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: 8554281
• 负责人: Angela H DePace
• 金额: $ 50万
• 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/8554281
• 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; 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）：数百个测序基因组可用于许多物种，现在的挑战在于为基因型到表型图构建预测模型。数百万的多态性基础使我们每个人在智力，心理上独特。将全基因组多态性与多种表型（GWAS）相关联的方法已经处于时尚状态。它依靠纯粹的统计关联需要筛选数千个个体来查明等位基因，这些等位基因通常可以解释自然变化的明显（尽管谦虚）。下一步 - 该项目的长期目标 - 是从关联到因果关系；对于每个基因型，对良好理解的分子途径模型进行了修改，以反映其独特的多态性集合的功能效应。我们开发了使用果蝇精确的果蝇且可验证的定量预测所需的概念和模型来促进这一目标。我们将开发几个级别的预测模型。首先，我们将基于转录因子（TF）结合位点的知识（TF）结合位点的知识以及序列如何影响DNA形状的预测模型，预测SNP对基因表达的功能后果。这些模型将通过CIS-EQTL方法和表达和TF结合的定向测量进行验证。其次，编码和调节多态性的综合效应将纳入网络级结构方程模型（SEM）中。我们将使用在多种基因型中收集的两种类型的表达数据拟合模型，并预测和实验验证未测量的多态性的功能后果。第三，该模型将扩展到使用近似贝耶斯计算估计的推定的上皮相互作用。这将概括和“定量” SEM，并评估下游表型对不同层分子扰动的敏感性。我们将使用人群遗传数据验证这些预测。虽然从概念上讲简单，但开发该框架需要计算和分子生物学家建立精致的分子生物学知识和工具之间的密切合作。一个发育过程 - 果蝇中早期的胚胎分割 - 攻击似乎已经成熟。该网络是特征良好的，并且可以在单个组件上获得大量功能数据，包括DNA结合偏好和临界TF的细胞分辨率表达模式。必要的实验技术可扩展以处理许多测序的蝇类基因型。胚胎发育过程中表达，时机和形态的丰富遗传变异已得到充分记录。我们的重点是建立胚胎基因型与表型图的第一个机械模型，但这将对医疗领域产生强大的影响。开发这些综合方法的成功将使治疗干预措施的最佳目标选择以恢复疾病的网络功能。我们建立的概念和工具将作为分析与人类健康相关的复杂网络的模板。