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，科学家已经确定了数千种疾病/特质易感性地方，更多被发现。但是，这些变体影响基因功能的生物学机制和下游生物过程尚不清楚。前进的有前途的途径是研究通用的影响细胞/分子表型的变化，例如转录组，蛋白质组和表观基因组（即“ imics” 表型）。此外，对遗传变异与复杂性状和复杂性状的联合关联的分析 OMICS-表型具有阐明已知关联基础机制或揭示新颖的机制遗传变异与复杂性状之间的关系。我们的第一个目的是开发整合QTL的方法从多个研究/组织/细胞类型的关联汇总统计数据与重叠或独立识别具有协调效果的OMICS QTL和多矩QTL的样品（并且潜在不同效应大小）对不同条件下多种OMICS型的效果。此外，大多数现有的OMIC QTL分析专注于顺式求解，因为考虑多个测试调整。在我们的第二个目标中，我们将提出新的方法来检测特定但很普遍的方法跨性别的类型 - 从顺式转录本介导的类型。不同于反式关联具有通常具有组织特异性的极端作用，由顺式 - 基因表达介导通常在功能相关的组织类型之间共享效果。因此，我们提出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（顺式和反式）疾病/性状图中的QTL，并进一步了解其疾病/性状相关性。在第三目的，通过利用基因特异性模式的eqtl效应如何在不同的组织类型中共享，我们将与现有的全转录组关联研究方法相比，开发改进的方法。我们将提出建议模型预测多种组织类型的基因表达水平，并进一步关联基因型使用现有的GWAS数据，与疾病相关的组织类型的表达水平。在这三个目的是分别分析乳腺癌，精神分裂症和身高，为三个集中的特征在每个目标中，通过整合来自基因型组织表达项目（GTEX）的数据，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数据研究联盟。提出的方法可以应用于其他相关疾病和特征。我们的工作将确定与复杂性状相关的新基因候选者，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。