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

项目摘要/摘要 全基因组关联研究已经成功地定位了数千个复杂的基因座 然而，这些基因座影响这些表型的方式已经被证明是难以捉摸的，因为大多数 具有不明确的生物学意义。最近的研究表明，GWAS的关联是 富含转录调控和增强子区域。利用这些信息来研究复杂的 表型，目前的研究将分子数量性状基因座(QTL)定位于多组学(即， 表观遗传学、转录组、蛋白质组学和代谢组学)数据，然后将分子QTL整合到GWAS中 功能联想研究。然而，这种方法的影响是有限的，因为现有方法通常 只分析顺式作用的分子QTL，没有考虑连锁不平衡的复杂效应 (LD)对分子QTL(从附近解缠真实因果变异)的定位不确定性 相关的零变量)。这些限制减少了功能关联研究的产量以供考虑 关于分子QTL的信息不完全。这项提议将开发新的贝叶斯统计方法 改进的综合多组学研究与实际应用验证。我们提出的方法有 通过提高作图的精确度来阐明许多复杂表型的基因组病因学 分子QTL与危险基因的识别。这些新的贝叶斯方法是建立在我们最近的工作基础上的 并将通过灵活的先验分布假设来考虑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。(一)首先，我们将延长最近提出的 贝叶斯GWAS方法，能够定位顺式和反式(基因组范围)分子QTL。我们会 通过假设各自的先验分布，为顺式和反式作用变体建立不同的遗传结构模型。 我们以前导出的可伸缩贝叶斯推理算法也将适用于这一新模型。(Ii)接下来， 我们将开发新的贝叶斯方法来研究功能关联，这将采用映射 通过对不同效应大小的灵活的先验假设来考虑分子QTL的不确定性。(Iii)最后， 为了最大限度地利用大样本量的公开摘要级别的多组学数据，我们将推导出新的 仅使用摘要级数据而获得与使用相同结果的贝叶斯推理算法 为我们提出的贝叶斯方法提供个人级别的数据。(Iv)我们会通过以下方式验证建议的方法： 这些数据来自具有良好特征的老年人和相关公众摘要级别的多组学和GWAS数据 研究阿尔茨海默病(AD)、痴呆症和其他复杂表型的数据。我的实验室可以进入那口井- 表征了AD痴呆相关的表型、多组学和来自参与研究的老年人的GWA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现在该提案中开发的新型贝叶斯统计工具。

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.