Integrative multivariate association and genomic analyses

综合多变量关联和基因组分析

• 批准号: 10162318
• 负责人: Lin Chen
• 金额: $ 36.6万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-02-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10162318
• 关键词: Address; Affect; Biological; Biological Process; CISH gene; Candidate Disease Gene; Cells; Clinical; Collaborations; Complex; Computer software; Data; Data Sources; Disease; Distal; Gene Expression; Genes; Genetic; Genetic Complementation Test; Genetic Variation; Genome; Genomics; Genotype; Genotype-Tissue Expression Project; Goals; Height; Human; Joints; Learning; Literature; Maps; Mediating; Mediation; Methods; Methylation; Modeling; Pattern; Phenotype; Predisposition; Protein Methylation; Proteins; Proteome; Proteomics; Quantitative Trait Loci; Regulation; Reporting; Research; Research Design; Resources; Sampling; Schizophrenia; Scientist; Source; Statistical Methods; Structural Protein; Susceptibility Gene; Testing; The Cancer Genome Atlas; Tissues; Transcript; Variant; Work; base; biobank; brain tissue; cell type; computerized tools; data resource; epigenome; gene function; genetic variant; genome wide association study; genome-wide; human disease; human tissue; improved; interest; malignant breast neoplasm; molecular phenotype; multiple omics; novel; predictive modeling; psychiatric genomics; software development; statistics; tool; trait; transcriptome; transgene expression; tumor; web portal

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.

抽象的 在过去的十年中，科学家们已经确定了数千个疾病/性状易感位点，还有更多的位点有待进一步研究。 然而，这些变异影响基因功能的生物学机制还有待发现。 下游生物过程仍不清楚，一条有希望的前进道路是研究遗传的影响。 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”） 此外，分析遗传变异与复杂性状的联合关联，以及 组学表型有可能阐明已知关联的机制或揭示新的关联 我们的首要目标是开发整合 QTL 的方法。 来自重叠或独立的多项研究/组织/细胞类型的关联汇总统计数据 样本来识别具有协调效应（以及可能不同的组学 QTL）的组学 QTL 和多组学 QTL 此外，大多数现有的组学 QTL 分析。 重点关注顺式关联，因为在考虑多重关联后，反式关联的研究动力不足 在我们的第二个目标中，我们将提出新的方法来检测特定但相当普遍的情况。 反式关联类型——由顺式基因转录物介导的类型，与反式关联不同。 顺式基因表达介导的反式关联具有通常具有组织特异性的极端效应 通常存在功能相关组织类型之间共享的效应，因此，我们提出的中介作用。 方法将借用跨组织类型的信息来提高功效，最终目标是如何进一步利用。 疾病/性状作图中的（顺式和反式）QTL，并进一步了解它们的疾病/性状相关性。 目标是，通过利用 eQTL 效应如何在不同组织类型之间共享的基因特异性模式，我们将 我们将提出针对全转录组关联研究的现有方法的改进方法。 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 数据确定具有复杂疾病/性状的疾病相关组织类型的表达水平。 这三个目标，我们将分别分析乳腺癌、精神分裂症和身高，作为三个重点特征 通过整合基因型组织表达项目 (GTEx)、临床蛋白质组肿瘤的数据来实现每个目标 分析联盟 (CPTAC)、英国生物银行和大规模全基因组协会的汇总统计数据 研究联盟所提出的方法可以应用于其他相关疾病和性状。 识别与复杂性状相关的新候选基因，并提供新的假设、工具和 数据资源将加速未来的研究工作，以了解疾病的易感性机制 人类疾病。