Multiomics data integration methods to discover putative causal variants, genes and patient heterogeneity for Alzheimers disease

多组学数据整合方法发现阿尔茨海默病的假定因果变异、基因和患者异质性

• 批准号: 10587524
• 负责人: Gao Wang
• 金额: $ 51.17万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2027-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10587524
• 关键词: Acylation; Address; Alternative Splicing; Alzheimer' s Disease; Alzheimer' s disease patient; Alzheimer' s disease risk; Atlases; Bayesian Modeling; Bioinformatics; Brain; Clinical Trials Design; Code; Collection; Complex; Data; Disease; Disease susceptibility; Drug Targeting; Engineering; Etiology; Family; Gene Expression; Genes; Genetic; Genetic Diseases; Genetic Heterogeneity; Genetic study; Genome Mappings; Genomic Segment; Goals; Heterogeneity; Histones; Human; Individual; Investigation; Late Onset Alzheimer Disease; Maps; Methods; Methylation; Modeling; Molecular; Multiomic Data; Multivariate Analysis; Pathogenesis; Pathogenicity; Pathway interactions; Patient risk; Patients; Performance; Pharmaceutical Preparations; Poly A; Polyadenylation; Population; Process; Proteomics; Quantitative Trait Loci; Research Design; Resources; Risk; Sampling; Series; Signal Transduction; Single Nucleotide Polymorphism; Site; Software Engineering; Statistical Models; Susceptibility Gene; Testing; Time; Tissues; Universities; Untranslated RNA; Variant; brain tissue; causal variant; cell type; cohort; computerized tools; data integration; data resource; design; disorder subtype; drug development; endophenotype; epigenomics; functional genomics; gene discovery; genetic association; genetic variant; genome wide association study; genome-wide; improved; individual patient; insight; methylation pattern; model building; molecular phenotype; multi-ethnic; multiple omics; novel; novel strategies; personalized therapeutic; polygenic risk score; risk prediction; success; therapeutic development; trait; transcriptome sequencing; whole genome

PROJECT SUMMARY Despite the success of genome-wide association studies (GWAS) in identifying over 70 susceptibility loci for Late-onset (LO) Alzheimer’s disease (AD), AD related disease and endophenotypes, it remains challenging to pinpoint 1) which are truly causal AD variants; 2) the molecular processes that cause AD; and 3) how AD patients are pathogenically different from each other. Emerging resources for the study of AD genetics, including sequence, functional genomics and epigenomic data, provide unparalleled opportunity to investigate these questions at different molecular levels. We propose a multiomics data integration project to characterizes AD risk for both genetic variants and individual patients, by developing and applying a series of novel computational approaches using Bayesian hierarchical modeling, variable selection and multivariate analysis, for analyses of a wide range of existing and novel AD multiomics data. These methods are designed to integrate many genetic factors — single nucleotide variants, brain tissue molecular traits such as gene expression, alternative splicing, alternative polyadenylation, methylation, histone acylation and proteomics, and various functional annotations for coding and non-coding regions — into a coherent framework for discovery of causal AD variants and genes, and understand patient heterogeneity. Our goals are to 1) combine genetic association evidence from population and family-based studies of diverse ancestry backgrounds; 2) incorporate functional information to infer putative causal genetic variants; 3) identify novel molecular traits and QTLs for alternative polyadenylation and differentially methylated regions in brain tissues; 4) dissect AD association signals using multiple molecular traits across a comprehensive collection of brain tissues and relevant cell types; and 5) characterize AD patients’ risk profiles using causal effects at different molecular levels across brain tissues. Our methods and bioinformatics analyses will be engineered into a high-quality toolbox to also facilitate multiomics studies of other complex diseases. We will develop fine-mapping methods for family and multi-ancestry data, integrated with thousands of genomic functional annotations, to identify putative causal variants from whole-genome sequences. We will develop a new method to generate alternative polyadenylation from RNA-seq data in brain tissues of AD patients and controls, and fine-map its QTL. We will develop and apply new approaches to fine-map differentially methylated regions in brains, to colocalize QTLs for dozens of molecular traits with AD, and to identify novel gene-level associations using predicted molecular traits. Causal effects estimated at variants and gene levels will be integrated to identify new AD gene-sets and pathways, and to characterize risk profiles for AD patients. Causal variants and genes discovered from our project will provide insight for development of therapeutic drugs targeting at specific cellar processes. The multiomics risk profiles built for AD patients will improve clinical trial designs for AD drug development, paving the path to personalized therapeutics.

项目概要 尽管全基因组关联研究 (GWAS) 成功鉴定了 70 多个易感位点 迟发性 (LO) 阿尔茨海默病 (AD)、AD 相关疾病和内表型，仍然具有挑战性 查明 1) 哪些是真正致病的 AD 变异；2) 导致 AD 的分子过程；以及 3) AD 患者是如何发生的； AD 遗传学研究的新兴资源在致病性上彼此不同，包括 序列、功能基因组学和表观基因组数据，为研究这些提供了无与伦比的机会 我们提出了一个多组学数据集成项目来表征 AD。 通过开发和应用一系列新颖的计算技术，可以降低遗传变异和个体患者的风险 使用贝叶斯分层建模、变量选择和多元分析的方法来分析 这些方法旨在整合大量现有的和新颖的 AD 多组学数据。 因素——单核苷酸变异、脑组织分子特征，例如基因表达、选择性剪接、 替代多腺苷酸化、甲基化、组蛋白酰化和蛋白质组学以及各种功能注释 编码区和非编码区——形成一个连贯的框架，用于发现AD变异和基因的因果关系， 并了解患者的异质性：1) 结合来自人群的遗传关联证据。 以及基于家庭的不同血统背景的研究；2）结合功能信息来推断假定的信息 因果遗传变异；3) 识别替代多聚腺苷酸化的新分子特征和 QTL； 脑组织中的差异甲基化区域；4) 使用多种分子特征剖析 AD 关联信号 涵盖全面收集的脑组织和相关细胞类型；5) 描述 AD 患者的风险特征； 我们的方法和生物信息学利用脑组织不同分子水平的因果效应进行分析。 分析将被设计成高质量的工具箱，以促进其他复杂的多组学研究 我们将为家庭和多祖先数据开发精细绘图方法，整合数千种疾病。 基因组功能注释，从全基因组序列中识别假定的因果变异。 开发一种新方法，从 AD 患者脑组织的 RNA-seq 数据中生成替代多腺苷酸化 和控制，并精细定位其 QTL。我们将开发和应用新的方法来进行差异精细定位。 大脑中的甲基化区域，将数十个分子性状的 QTL 与 AD 共定位，并识别新的 使用在变异和基因水平上估计的预测分子特征进行基因水平关联。 将被整合以识别新的 AD 基因组和途径，并描述 AD 患者的风险状况。 从我们的项目中发现的因果变异和基因将为治疗药物的开发提供见解 针对特定细胞过程的多组学风险分析将改善 AD 患者的临床试验。 AD 药物开发设计，为个性化治疗铺平道路。