Integrating Alzheimer's disease GWAS with proteomic and metabolomic QTL data

将阿尔茨海默病 GWAS 与蛋白质组学和代谢组学 QTL 数据整合

• 批准号: 10018279
• 负责人: Wei Pan
• 金额: $ 186.85万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10018279
• 关键词: Accounting; Address; Aging; Alzheimer' s Disease; Alzheimer' s disease risk; Atrial Fibrillation; Biological; Clinical; Communities; Complex; Computer software; Data; Data Analyses; Data Set; Diagnostic; Disease; Documentation; Early Diagnosis; Environment; Etiology; Gene Expression; Genes; Genetic; Genomics; Goals; Individual; Influentials; Intervention; Knowledge; Least-Squares Analysis; Link; Linkage Disequilibrium; Mediating; Methods; Modeling; Phenotype; Prevention; Proteomics; Public Domains; Publishing; Quantitative Trait Loci; Randomized; Research; Risk Factors; Sampling; Statistical Computing; Statistical Data Interpretation; Statistical Methods; Study models; Testing; Transcript; Untranslated RNA; Work; causal variant; cohort; computerized tools; cost effective; drug development; genetic variant; genome wide association study; improved; insight; metabolomics; method development; novel; pleiotropism; practical application; programs; protein metabolite; response; secondary analysis; software development; statistics; therapeutic development; therapy development; tool; trait; transcriptome; transcriptomics; web site

Summary In response to PA-17-088, “Secondary Analyses of Existing Cohorts, Data Sets and Stored Biospecimens to Address Clinical Aging Research Questions (R01)”, we propose integrating existing GWAS summary data of Alzheimer's disease (AD) with existing proteomic and metabolomic quantitative trait locus (pQTL/mQTL) data to identify proteins and metabolites putatively causal to AD. The overarching goal is to both boost statistical power and enhance interpretability for causal inference in the post-GWAS era by leveraging many published large-scale GWAS summary association datasets and omic data. In an emerging and increasingly influential approach called transcriptome-wide association studies (TWAS), by integrating GWAS summary data with gene expression (or eQTL) data, one aims to improve over the current practice of GWAS to not only increase statistical power to identify more genetic variants associated with GWAS traits, but also link the (non-coding) genetic variants to their target genes, thus gaining insights into the genetic basis of common diseases and complex traits. In practice, however, TWAS may fail to identify true causal genes while giving false positives due to the violation of its modeling assumptions (e.g. due to LD or horizontal pleiotropy of SNPs). We first propose three new methods to check possible violations of modeling assumptions in TWAS, then propose two more robust and powerful approaches that improve over the standard TWAS. Next, we extend TWAS to xWAS to integrate GWAS with proteomic and metabolomic traits (i.e. pQTL and mQTL), to identify (putatively) causal proteins and metabolites, analogous to detecting causal genes/transcripts in TWAS. We apply the new (and existing) methods to integrate large-scale GWAS summary data of AD and atrial fibrillation (AF) with pQTL and mQTL to identify putatively causal proteins and metabolites for AD and AF respectively, and to investigate whether AF is causal to AD, thus not only advancing our understanding of the etiology of AD and AF, but also possibly offering modifiable targets for interventions on the two devastating diseases. Finally, we will develop and disseminate publicly available software implementing the proposed analysis methods, e.g. as R packages, to facilitate the wide use by the scientific community.

摘要 针对PA-17-088，“对现有队列、数据集和储存的生物样品进行二次分析 为了解决临床老龄化研究问题(R01)，我们建议整合现有的GWAS汇总数据 阿尔茨海默病(AD)与现有的蛋白质组和代谢组数量性状基因座(pQTL/mQTL)数据 确定可能与AD有关的蛋白质和代谢物。首要目标是既要增强统计力量 并通过利用许多已出版的大规模数据来增强后GWAS时代因果推理的可解释性 全球农业和农业研究组织的摘要关联数据集和基因组数据。在一种新兴且越来越流行的方法中，称为fl 转录组范围的关联研究(TWAS)，通过将GWAS汇总数据与基因表达(或 EQTL)数据，其目的是改进GWAS的当前做法，不仅增加统计能力， 识别更多与GWAS特征相关的遗传变异，但也将(非编码)遗传变异链接到 他们的目标基因，从而获得对常见疾病和复杂特征的遗传基础的洞察。在实践中， 然而，由于其模型的违反，在给出假阳性的同时，TWAS可能无法识别真正的因果基因 假设(例如，由于Ld或SNPs的水平多效性)。我们首次提出了三种新的检测方法fi 可能违反TWAS中的建模假设，然后提出两种更健壮和更强大的方法 这比标准的TWAs要好。接下来，我们将TWAS扩展到xWAS，以将GWAS与蛋白质组学和 代谢性状(即pQTL和mQTL)，以识别(假定)原因蛋白质和代谢物，类似于 检测三叉戟的致病基因/转录本。我们应用新的(和现有的)方法来集成大规模的 用pQTL和mQTL分析AD和房颤(房颤)的fi数据以确定可能的致病蛋白 和代谢产物分别用于AD和房颤，并探讨房颤是否与AD有关，从而不仅促进 我们对AD和AF的病因的理解，但也可能为Modifi提供干预的目标 这两种毁灭性的疾病。最后，我们将开发和发布公开可用的软件实现 建议的分析方法，例如作为R包，以促进科学fic社区的广泛使用。