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: 10028615
• 负责人: Jingjing Yang
• 金额: $ 39.7万
• 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/10028615
• 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.

项目概要/摘要 全基因组关联研究（GWAS）已成功绘制了数千个复杂基因座的图谱 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 的关联具有不明确的生物学意义。最近的工作表明 GWAS 协会 富含转录调控区和增强子区。利用这些信息来研究复杂的 表型，目前的研究绘制了多组学的分子数量性状位点（QTL）（即， 表观遗传、转录组、蛋白质组和代谢组）数据，然后将分子 QTL 纳入 GWAS 中 功能关联研究。然而，这种方法的影响是有限的，因为现有的方法通常 只分析顺式作用分子QTL，没有考虑连锁不平衡的复杂影响 (LD) 对分子 QTL 的作图不确定性具有影响（从附近的真实因果变异中解脱出来） 相关的零变化）。这些限制降低了功能关联研究的产量，以考虑 有关分子 QTL 的信息不完整。该提案将开发新的贝叶斯统计方法 改进综合多组学研究和实际应用验证。我们提出的方法有 通过提高作图精度，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。这些新颖的贝叶斯方法建立在我们最近的工作基础上 并将通过灵活的先验分布假设来解释感兴趣参数的先验知识 并通过联合建模全基因组变异来解释 LD。 (i) 首先，我们将延长我们最近提出的 贝叶斯 GWAS 方法能够绘制顺式和反式作用（全基因组）分子 QTL。我们将 通过假设各自的先验分布，为顺式和反式作用变异体的不同遗传结构建模。 我们之前推导的可扩展贝叶斯推理算法也将适用于这个新模型。 (二) 接下来， 我们将开发用于功能关联研究的新贝叶斯方法，该方法将采用映射 通过对变量效应大小的灵活先验假设来考虑分子 QTL 的不确定性。 （三）最后， 为了充分利用大样本量的公共汇总级多组学数据，我们将推导出新的 贝叶斯推理算法仅使用摘要级别的数据，同时获得与使用相同的结果 我们提出的贝叶斯方法的个体级数据。 (iv) 我们将通过应用来验证所提出的方法 将它们转化为来自特征明确的老年人和相关公共摘要级别的多组学和 GWAS 数据 用于研究阿尔茨海默病 (AD) 痴呆和其他复杂表型的数据。我的实验室可以使用井- 表征了参与老年人的 AD 痴呆相关表型、多组学和 GWAS 数据 Rush 阿尔茨海默病的宗教秩序研究 (ROS) 和记忆与衰老项目 (MAP) 研究 中心。我们将发布免费软件来实施本提案中开发的新颖的贝叶斯统计工具。