Harmonization of Additional Data Sets for the Alzheimer's Disease Sequencing Project (ADSP) Follow-Up Study (FUS)

摘要 阿尔茨海默病(AD)是导致老年人痴呆的主要原因，在所有人种和种族中都会发生。 大量研究已经确定了多个AD相关基因和基因座，但很大一部分遗传 对AD的贡献仍不清楚。阿尔茨海默病测序项目(ADSP)正在使用大规模的 测序努力增加我们对影响阿尔茨海默病的遗传变异的了解，特别是罕见的 增加AD风险或预防AD的基因变异。发展战略文件内的一项主要倡议是后续行动 研究(ADSP-FUS)，扩大基因发现工作，将多样化和独特的种群包括在内。超过了 在过去的12个月里，ADSP-FUS的范围已经扩大到包括一些新的测序队列。 这些新队列的纳入具有广泛的临床表型信息，显著 丰富了ADSP在实现其目标方面的价值，即增加对阿尔茨海默病遗传变异的了解 不同的人群。 本补编旨在完成六个新队列的统计前协调活动，这些队列将是 包括在所有ADSP数据集中临床-表型数据的更大协调中。最终的ADSP 协调的数据集，其中包括所有队列(和未来的队列)将创建一个无价的、急需的、 NIA的旧资源。我们将为六个新的FU开展全面的协调前活动 队列(年龄、基因/环境易感性(AGEs)研究；印度老龄化的纵向研究-诊断 痴呆症评估(LASI-DAD)；光州阿尔茨海默病和相关痴呆症(GARD)研究；长寿 家庭研究(LLFS)；阿司匹林减少老年人事件(ASPREE)试验队列；伊比利亚半岛队列) 目前没有通过其他来源提供资金。鉴于这些群体的侧重点各不相同(即，并不是所有人都是 痴呆症队列)有广泛的临床表型信息，这使得协调 很有挑战性。创建统计前协调工作流程，其中这些数据由 ADSP-协调联盟将加快向更大的患者提供协调的临床表型数据 广告社区。为完成统计前的统一工作，我们将：(A)确定、收集和组织 来自新队列的临床表型数据，(B)审查和记录所有人的数据收集程序 相关的临床表型变量，以及(C)对特定队列进行初步质量控制分析 数据。 拟议的统计前统一工作的成功完成将产生以下方面的统一准备数据 将用于对所有亚洲及太平洋战略计划数据集进行统计统一的六个队列。同样重要的是，这 补编将建立一个基础设施，以执行将被整合的统计前协调 进入ADSP-协调联盟。从长远来看，通过加强发展战略计划各群体之间的协调，我们 正在帮助AD研究社区获得有价值的序列数据，以加快基因发现和 确定AD治疗的目标。

ABSTRACT Alzheimer disease (AD) is the leading cause of dementia in older adults and occurs in all ethnic and racial groups. A multitude of studies have identified multiple AD associated genes and loci, but a large portion of the genetic contribution to AD remain unknown. The Alzheimer Disease Sequencing Project (ADSP) is using large-scale sequencing efforts to increase our knowledge about the genetic variation that influences AD, particularly rare genetic variants that enhance AD risk or protect against AD. A major initiative within the ADSP is the Follow Up Study (ADSP-FUS), an expansion of gene discovery efforts to include diverse and unique population. Over the past 12 months, the scope of the ADSP-FUS has expanded to include a number of new cohorts for sequencing. The inclusion of these new cohorts, which have a broad span of clinical-phenotype information, significantly enriches the value of the ADSP in achieving its goal of increasing knowledge of genetic variation in AD across different populations. This supplement is designed to complete pre-statistical harmonization activities in six new cohorts that will be included in the larger harmonization of clinical-phenotype data in all of the ADSP datasets. The final ADSP harmonized dataset, which includes all cohorts (and future cohorts) will create an invaluable, much needed, legacy resource for the NIA. We will perform comprehensive pre-harmonization activities for the six new FUS cohorts (Age, Gene/Environment Susceptibility (AGES) Study; Longitudinal Study of Aging in India-Diagnostic Assessment of Dementia (LASI-DAD); Gwangju Alzheimer and Related Dementias (GARD) Study; Long Life Family Study (LLFS); Aspirin in Reducing Events in the Elderly (ASPREE) Trial Cohort; Iberian Peninsula Cohort) that are not currently funded through other sources. Given that these cohorts vary in their focus (i.e., not all are dementia cohorts) there is a wide span of clinical-phenotype information which makes harmonization challenging. Creating a pre-statistical harmonization workflow in which these data are prepared and used by the ADSP-Harmonization Consortium will expedite the delivery of harmonized clinical-phenotype data to the larger AD community. To complete the pre-statistical harmonization efforts, we will: (a) Identify, collect and organize clinical-phenotype data from the new cohorts, (b) Review and document procedures for data collection for all relevant clinical-phenotype variables, and (c) Perform preliminary quality control analyses for cohort specific data. The successful completion of the proposed pre-statistical harmonization will yield harmonization ready data for the six cohorts that will be utilized in the statistical harmonization of all ADSP datasets. Just as important, this supplement will establish an infrastructure for implementing pre-statistical harmonization that will be integrated into the ADSP-Harmonization Consortium. In the long run, by enhancing harmonization of the ADSP cohorts we are helping make available valuable sequence data to the AD research community to speed gene discovery and identify targets for AD therapies.