Integrative multivariate association and genomic analyses

摘要 在过去的十年中，科学家们已经确定了数千个疾病/性状易感基因座，其中更多的是 被发现然而，这些变异影响基因功能的生物学机制， 下游生物过程仍不清楚。一条有希望的前进道路是研究基因的影响， 细胞/分子表型的变异，例如转录组、蛋白质组和表观基因组（即，"组学" 表型）。此外，分析遗传变异与复杂性状的联合关联， 组学-表型有可能阐明已知关联的潜在机制或揭示新的 遗传变异和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 来自多项研究/组织/细胞类型的关联汇总统计量， 样品，以确定具有协调效应的组学QTL和多组学QTL（以及潜在的不同的 效应大小）在不同条件下对多种组学表型的影响。此外，大多数现有的组学QTL分析， 重点放在顺式关联上，因为在考虑了多个关联之后， 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个特定的但相当普遍的 反式缔合类型-由顺式基因转录物介导的类型。与跨协会不同 具有通常是组织特异性的极端效应，由顺式基因表达介导的互作 通常呈现在功能相关的组织类型之间共享的效应。因此，我们提议的调解 方法将借用跨组织类型的信息以提高功率。最终目标是如何进一步利用 (cis-和反式QTL在疾病/性状定位中的应用，并进一步了解其疾病/性状相关性。第三 通过利用eQTL效应在不同组织类型中如何共享的基因特异性模式，我们将 开发优于现有方法的改进方法，用于全转录组关联研究。我们将提出 预测多种组织类型中基因表达水平的模型，并进一步关联基因型预测 使用现有的GWAS数据在具有复杂疾病/性状的疾病相关组织类型中的表达水平。在 这三个目标，我们将分析乳腺癌，精神分裂症，身高，分别作为三个重点性状 通过整合来自基因型-组织表达项目（GTEx）、临床蛋白质组学肿瘤 分析联盟（CPTAC），英国生物库和大规模全基因组关联的汇总统计 研究财团。所提出的方法可以应用于其他相关疾病和性状。我们的工作将 识别与复杂性状相关的新基因候选者，并提供新的假设，工具， 数据资源，将加速未来的研究工作，以了解易感性机制， 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.