Integration of multiscale genomic data for comprehensive analysis of complex dise

整合多尺度基因组数据以全面分析复杂疾病

• 批准号: 9334256
• 负责人: YU-PING WANG
• 金额: $ 32.58万
• 依托单位: TULANE UNIVERSITY OF LOUISIANA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-17 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9334256
• 关键词: Address; Bioinformatics; Biological Markers; Biological databases; Biomedical Engineering; Clinical Data; Complex; Copy Number Polymorphism; Data; Data Analyses; Data Set; Detection; Development; Diagnosis; Dimensions; Disease; Drug Targeting; Gene Chips; Genes; Genetic; Genetic Epistasis; Genetic Risk; Genetic Variation; Genetic study; Genomics; Goals; Group Structure; Heterogeneity; Joints; Knowledge; Link; Methodology; MicroRNAs; Modeling; Molecular; Osteoporosis; Pathway interactions; Patients; Pattern; Phenotype; Population; Prevention; Property; Proteomics; Research; Resolution; Risk; Sample Size; Sampling; Single Nucleotide Polymorphism; Source; Techniques; United States National Institutes of Health; Work; analytical tool; biomarker discovery; clinical effect; data integration; data management; disorder risk; epigenomics; genetic signature; genetic variant; genomic data; innovation; multilevel analysis; multiple omics; next generation sequencing; novel strategies; outcome forecast; personalized diagnostics; personalized medicine; public health relevance; risk variant; software development; tool; trait; transcriptomics

DESCRIPTION (provided by applicant): Complex diseases are caused by a variety of genomics, transcriptomics, epigenomics, and proteomics factors and many studies have suggested that these different factors do not act in isolation, but rather interact/crosstalk at multiple levels and depend on one another in an intertwined manner. A variety of genomics techniques such as SNPs, microarray gene expressions, and the emerging next generation sequencing (NGS), have generated vast amount of multiscale genomic data, providing multi-dimensional and complementary information. However, currently these multiscale genomics data have not been well integrated and associated with clinical data for comprehensive analysis of a disease. The difficulty lies in the complexity and heterogeneity of these multi-omics data. In addition, the specific properties of these data (e.g., their correlations across multiple levels, small sample size but large number of biomarkers, group structures) have not been well considered, which necessitate a paradigm shift in the technical approaches. The goal of this project is therefore to tackle these significant bioinformatics challenges by developing innovative integration approaches such as sparse models by considering the specific features of multiscale genomic data. Furthermore, we will apply them to the diagnosis (e.g., identification of genes) and prediction of risks to complex diseases (e.g., osteoporosis). Our multi-/inter-disciplinary research team consisting of statisticians, geneticists, molecular biologists, bioinformaticians and biomedical engineers with complementary expertise has worked synergistically in the past few years and contributed significantly to the development of data integration approaches. Building on this work, we plan to accomplish the following specific aims: 1) To extract genetic signatures (e.g., CNVs) from multiple NGS samples and incorporate them into multi-omics studies; 2) To study the cross-talks/correlations between multi-omics data, from which epistatic networks can be detected; 3) To develop data integration techniques that can combine multiple genomic factors for the identification of risk genes and regions; and 4) To construct a sparse regression model to predict quantitative traits with increased power from multiple sources of genomic information including pathways and interaction networks. We will validate our model with the study of osteoporosis at Tulane Center for Bioinformatics and Genomics. With over 20,000 patients collected, to our knowledge, we have the largest and most comprehensive datasets, which will serve as a unique platform for validating our approaches. We anticipate that the project will have a large and sustained impact. The successful implementation of the project will enable us to 1) better elucidate specific genetic risk mechanisms for osteoporosis; 2) search for potential drug targets; and 3) ultimately obtain novel approaches for better prevention and treatment of osteoporosis. Upon the completion of the project, we will provide a set of efficient and powerful analytical tools for integrative data analysis, and make them freely available through our ongoing software development of GCATs (Genomic Convergence Analysis Tools) for multiscale genomic data management and analysis.

描述(申请人提供)：复杂的疾病是由各种基因组学、转录组学、表观基因组学和蛋白质组学因素引起的，许多研究表明，这些不同的因素并不是单独作用的，而是在多个水平上相互作用/串扰，以相互交织的方式相互依赖。各种基因组学技术，如SNPs、微阵列基因表达和新兴的下一代测序(NGS)，已经产生了海量的多尺度基因组数据，提供了多维和互补的信息。然而，目前这些多尺度基因组学数据还没有很好地整合在一起，并与临床数据相关联，以便对疾病进行全面分析。困难在于这些多组学数据的复杂性和异质性。在……里面 此外，这些数据的具体性质(例如，它们在多个水平上的相关性、小样本量但大量的生物标志物、组结构)没有得到很好的考虑，这就需要在技术方法上进行范式转变。因此，该项目的目标是通过开发创新的技术来应对这些重大的生物信息学挑战 通过考虑多尺度基因组数据的特定特征的稀疏模型等整合方法。此外，我们将把它们应用于诊断(例如，识别基因)和预测复杂疾病(例如，骨质疏松症)的风险。我们的多学科/跨学科研究团队由统计学家、遗传学家、分子生物学家、生物信息学家和 具有互补专业知识的生物医学工程师在过去几年中协同工作，为数据集成方法的发展做出了重大贡献。在这项工作的基础上，我们计划实现以下具体目标：1)从多个NGS样本中提取遗传特征(例如CNV)，并将它们纳入多组学研究；2)研究多组学数据之间的相互作用/相关性，从中可以检测到上位性网络；3)开发能够结合多个基因组因素来识别风险基因和区域的数据集成技术；以及4)构建稀疏回归模型，以预测从包括路径和相互作用网络在内的多个基因组信息来源中获得的更强大的数量性状。我们将通过杜兰生物信息学和基因组学中心对骨质疏松症的研究来验证我们的模型。据我们所知，我们收集了20,000多名患者，我们拥有最大和最全面的数据集，这将成为验证我们的方法的独特平台。我们预计该项目将产生巨大和持续的影响。该项目的成功实施将使我们能够1)更好地阐明骨质疏松的特定遗传风险机制；2)寻找潜在的药物靶点；3)最终获得更好地预防和治疗骨质疏松症的新方法。项目完成后，我们将为综合数据分析提供一套高效而强大的分析工具，并通过我们正在进行的用于多尺度基因组数据管理和分析的GCATS(基因组聚合分析工具)软件的开发免费提供这些工具。