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.

项目摘要 尽管全基因组关联研究（GWAS）在确定70多个易感基因座方面取得了成功， 晚发性（LO）阿尔茨海默病（AD）、AD相关疾病和内表型， 查明1）哪些是真正的因果AD变体; 2）导致AD的分子过程; 3）AD患者如何 在致病性上是不同的AD遗传学研究的新兴资源，包括 序列，功能基因组学和表观基因组学数据，提供了无与伦比的机会，调查这些 不同分子水平的问题。我们提出了一个多组学数据集成项目，以表征AD 通过开发和应用一系列新的计算方法， 方法使用贝叶斯分层模型，变量选择和多变量分析，分析 广泛的现有和新的AD多组学数据。这些方法旨在整合许多遗传 因素-单核苷酸变异，脑组织分子特征，如基因表达，可变剪接， 替代的多聚腺苷酸化、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-为发现致病性AD变体和基因提供了一个连贯的框架， 了解患者的异质性。我们的目标是：1）将来自人群的联合收割机遗传关联证据 和不同祖先背景的以家庭为基础的研究; 2）结合功能信息来推断假定的 致病性遗传变体; 3）鉴定新的分子性状和用于选择性多聚腺苷酸化的QTL， 脑组织中的差异甲基化区域; 4）使用多个分子性状剖析AD关联信号 在脑组织和相关细胞类型的全面收集中;以及5）表征AD患者的风险 在脑组织中使用不同分子水平上的因果效应进行分析。我们的方法和生物信息学 分析将被设计成一个高质量的工具箱，也有利于其他复杂的多组学研究。 疾病我们将开发家庭和多祖先数据的精细映射方法， 的基因组功能注释，以确定推定的因果变异从全基因组序列。我们将 开发一种新的方法，从AD患者脑组织中的RNA-seq数据中产生替代性聚腺苷酸化 和对照，并精细定位其QTL。我们将开发和应用新的方法， 甲基化区域，共定位数十个分子性状与AD的QTL，并鉴定新的 使用预测的分子特征进行基因水平关联。在变异体和基因水平上估计的因果效应 将整合以确定新的AD基因集和途径，并描述AD患者的风险特征。 从我们的项目中发现的致病变异和基因将为治疗药物的开发提供见解 针对特定的酒窖工艺。为AD患者建立的多组学风险特征将改善临床试验 为AD药物开发设计，为个性化治疗铺平道路。