Whole Genome Sequencing in Ethnically Diverse Cohorts for the ADSP Follow-Up Study (FUS)

项目摘要 阿尔茨海默病测序项目（ADSP）是一项国家测序计划，重点是 确定阿尔茨海默病（AD）的遗传风险和保护因素，以确定新的途径 用于预防和药物开发的新靶点。该项目的发现阶段包括整个外显子组 10，914例无关病例（N= 5，778）和对照（N= 5，136）的WES测序和全基因组测序 (WGS)1,019个家族样本这些样本中的大多数是非西班牙裔白色（NHW）， 在研究中增加种族多样性样本对于鉴定共享和新的遗传学至关重要。 人群之间AD的危险因素。ADSP后续研究强调了这种种族多样性 (FUS)阶段“计划阶段，指示额外的现有队列与不相关的AD病例， “包容尽可能丰富的种族多样性”应被赋予包容的最高优先权。 为了实现本RFA和本FUS阶段任务的目标，本提案确定了七个现有的老年人 非裔美国人（AA）和泛HI血统的队列，共10，430份样本（N= 2，322例AA AD病例 和1，843例AA对照和2，928例西班牙裔AD病例和2，875例西班牙裔对照）进行WGS和处理， 与现有NIH资助的AD基础设施合作。将这些队列与现有的非洲裔美国人相结合 (AA)来自华盛顿高地-汉密尔顿高地-因伍德社区老龄化的 项目（WHICAP），阿尔茨海默病遗传学联盟（ADGC）和ADSP将提供大量的 用于验证ADSP发现阶段结果和发现新风险的种族多样性数据集 和/或AD的保护性变体。重要的是，这些数据将允许混合映射，这是一种强大的方法， 通过比较等位基因频率差异，对显示不同祖先风险的疾病进行基因定位 在人群之间。它们也将成为整个AD研究界的宝贵资源， 并将有助于解决AA和HI人群AD发病率较高的健康差异 比NHW。因此，我们将通过创建AA和泛HI AD病例的大型数据集来解决这些重要问题 和对照进行研究。 具体而言，我们建议：1）通过收集来自以下地区的样本，增加ADSP的种族多样性 现有的AA和HI AD病例和对照组; 2）与国家细胞库合作， 阿尔茨海默病（NCRAD）在这些队列中的DNA组装，储存和分布; 3）产生 全基因组SNP阵列数据和所有收集样本的WGS;以及4）与NIA Genetics合作， 阿尔茨海默病数据存储网站（NIAGADS）和阿尔茨海默病基因组中心（GCAD） 最终数据集的处理、质量控制、存储和分发。我们的总体目标是提高 通过促进在种族多样性数据集中对AD的研究来发现AD风险因素。

PROJECT SUMMARY The Alzheimer’s Disease Sequencing Project (ADSP) is a national sequencing initiative focused on identifying genetic risk and protective factors for Alzheimer’s Disease (AD) in an effort to identify new pathways for prevention and new targets for drug development. The projects’ discovery phase included whole exome sequencing (WES) of 10,914 unrelated cases (N=5,778) and controls (N=5,136) and whole genome sequencing (WGS) of 1,019 familial samples. A majority of these samples are non-Hispanic white (NHW) in origin, making the addition of ethnically diverse samples to the study critical to identification of both shared and novel genetic risk factors for AD between populations. This ethnic diversity was emphasized in the ‘ADSP Follow-Up Study (FUS) Phase’ planning stage with a directive that additional existing cohorts with unrelated AD cases that ‘encompass the richest possible ethnic diversity’ be given the highest priority for inclusion. To fulfill the goals of this RFA and this FUS Phase Mandate, this proposal identifies seven existing elderly cohorts of African-American (AA) and pan-HI ancestry with a total of 10,430 samples (N=2,322 AA AD cases and 1,843 AA controls and 2,928 Hispanic AD cases and 2,875 Hispanic controls) for WGS and processing in collaboration with existing NIH-funded AD infrastructure. Combining these cohorts with existing African America (AA) and Hispanic (HI) sequencing from the Washington Heights-Hamilton Heights-Inwood Community Aging Project (WHICAP), the Alzheimer’s Disease Genetics Consortium (ADGC) and the ADSP will provide large ethnically diverse datasets for both validation of ADSP discovery phase findings and discovery of novel risk and/or protective variants for AD. Importantly, these data will allow for admixture mapping, a powerful method of gene mapping for diseases that show differential risk by ancestry, by comparing allele frequency differences between populations. They will also become an invaluable resource for the AD research community at-large, and will help to address the health disparities that contribute to AA and HI populations having higher rates of AD than NHW. Thus, we will address these important issues by creating a large dataset of AA and pan-HI AD cases and controls for study. Specifically we propose to: 1) increase the ethnic diversity of the ADSP by assembling samples from existing cohorts with AA and HI AD cases and controls; 2) collaborate with the National Cell Repository for Alzheimer’s Disease (NCRAD) in assemblage, storage, and distribution of DNA on these cohorts; 3) generating genome-wide SNP array data and WGS for all collected samples; and 4) collaborate with the NIA Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS) and The Genome Center for Alzheimer’s Disease (GCAD) in processing, quality controls, storage and distribution of the final datasets. Our overall goal is to enhance the discovery of AD risk factors by facilitating research on AD in ethnically diverse datasets.