Integrative multivariate association and genomic analyses

摘要 在过去的十年里，科学家们已经确定了数千个疾病/性状易感基因，其中更多的易感基因 被发现。然而，这些变异影响基因功能和基因的生物学机制 下游的生物过程仍不清楚。一条有希望的前进道路是研究遗传基因的影响 细胞/分子表型的变异，如转录组、蛋白质组和表观基因组(即“组学” 表型)。此外，遗传变异与复杂性状的联合关联分析(S)和 组学-表型有可能阐明潜在的已知关联的机制或揭示新的 遗传变异与复杂性状之间的关系。我们的第一个目标是开发整合QTL的方法 来自重叠或独立的多个研究/组织/细胞类型的关联汇总统计数据 用于识别具有协同效应(且潜在不同)的组学QTL和多组学QTL的样本 效应大小)在不同条件下对多种组学表型的影响。此外，现有的大多数组学QTL分析 将重点放在顺式关联上，因为在考虑了多重关联之后，跨关联的研究力度不够 测试调整。在我们的第二个目标中，我们将提出新的方法来检测一种特定但相当普遍的 反式联合类型--由顺式基因转录本介导的类型。不同于跨国联想 在通常是组织特有的极端影响下，顺式基因表达介导的反式关联 通常呈现出在功能相关的组织类型中共享的效果。因此，我们建议的调解 方法将借用不同组织类型的信息来提高能力。一个最终的目标是如何进一步利用 (顺式和反式)QTL在疾病/性状作图中的作用，并进一步了解它们与疾病/性状的相关性。在第三节 目的，通过利用eQTL效应如何在不同组织类型中分享的基因特定模式，我们将 在现有方法的基础上改进转录组关联研究方法。我们会向你提议 预测多种组织类型中基因表达水平的模型，并进一步关联基因预测的基因 利用现有的GWAS数据，在具有复杂疾病/特征的疾病相关组织类型中的表达水平。在……里面 在这三个目标中，我们将分别将乳腺癌、精神分裂症和身高作为三个重点特征进行分析 通过整合来自基因类型-组织表达项目(GTEx)的数据，临床蛋白质组肿瘤 分析联盟(CPTAC)、英国生物库和来自大规模全基因组协会的汇总统计数据 研究财团。所提出的方法也适用于其他相关的疾病和性状。我们的工作将 识别与复杂性状相关的新候选基因，以及提供新的假设、工具和 数据资源，将加快未来的研究工作，以了解的易感机制 人类疾病。

ABSTRACT Over the last decade, scientists have identified many thousands of disease/trait susceptibility loci, with more to be discovered. However, the biological mechanisms by which these variants affect gene function and downstream biological processes remain unclear. A promising path forward is to study the effects of genetic variation on cellular/molecular phenotypes, such as the transcriptome, proteome, and epigenome (i.e., “omics” phenotypes). Additionally, the analysis of the joint associations of a genetic variant to complex trait(s) and omics-phenotypes has the potential to elucidate mechanisms underlying known associations or to reveal novel relationships between genetic variants and complex traits. Our first aim is to develop methods to integrate QTL association summary statistics from multiple studies/tissue-/cell-types with overlapping or independent samples to identify the omics QTLs and multi-omics QTLs with coordinated effects (and potentially different effect sizes) on multiple omics phenotypes in different conditions. Moreover, most existing omics QTL analyses focus on cis-associations, because the study of trans-associations is underpowered after considering multiple testing adjustment. In our second aim, we will propose novel methods to detect a particular yet quite prevalent type of trans-association – the type mediated by a cis-gene transcript. Different than the trans-associations with extreme effects that are often tissue-specific, the trans-associations mediated by cis-gene expression often present effects shared among functionally related tissue types. As such, our proposed mediation methods will borrow information across tissue types to improve power. An ultimate goal is how to further utilize (cis- and trans-) QTLs in disease/trait-mapping and further understand their disease/trait relevance. In the third aim, by harnessing gene-specific patterns of how eQTL effects are shared across different tissue types, we will develop improved methods over existing methods for transcriptome-wide association studies. We will propose models predicting gene expression levels in multiple tissue types and further associate genotype-predicted expression levels in disease-relevant tissue types with complex diseases/traits using existing GWAS data. In the three aims, we will analyze breast cancer, schizophrenia, and height, respectively, as three focused traits in each aim by integrating data from Genotype-Tissue Expression Project (GTEx), Clinical Proteomic Tumor Analysis Consortium (CPTAC), UK Biobank and summary statistics from large-scale genome-wide association studies consortia. The proposed methods can be applied to other related diseases and traits. Our work will identify new gene candidates associated with complex traits, as well as provide new hypotheses, tools, and data resources that will accelerate future research efforts to understand the susceptibility mechanisms of human diseases.