Genome-Transcription-Phenome-Wide Association: a new paradigm for association stu

全基因组-转录-表型组关联：关联研究的新范式

• 批准号: 7845048
• 负责人: Sally E Wenzel
• 金额: $ 51.57万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-15 至 2014-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7845048
• 关键词: Address; Admixture; Affect; Algorithms; Asthma; Biological Markers; Cells; Clinical; Code; Complex; Computational algorithm; Computer software; DNA; DNA Sequence; Data; Descriptor; Detection; Development; Diagnosis; Diagnostic; Disease; Elements; Family; Gene Expression; Gene Expression Profile; Gene Expression Regulation; Genes; Genetic; Genetic Recombination; Genetic Transcription; Genetic Variation; Genome; Genomics; Genotype; Graph; Haplotypes; Indium; Individual; Joints; Lasso; Lead; Machine Learning; Malignant Neoplasms; Maps; Measures; Methods; Molecular; Molecular Analysis; Molecular Genetics; Molecular Profiling; Obesity; Outcome; Pathogenesis; Pathway interactions; Phenotype; Plastics; Population; Process; Quantitative Trait Loci; Regulator Genes; Research; Role; Software Tools; Statistical Models; Structure; Study Subject; Syndrome; System; Techniques; Technology; Testing; Time; Tissues; Transcript; Variant; Work; base; clinical phenotype; data integration; data mining; disorder control; gene function; genetic linkage analysis; grasp; improved; innovation; novel; phenome; public health relevance; trait

DESCRIPTION (provided by applicant): Many complex disease syndromes consist of a large number of highly related, rather than independent, clinical phenotypes. Differences between these syndromes involve the complex interplay of a large number of genomic variations that perturb the function of disease-related genes in the context of a regulatory network, rather than individually. Thus unraveling the causal genetic variations and understanding the mechanisms of consequent cell and tissue transformation requires an analysis that jointly considers the epistatic, pleiotropic, and plastic interactions of elements and modules within and between the genome (G), transcriptome (T), and phenome (P). Most conventional methods focus on associations between every individual marker genotype and every single phenotype; they have limited statistical power and overlook the complex omit structures. We propose a systematic attempt on methodological development for the largely unexplored but practically important problem of structured associations between the "-omes". Rather than testing each SNP separately for association and then applying a correction by multiple hypothesis test, a structured association analysis identifies associations between groups of entities each with its own sophisticated structure that can not be ignored, such as blocks of SNPs with high LD, modules of genes in the same pathway, and clusters of phenotypes belong to a system of clinical descriptors of a disease. We will develop a mathematically rigorous and computationally efficient machine learning platform and software to address the methodological challenges involved with unraveling the interplay between disease-relevant elements in the G, T, and P omes. Our technical innovations include novel statistical models and algorithms for haplotype inference, recombination hotspot detection, gene network and phenotype network inference, admixture association mapping, and most importantly, a family of new structured regression techniques such as the graph-regularized regression, graph- guided fused lasso and extensions, that perform functional approximations to the association functions among structural elements in the G, T, and P omes, and have provable guarantee on consistency and sparsistency. We envisage our proposed research will open a new paradigm for association studies of complex diseases, which facilitates: 1) Intra- and inter-omic integration of data for association mapping and disease gene/pathway discovery, 2) Thorough explorations of the internal structures within different omic data, so that cryptic associations that are not possibly detectable in unstructured analysis due to their weak statistical power can be now inferred. 3) Joint statistical inference of mechanisms and pathways of how variations in DNA lead to variations in complex traits flows through molecular networks, and inference of condition-specific state of gene function in the molecular networks, and 4) Development of faster and automated computational algorithm with greater scalability and robustness to large-scale inter-omic analysis, and more convenient software package and user interface. All the software tools will be made available for free to the public. PUBLIC HEALTH RELEVANCE: We propose a systematic attempt on methodological development for the largely unexplored but practically important problem of structured association mapping between disease-relevant elements in the genome, transcriptome, and phenome. Since many complex diseases involve composite phenotypes that are the outcome of intricate perturbation of molecular network underlying gene regulatory resulted from complex and interdependent genome variations, structured association analysis at multi-omic level is not only needed, but also necessary, but it is beyond the grasp of convention methods and requires the methodological innovations we propose. Characterizing such interactions can provide a more comprehensive genetic and molecular view of complex diseases, which may lead to the identification of genes underlying disease processes; in addition, such an approach will allow us to formulate hypotheses regarding the roles of these genes with respect to disease pathogenesis, and to develop improved diagnostic biomarkers for multivariate clinical phenotypes.

描述(由申请人提供)：许多复杂的疾病综合征由大量高度相关而不是独立的临床表型组成。这些综合征之间的差异涉及大量基因组变异的复杂相互作用，这些变异在调控网络的背景下扰乱了疾病相关基因的功能，而不是单独的。因此，要了解导致遗传变异的原因并了解随后的细胞和组织转化的机制，需要一种分析，该分析联合考虑基因组(G)、转录组(T)和表现组(P)内部和之间的元素和模块的上位性、多效性和可塑性相互作用。大多数传统方法侧重于每个个体标记基因型和每个单一表型之间的关联，统计能力有限，忽略了复杂的省略结构。我们提出了一种系统的方法论发展的尝试，以解决在很大程度上未被探索但实际意义重大的“群体”之间的结构性关联问题。结构化关联分析不是单独测试每个SNP的关联性，然后通过多个假设检验应用校正，而是识别实体组之间的关联，每个实体具有其自己的复杂结构，不可忽视，例如具有高LD的SNP块、同一途径中的基因模块、以及表型簇属于疾病的临床描述符系统。我们将开发一个数学上严格、计算上高效的机器学习平台和软件，以解决在解开G、T和P基因组中与疾病相关的元素之间的相互作用所涉及的方法学挑战。我们的技术创新包括单倍型推理、重组热点检测、基因网络和表型网络推理、混合关联映射等新的统计模型和算法，最重要的是，一系列新的结构化回归技术，如图正则回归、图引导融合套索和扩展，对G、T和P基因组中结构元素之间的关联函数进行函数逼近，并具有可证明的一致性和稀疏性保证。我们预计我们提出的研究将为复杂疾病的关联研究开辟一种新的范式，这有助于：1)内部和内部集成数据以进行关联映射和疾病基因/途径发现，2)彻底探索不同基因组数据的内部结构，以便现在可以推断出由于统计能力较弱而在非结构化分析中不可能检测到的隐秘关联。3)对DNA变异如何导致复杂性状在分子网络中流动的机制和途径进行联合统计推断，以及对分子网络中基因功能的特定条件状态进行推断；4)开发更快和自动化的计算算法，具有更大的可扩展性和对大规模经济分析的稳健性，以及更方便的软件包和用户界面。所有的软件工具都将免费向公众开放。公共卫生相关性：对于基因组、转录组和现象组中与疾病相关的元素之间的结构化关联图谱这一在很大程度上未被探索但实际上很重要的问题，我们提出了一种系统的方法学开发尝试。由于许多复杂疾病涉及复合表型，这些表型是复杂和相互依赖的基因组变异导致的基因调控分子网络复杂扰动的结果，因此在多基因组水平上进行结构化关联分析不仅是必要的，而且是必要的，但它超出了常规方法的掌握，需要我们提出的方法学创新。表征这些相互作用可以提供复杂疾病的更全面的遗传和分子观点，这可能导致识别疾病过程的潜在基因；此外，这种方法将允许我们就这些基因在疾病发病机制中的作用提出假设，并开发针对多变量临床表型的改进的诊断生物标志物。