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

项目摘要 阿尔茨海默病（AD）是美国老年痴呆症的主要原因， 民族和种族群体。虽然有超过20个易感基因位点与AD相关，但大多数遗传易感性位点都与AD相关。 对AD的影响尚不清楚。这对于不同种族的人群尤其如此，例如西班牙裔（HI） 与非西班牙裔白人相比，非裔美国人（AA）在大多数遗传研究中的代表性不足 （NHW）。虽然新兴的研究已经开始解开一些与AD遗传风险相关的祖先， 理解DNA变异的作用只是理解复杂的生物学基础的关键一步 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范围， 不同祖先的个体，在这里，我们提出了一个转录组学，表观基因组学和基因组关联研究 来自波多黎各（PR）和秘鲁（PER）的NHW、AA和HI，涉及不同的混合人群。完成 该项目的研究将提供强大的功能基因组资源，可应用于大型现有和 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。我们特别 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病例和老年认知正常对照; 2）鉴定转录组和 甲基化差异，包括基因表达、可变剪接、等位基因特异性表达和位点和 NHW、AA和HI病例与对照之间可能导致疾病风险的区域甲基化差异 祖先之间的差异; 3）联合收割机将这些数据与可用的DNA基因分型/全基因组相结合 测序数据以鉴定群体和疾病特异性表达/甲基化QTL; 4）产生第一个 多种族填补面板的表达/甲基化QTL效应，用于估计基因表达性状， 不同的（NWH，AA，HI）AD数据集;以及5）将这些面板应用于更广泛的数千个多种族 基因分型样本。 这里概述的多组学方法将为正在进行的DNA研究提供急需的功能背景。 在这些人群中的变异发现工作，同时解决AD差异的重要问题， research.这些研究将拓宽AD风险与基因表达和甲基化的关系， 将现有的基因组工作扩展到更广泛的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.