Novel Bayesian statistical tools for integrating multi-omics data to help elucidate the genomic etiology of complex phenotypes

用于整合多组学数据的新型贝叶斯统计工具，有助于阐明复杂表型的基因组病因学

• 批准号: 10261486
• 负责人: Jingjing Yang
• 金额: $ 38.6万
• 依托单位: EMORY UNIVERSITY
• 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 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10261486
• 关键词: Aging; Algorithms; Alzheimer' s Disease; Alzheimer' s disease related dementia; Bayesian Analysis; Bayesian Method; Biological; Complex; Computer software; Data; Elderly; Enhancers; Epigenetic Process; Etiology; Gene Targeting; Genetic Transcription; Genomics; Individual; Knowledge; Linkage Disequilibrium; Maps; Memory; Methods; Modeling; Molecular; Multiomic Data; Phenotype; Proteomics; Quantitative Trait Loci; Risk; Sample Size; Uncertainty; Validation; Variant; Work; drug discovery; flexibility; genetic architecture; genome wide association study; genome-wide; improved; interest; metabolomics; multiple omics; novel; phenotypic data; religious order study; risk variant; tool; transcriptomics

Project Summary/Abstract Genome-wide association studies (GWAS) have successfully mapped many thousands of loci for complex phenotypes, yet the manner by which such loci influence these phenotypes has proven elusive as the majority of associations have unclear biological significance. Recent work has shown that GWAS associations are enriched in transcription regulatory and enhancer regions. To leverage this information for studying complex phenotypes, current studies map molecular quantitative trait loci (QTL) with respect to multi-omics (i.e., epigenetic, transcriptomic, proteomic, and metabonomic) data and then incorporate molecular QTL in GWAS for functional association studies. However, the impact of this approach is limited because existing methods usually only analyze cis-acting molecular QTL and fail to consider the complicating effects that linkage disequilibrium (LD) has on the mapping uncertainty of molecular QTL (disentangling true causal variation from nearby correlated null variations). These limitations reduce the yield of functional association studies for considering incomplete information about molecular QTL. This proposal will develop novel Bayesian statistical methods for improved integrative multi-omics studies with real applications for validation. Our proposed methods have potential to elucidate the genomic etiology of many complex phenotypes, by increasing the precision of mapping molecular QTL and identification of risk genes. These novel Bayesian methods are built upon our recent work and will account for prior knowledge for the parameters of interest through flexible prior distribution assumptions and account for LD by jointly modeling genome-wide variants. (i) First, we will extend our recently proposed Bayesian GWAS method to enable mapping both cis- and trans-acting (genome-wide) molecular QTL. We will model different genetic architectures for cis- and trans-acting variants by assuming respective prior distributions. Our previously derived scalable Bayesian inference algorithm will also be adapted for this new model. (ii) Next, we will develop novel Bayesian methods for functional association studies, which will take the mapping uncertainty of molecular QTL into account through flexible prior assumptions for variant effect sizes. (iii) Finally, to make the most use of public summary-level multi-omics data of large sample sizes, we will derive new Bayesian inference algorithms using only summary-level data while obtaining equivalent results as using individual-level data for our proposed Bayesian methods. (iv) We will validate the proposed methods by applying them to multi-omics and GWAS data from well-characterized older adults and relevant public summary-level data to study Alzheimer's disease (AD) dementia and other complex phenotypes. My lab has access to the well- characterized AD dementia related phenotypic, multi-omics, and GWAS data from older adults participating in the Religious Orders Study (ROS) and Memory and Aging Project (MAP) studies by Rush Alzheimer Disease Center. We will release free software to implement the novel Bayesian statistical tools developed in this proposal.

项目总结/摘要 全基因组关联研究（GWAS）已经成功地绘制了数千个复杂的基因座， 表型，但这些基因座影响这些表型的方式已被证明是难以捉摸的，因为大多数 相关性的生物学意义尚不清楚。最近的研究表明，GWAS协会是 富含转录调节和增强子区域。为了利用这些信息来研究复杂的 表型，目前的研究绘制了关于多组学的分子数量性状位点（QTL）（即， 表观遗传学、转录组学、蛋白质组学和代谢组学）数据，然后将分子QTL整合到GWAS中， 功能性联想研究然而，这种方法的影响是有限的，因为现有的方法通常 目前的方法只分析顺式作用的分子QTL，而没有考虑连锁不平衡的复杂效应， (LD)对分子QTL定位的不确定性（从附近分离出真正的因果变异 相关零变化）。这些局限性降低了功能关联研究的产量， 分子QTL信息不完整。该提案将开发新的贝叶斯统计方法， 改进的综合多组学研究，并将其应用于真实的验证。我们提出的方法有 通过提高定位的精确度，有可能阐明许多复杂表型的基因组病因 分子QTL和风险基因的鉴定。这些新的贝叶斯方法是建立在我们最近的工作 并且将通过灵活的先验分布假设来说明感兴趣的参数的先验知识 并通过联合建模全基因组变异来解释LD。(i)首先，我们将扩大我们最近提出的 Bayesian GWAS方法能够定位顺式和反式作用（全基因组）分子QTL。我们将 通过假设各自的先验分布来模拟顺式和反式作用变体的不同遗传结构。 我们以前推导的可扩展贝叶斯推理算法也将适用于这个新模型。(ii)接下来， 我们将开发新的贝叶斯方法的功能关联研究，这将采取映射 通过对变量效应大小的灵活先验假设，考虑分子QTL的不确定性。(iii)最后， 为了最大限度地利用大样本量的公共摘要级多组学数据，我们将得出新的 贝叶斯推理算法只使用摘要级数据，同时获得与使用 我们提出的贝叶斯方法的个人水平的数据。(iv)我们将通过应用 他们的多组学和GWAS数据，从充分表征的老年人和相关的公共摘要水平 数据来研究阿尔茨海默病（AD）痴呆和其他复杂的表型。我的实验室可以进入油井- 特征性AD痴呆相关表型、多组学和GWAS数据，来自参与研究的老年人， 宗教秩序研究（ROS）和记忆和衰老项目（MAP）研究拉什阿尔茨海默病 中心我们将发布免费软件来实现本提案中开发的新颖的贝叶斯统计工具。