Genomic, Epigenomic, and Transcriptomic Mechanisms of Contributing to Alzheimer's Disease Risk in Diverse Ancestral Populations

项目总结 阿尔茨海默病(AD)是美国老年人痴呆症的主要原因， 民族和种族群体。尽管&gt；20个易感基因座与阿尔茨海默病相关，但大多数遗传 对AD的影响尚不清楚。对于西班牙裔(HI)等不同种族的人群来说，情况尤其如此 和非洲裔美国人(AA)相比，他们在大多数基因研究中的代表性低于非西班牙裔白人 (Nhw)。尽管新出现的研究已经开始揭开一些与阿尔茨海默病祖先相关的遗传风险， 了解DNA变异的作用只是理解复杂的潜在生物学的关键一步 变异并不总是提供关于受影响的基因和机制的直接信息。 为了扩大正在进行的大规模基因组基因分型和全基因组测序工作的背景，以 不同祖先的个体，在这里我们建议进行转录、表观基因组和基因组关联研究 来自波多黎各(PR)和秘鲁(PER)的NHW、AA和HI解决了不同的混杂人口。完成 将提供强大的功能基因组资源，可应用于现有的大型和 新兴的AD数据集，以更好地了解遗传变异对AD风险的生物学影响。具体来说，我们 将1)在现有集合上生成全血RNAseq、单细胞RNAseq和DNA甲基化数据集 NHW、AA、PR和PER AD患者和认知正常的老年对照组；2)鉴定转录和 甲基化差异包括基因表达、选择性剪接、等位基因特异性表达以及位点和 NHW、AA和HI病例和对照之间的区域甲基化差异可能导致疾病风险 祖先之间的差异；3)将这些数据与现有的DNA基因分型/全基因组相结合 测序数据以确定群体和疾病特异性表达/甲基化QTL；4)生成第一个 表达/甲基化QTL效应的多种族归因组用于估计基因表达性状 不同的(NWH、AA、HI)AD数据集；以及5)将这些面板应用于数千个多种族的更广泛的集合 对样本进行基因分型。 这里概述的多重组学方法将为正在进行的DNA提供急需的功能背景 在解决AD差异这一重要问题的同时，在这些人群中开展变异发现工作 研究。这些研究将拓宽AD对基因表达和甲基化的风险范围，并扩大 将现有的基因组工作推广到更广泛的AD社区。

PROJECT SUMMARY Alzheimer disease (AD) is the leading cause of dementia in the elderly in the United States and occurs in all ethnic and racial groups. Although >20 susceptibility loci have been associated with AD, much of the genetic influence on AD remains unknown. This is particularly true for diverse ethnic populations such as Hispanics (HI) and African Americans (AA) that are underrepresented in most genetic studies compared to non-Hispanic Whites (NHW). Though emerging studies have begun to unravel some of the ancestry associated genetic risk of AD, understanding the role of DNA variation is only one critical step in understanding the complex underlying biology variants do not always provide direct information on the genes and mechanisms influenced. To expand the context of ongoing large-scale genomic genotyping and whole genome sequencing efforts to individuals of diverse ancestries, here we propose a transcriptomic, epigenomic, and genomic association study of NHW, AA, and HI from Puerto Rico (PR) and Peru (PER) addressing diverse admixed populations. Completion of this project will provide powerful functional genomic resources that could be applied to large existing and emerging AD datasets to better understand the biological impact of genetic variation on AD risk. Specifically, we will 1) generate whole-blood RNAseq, single-cell RNAseq, and DNA methylation datasets on existing collections of NHW, AA, PR, and PER AD cases and elderly cognitively normal controls; 2) identify transcriptomic and methylation differences including gene expression, alternative splicing, allele specific expression, and site and regional methylation differences between NHW, AA, and HI cases and controls that may drive disease risk differences between the ancestries; 3) combine these data with available DNA genotyping/whole genome sequencing data to identify population and disease-specific expression/methylation QTLs; 4) generate the first multiethnic imputation panel of expression/methylation QTL effects for estimating gene expression traits in diverse (NWH, AA, HI) AD datasets; and 5) apply these panels to a broader set of thousands of multi-ethnic genotyped samples. The multi-omics approach outlined here will provide much needed functional context to the ongoing DNA variant discovery efforts in these populations while addressing the important problem of disparities in AD research. These studies will broaden the spectrum of AD risk to gene expression and methylation and expand existing genomic efforts to a broader AD community.