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

项目总结 阿尔茨海默氏症测序项目(ADSP)是一项全国性的测序计划，重点是 确定阿尔茨海默病(AD)的遗传风险和保护因素，以努力确定新的途径 用于预防和药物开发的新目标。项目的发现阶段包括整个exome 10,914例无关病例(N=5,778)和对照(N=5,136)的测序和全基因组测序 (WGS)1,019个家系样本。这些样本中的大多数都是非西班牙裔白人(Nhw)，这使得 将不同种族的样本添加到研究中，这对识别共同和新的基因至关重要 人群间AD的危险因素。这一种族多样性在ADSP的后续研究中得到了强调 (FUS)阶段规划阶段，指示增加具有无关AD病例的现有队列 “包含尽可能丰富的种族多样性”被列为最优先考虑的内容。 为了实现本RFA和本FUS阶段任务的目标，本提案确定了七位现有的老年人 非洲裔美国人(AA)和泛HI血统队列共10,430例样本(N=2,322例AA AD病例 和1,843个AA对照和2,928个西班牙裔AD病例和2,875个西班牙裔对照)用于WGS和处理 与NIH资助的现有AD基础设施协作。将这些群体与现有的非裔美国人结合起来 (AA)和西班牙裔(HI)从华盛顿高地-汉密尔顿高地-因伍德社区老龄化 项目(WHICAP)、阿尔茨海默病遗传学联合会(ADGC)和ADSP将提供大量 用于验证ADSP发现阶段发现和发现新风险的不同种族的数据集 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 通过比较等位基因频率差异，对根据祖先表现出不同风险的疾病进行基因作图 在种群之间。它们也将成为AD研究界的宝贵资源， 并将有助于解决导致AA和HI人群AD发病率较高的健康差距 而不是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 和学习的对照。 具体地说，我们建议：1)通过收集以下样本来增加ADSP的种族多样性 有AA和HI AD病例和对照的现有队列；2)与国家细胞库合作 阿尔茨海默病(NCRAD)与DNA在这些队列上的组装、存储和分布；3)产生 所有收集的样本的全基因组SNP阵列数据和WGS；以及4)与NIA Genetics 阿尔茨海默病数据存储站点(NIAGADS)和阿尔茨海默病基因组中心(GCAD) 在处理、质量控制、存储和分配最终数据集方面。我们的整体目标是加强 通过促进在不同种族的数据集中对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.