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: 8251157
• 负责人: Wei Wu
• 金额: $ 47.71万
• 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 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8251157
• 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 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 analysis; genetic linkage analysis; grasp; improved; innovation; novel; phenome; 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.

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非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而不是单独地干扰疾病相关基因的功能。因此，解开因果遗传变异和理解随之而来的细胞和组织转化的机制需要一种分析，该分析共同考虑基因组（G）、转录组（T）和表型组（P）内和之间的元件和模块的上位性、多效性和可塑性相互作用。大多数传统的方法集中在每一个单独的标记基因型和每一个表型之间的关联，他们有有限的统计能力，忽略了复杂的省略结构。我们提出了一个系统的尝试，方法论的发展，在很大程度上未被探索，但实际上是重要的问题之间的结构化协会的“-omes”。结构化关联分析不是单独测试每个SNP的关联性，然后通过多假设检验应用校正，而是识别实体组之间的关联性，每个实体具有其自身不可忽略的复杂结构，例如具有高LD的SNP块、相同途径中的基因模块和属于疾病的临床描述符系统的表型簇。我们将开发一个数学上严格和计算效率高的机器学习平台和软件，以解决与解开G，T和P组中疾病相关元素之间的相互作用有关的方法学挑战。我们的技术创新包括用于单倍型推断、重组热点检测、基因网络和表型网络推断、混合关联映射的新型统计模型和算法，以及最重要的是，一系列新的结构化回归技术，如图正则化回归、图引导融合套索和扩展，其对G、T、和Pomes，并且在一致性和稀疏性上有可证明的保证。我们设想我们提出的研究将为复杂疾病的关联研究开辟一个新的范式，这有助于：1）用于关联映射和疾病基因/途径发现的数据的组内和组间整合，2）彻底探索不同组学数据中的内部结构，因此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神秘关联。3)对DNA变异如何导致复杂性状变异的机制和途径进行联合统计推断，并推断分子网络中基因功能的条件特异性状态，以及4）开发更快和自动化的计算算法，其具有更大的可扩展性和对大规模组间分析的鲁棒性，以及更方便的软件包和用户界面。所有软件工具将免费向公众提供。