联的方法，多基因和祖先效应分析，以及彻底的检查神经认知、神经精神和心血管内表型。我们期望当成功时完成后，这项研究将指出 EOAD 的新遗传因素，阐明 AD 的机制和促进新疗法的开发。采样、表型分析和测序分析方案将与现有的 LOAD 基因组学资源，例如阿尔茨海默病测序项目 (ADSP) 和相关研究。这种表型和基因组的一致性，以及现有 AD 基础设施的使用 (NIAGADS)，允许立即与 LOAD 上的领先工作集成，从而实现快速大规模对各种其他关键 AD 基因组学假设的研究。