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

项目总结 尽管全基因组关联研究在确定70多个易感基因座上取得了成功， 晚发性(LO)阿尔茨海默病(AD)、AD相关疾病和内表型，它仍然具有挑战性 精确定位1)哪些是真正引起AD的变异；2)导致AD的分子过程；以及3)AD患者是如何 在病理性上是不同的。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数据为研究这些提供了无与伦比的机会 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来表征AD 通过开发和应用一系列新的计算方法，对遗传变异和个体患者的风险 使用贝叶斯分层建模、变量选择和多变量分析的方法 广泛的现有和新的AD多组学数据。这些方法旨在整合许多基因 因子-单核苷酸变异，脑组织分子特征，如基因表达，选择性剪接， 另类多聚腺苷、甲基化、组蛋白酰化和蛋白质组学，以及各种功能注释 对于编码区和非编码区-进入一致的框架以发现原因AD变体和基因， 并了解患者的异质性。我们的目标是1)结合来自种群的遗传关联证据 和对不同祖先背景的基于家庭的研究；2)结合功能信息来推断 因果遗传变异；3)确定新的分子性状和QTL的交替多聚腺苷和 脑组织中差异甲基化区域；4)利用多种分子特征分析AD关联信号 通过全面收集脑组织和相关细胞类型；以及5)表征AD患者的风险 在不同分子水平上使用不同分子水平的脑组织因果效应的概况。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，以促进对其他复合体的多组学研究 疾病。我们将开发家庭和多祖先数据的精细制图方法，整合数千人 基因组功能注释，以从全基因组序列中识别推定的因果变异。我们会 开发一种新的方法从AD患者脑组织的RNA-SEQ数据中产生替代多聚腺苷 和控制，并精细定位其QTL。我们将开发和应用新的方法来区别绘制精细地图 大脑中的甲基化区域，与AD的数十个分子特征的QTL共定位，并识别新的 使用预测的分子性状进行基因水平的关联。在变异和基因水平上估计的因果效应 将被整合，以确定新的AD基因组和途径，并表征AD患者的风险概况。 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洞察 瞄准特定的酒窖工艺。为阿尔茨海默病患者建立的多组学风险概况将改善临床试验 为AD药物开发设计，为个性化治疗铺平道路。

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.