Multi-scale modeling of genetic variation in a developmental network

发育网络中遗传变异的多尺度建模

• 批准号: 8740503
• 负责人: Angela H DePace
• 金额: $ 49.65万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-30 至 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8740503
• 关键词: Affect; Alleles; Amino Acid Substitution; Anterior; Binding; Binding Sites; Biochemical; Biological; Biology; Characteristics; Chromosome Mapping; Code; Collaborations; Complex; DNA; DNA Binding; Data; Dependency; Development; Developmental Process; Diagnosis; Disease; Drosophila genus; Drosophila melanogaster; Embryo; Embryonic Development; Epidemiologist; Equation; Etiology; Future; Gene Expression; Gene Expression Profile; Genetic Polymorphism; Genetic Variation; Genome; Genotype; Goals; Health; Human; Individual; Intervention; Joints; Knowledge; Lead; Maintenance; Maps; Measurement; Measures; Medical; Methods; Modeling; Molecular; Molecular Models; Molecular Target; Morphology; Mutation; Non-linear Models; Nucleotides; Output; Pathway Analysis; Pathway interactions; Pattern; Phenotype; Phylogenetic Analysis; Population; Population Genetics; Process; Regulation; Regulator Genes; Reliance; Reporter; Resolution; Shapes; Study models; Techniques; Testing; Therapeutic Intervention; Time; Transcription Initiation Site; Transcription factor genes; Variant; Vertebrates; base; computer based statistical methods; fly; functional genomics; gene function; genome sequencing; genome wide association study; improved; molecular modeling; multi-scale modeling; network models; novel; predictive modeling; preference; public health relevance; research study; screening; spatiotemporal; success; tool; trait; transcription factor; transcriptome sequencing

DESCRIPTION (provided by applicant): With hundreds of sequenced genomes available for many species, the challenge now lies in building predictive models for the genotype-to-phenotype map. Millions of polymorphic bases make each of us morphologically, intellectually, and psychologically unique. The approach of associating whole-genome polymorphisms with a myriad of phenotypes (GWAS) has been in fashion. Its reliance on purely statistical associations requires screening many thousands of individuals to pinpoint alleles that typically explain appreciable, though modest, fractions of natural variation. The next step - the long term goal of this project - is to move from association to causation; where a model of well-understood molecular pathways is modified, individually for each genotype, to reflect functional effects of it unique set of polymorphisms. We develop the concepts and models necessary to advance this goal using Drosophila, where the molecular tools are precise and quantitative predictions are verifiable. We will develop several levels of predictive models. First, we will predict the functioal consequences of SNPs on gene expression from sequence alone, based on knowledge of transcription factor (TF) binding sites and predictive models of how sequence affects DNA shape. These models will be validated with cis-eQTL approaches and directed measurements of expression and TF binding. Second, the composite effects of coding and regulatory polymorphisms will be incorporated into a network-level structural equation model (SEM). We will fit the model with two types of expression data gathered in multiple genotypes, and predict and experimentally verify the functional consequences of unmeasured polymorphisms. Third, the model will be extended to incorporate putative epistatic interactions, estimated using approximate Bayesean computation. This will generalize and 'quantitate' SEM, and evaluate sensitivity of downstream phenotypes to molecular perturbations at different tiers. We will validate these predictions using population genetic data. While conceptually simple, developing this framework requires close collaborations between computational and molecular biologists building refined molecular biological knowledge and tools. A developmental process - early embryo segmentation in Drosophila melanogaster - appears ripe for attack. The network is well-characterized and a wealth of functional data is available on the individual components, including DNA binding preferences and cellular resolution expression patterns of critical TFs. The requisite experimental techniques are scalable to process many sequenced fly genotypes. Abundant genetic variation in expression, timing, and morphology during embryo development are well-documented. Building the first mechanistic model of the embryo genotype-to-phenotype map is our focus, but this will have a strong impact on the medical field. Success in developing these integrated approaches will enable optimal choice of targets for therapeutic interventions to restore network function in disease. The concepts and tools we establish will serve as a template for analysis of complex networks relevant to human health.

描述（由申请人提供）：由于许多物种已有数百个基因组测序，现在的挑战在于为基因型-表型图谱建立预测模型。数以百万计的多态碱基使我们每个人在形态、智力和心理上都是独一无二的。将全基因组多态性与无数表型（GWAS）联系起来的方法一直很流行。它依赖于纯粹的统计关联，需要筛选成千上万的个体，以确定通常可以解释部分自然变异的可察觉的（尽管是适度的）等位基因。下一步——这个项目的长期目标——是从关联转向因果关系；其中一个被充分理解的分子途径的模型被修改，单独为每个基因型，以反映其独特的多态性集的功能效应。我们利用果蝇开发必要的概念和模型来推进这一目标，其中分子工具是精确的，定量预测是可验证的。我们将开发几个层次的预测模型。首先，我们将基于转录因子（TF）结合位点的知识和序列如何影响DNA形状的预测模型，预测单核苷酸多态性对基因表达的功能后果。这些模型将通过顺式eqtl方法和直接测量表达和TF结合进行验证。其次，编码多态性和调控多态性的复合效应将被纳入网络级结构方程模型（SEM）。我们将用在多个基因型中收集的两种类型的表达数据来拟合模型，并预测和实验验证未测量多态性的功能后果。第三，该模型将被扩展到包含假定的上位相互作用，使用近似贝叶斯计算估计。这将概括和“量化”SEM，并评估下游表型对不同层次分子扰动的敏感性。我们将使用群体遗传数据验证这些预测。虽然概念上很简单，但开发这个框架需要计算生物学家和分子生物学家之间的密切合作，建立完善的分子生物学知识和工具。一个发育过程——黑腹果蝇的早期胚胎分裂——似乎已经成熟，可以攻击了。该网络具有良好的特征，并且可以获得关于单个组件的丰富功能数据，包括DNA结合偏好和关键tf的细胞分辨率表达模式。必要的实验技术是可扩展的，以处理许多测序的苍蝇基因型。胚胎发育过程中丰富的基因表达、时间和形态变异是有充分证据的。建立胚胎基因型-表型图谱的第一个机制模型是我们的重点，但这将对医学领域产生强烈的影响。开发这些综合方法的成功将使治疗干预的最佳目标选择成为可能，以恢复疾病中的网络功能。我们建立的概念和工具将作为分析与人类健康有关的复杂网络的模板。