Multi-scale modeling of genetic variation in a developmental network

描述（由申请人提供）：随着数百个测序的基因组可用于许多物种，现在的挑战在于建立预测模型的基因型表型地图。数以百万计的多态碱基使我们每个人在形态上、智力上和心理上都是独一无二的。将全基因组多态性与无数表型相关联的方法（GWAS）已经流行。它依赖于纯粹的统计学关联，需要筛选成千上万的个体，以确定等位基因，这些等位基因通常可以解释可观的自然变异，尽管这些变异并不多。下一步-该项目的长期目标-是从关联转移到因果关系;其中对每个基因型单独修改了一个已充分理解的分子途径模型，以反映其独特的多态性集的功能效应。我们开发了必要的概念和模型，以推进这一目标使用果蝇，其中分子工具是精确的和定量的预测是可验证的。我们将开发几个层次的预测模型。首先，我们将根据转录因子（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。这些模型将用顺式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复合效应将被纳入网络水平的结构方程模型（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，该模型将扩展到包括假定上位相互作用，估计使用近似贝叶斯计算。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和分子生物学家之间的密切合作，以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割--似乎已经成熟，可以攻击了。该网络的特点是，丰富的功能数据是可利用的个别组件，包括DNA结合的偏好和细胞分辨率表达模式的关键TF。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。在胚胎发育过程中，在表达、时间和形态上的丰富遗传变异是有据可查的。建立第一个胚胎基因型-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能够为治疗干预措施选择最佳靶点，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。

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.