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Integrative multivariate association and genomic analyses

综合多变量关联和基因组分析

基本信息

批准号：
10412060
负责人：
Lin Chen
金额：
$ 36.42万
依托单位：
UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-02-15 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10412060
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 Structure 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

项目摘要

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。被发现。然而，这些变异影响基因功能的生物学机制和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”）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机制或揭示新的关联遗传变异和复杂性状之间的关系。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的组学 QTL 和多组学 QTL（以及可能不同的效应大小）对不同条件下多个组学表型的影响。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测试调整。在我们的第二个目标中，我们将提出新颖的方法来检测特定但相当普遍的反式关联类型——由顺式基因转录物介导的类型。与跨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，我们建议的调解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。在第三个目标是，通过利用不同组织类型之间共享 eQTL 效应的基因特异性模式，我们将为全转录组关联研究开发现有方法的改进方法。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 数据确定具有复杂疾病/性状的疾病相关组织类型的表达水平。在这三个目标，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。

项目成果

期刊论文数量（16）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A new method to study the change of miRNA-mRNA interactions due to environmental exposures.

DOI：
10.1093/bioinformatics/btx256
发表时间：
2017-07-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
0
作者：
Petralia F;Aushev VN;Gopalakrishnan K;Kappil M;W Khin N;Chen J;Teitelbaum SL;Wang P
通讯作者：
Wang P

Characterizing functional consequences of DNA copy number alterations in breast and ovarian tumors by spaceMap.

通过 spaceMap 表征乳腺癌和卵巢肿瘤中 DNA 拷贝数变化的功能后果。