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

基本信息

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
项目类别：
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

项目摘要

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）：由于许多物种有数百个已测序的基因组，现在的挑战在于建立基因型到表型图谱的预测模型。数以百万计的多态性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多种表型（GWAS）关联起来的方法已经很流行。它对纯粹统计关联的依赖需要筛选成千上万的个体来查明等位基因，这些等位基因通常可以解释自然变异中可观的（尽管适度的）部分。下一步——该项目的长期目标——是从关联转向因果关系；其中针对每种基因型单独修改了众所周知的分子途径模型，以反映其独特的多态性集的功能效应。我们利用果蝇开发了推进这一目标所需的概念和模型，其中分子工具是精确的，定量预测是可验证的。我们将开发几个级别的预测模型。首先，我们将根据转录因子 (TF) 结合位点的知识以及序列如何影响 DNA 形状的预测模型，仅从序列中预测 SNP 对基因表达的功能影响。这些模型将通过 cis-eQTL 方法以及表达和 TF 结合的定向测量进行验证。其次，编码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）中。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的多态性的功能后果。第三，该模型将扩展到包含假定的上位相互作用，使用近似贝叶斯计算进行估计。这将概括和“定量”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，构建完善的分子生物学知识和工具。黑腹果蝇的一个发育过程——早期胚胎分割——似乎已经准备好受到攻击了。该网络具有良好的特征，并且可以获取有关各个组件的大量功能数据，包括 DNA 结合偏好和关键 TF 的细胞分辨率表达模式。必要的实验技术可扩展以处理许多已测序的果蝇基因型。胚胎发育过程中表达、时间和形态方面的大量遗传变异已得到充分记录。建立第一个胚胎基因型到表型图谱的机制模型是我们的重点，但这将对医学领域产生重大影响。成功开发这些综合方法将使治疗干预目标的最佳选择成为可能，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相关的复杂网络的模板。