Building, understanding and applying a dictionary of genetic effects

描述（由申请人提供）：该项目将实施一种新的方法来理解遗传变异及其对表型的影响之间的关系，即基因型-表型（GP）图谱。基因型在一个离散的空间中存在和遗传，便于多种分析，但疾病状态和自然选择等重要现象的原因发生在一个连续的表型空间中，其与基因型空间的关系只能模糊地掌握。仅对基因组进行直接研究，很少参考表型，不足以理解为什么有些人生病，有些人健康。该项目采用一种综合的方法来研究GP图谱，将遗传和表型研究结合起来，这些研究可能会相互加强。黑腹果蝇的翅膀形状是一个很好的模型系统，因为表型可以很容易地在许多维度上表征，有大量的自然遗传变异，因为果蝇的遗传控制是可能的。该项目的目的1是通过控制基因表达的操作来描述基因表达在大量和多种基因中的变化的影响。Aim 2将开发一个结合这些结果的机翼发育模型，并产生一个完整的机翼表型，这是很少尝试的。目的3是确定自然发生的遗传变异的翅膀表型使用关联映射对翅膀的多变量影响。通过在分析中同时考虑所有表型，可以避免一次绘制多个性状的问题。在第4个目标中，将通过预测种群对人工选择的反应，然后在一系列实验中测试这些预测，来测试第1-3个目标中关于翅膀形状变化原因的新兴概括。如果从这些实验中得出遗传变异如何产生表型变异的连贯图景，那么将遗传操作、详细建模和自然变异及其影响的表征结合起来，就可以很容易地应用于哺乳动物系统，并最终应用于人类。如果没有这样的图景出现，预测失败的确切方式将把注意力集中在我们尚未理解的GP地图的那些方面。

DESCRIPTION (provided by applicant): This project will implement a novel approach to understanding the relationship between genetic variation and its effects on the phenotype that is to the genotype-phenotype (GP) map. Genotypes exist and are inherited in a discrete space convenient for many sorts of analyses, but the causation of the important phenomena such as disease status and natural selection takes place in a continuous phenotype space whose relationship to the genotype space is only dimly grasped. Direct study of genomes alone, with minimal reference to phenotypes is insufficient to understand why some individuals are sick and some are healthy. This project takes an integrative approach to the study of the GP map, combining both genetic and phenotypic studies that potentially reinforce each other. Wing shape in Drosophila melanogaster is an excellent model system for this because the phenotype can readily be characterized in many dimensions, there is substantial natural genetic variation, and because of the genetic control possible in Drosophila. Aim 1 of the project is to characterize the effects of variation in gene expression at a large number and variety of genes by controlled manipulations of gene expression. Aim 2 will develop a model of wing development that incorporates these results and produces a complete wing phenotype, something that has only rarely been attempted. Aim 3 is to identify naturally occurring genetic variation in wing phenotype using association mapping of multivariate effects on wings. By simultaneously considering all phenotypes in the analysis, the problems of mapping multiple traits one at a time will be avoided. In Aim 4 emerging generalizations about the causes of variation in wing form from Aims 1-3 will be tested by predicting how a population should respond to artificial selection, then testing these predictions in a series of experiments. If a coherent picture of how genetic variation produces phenotypic variation emerges from these experiments, the combination of genetic manipulations, detailed modeling, and characterization of natural variation and its effects can readily be applied to mammalian systems and ultimately to humans. If no such picture emerges, the precise ways in which predictions fail will focus attention on those aspects of the GP map that we do not yet understand. PUBLIC HEALTH RELEVANCE: This work will test whether detailed phenotypic data can be combined with existing genomic data to provide improved predictions of important events, such as disease status or outcome. Current research emphasizes using genotypic data alone for prediction. Recent results show that the nature of genetic causation is very complex, and highly influenced by the environment, strongly limiting the efficacy of a genes-alone approach to health. These problems may be lessened if genomic data is combined with a comprehensive characterization of the phenotype and detailed knowledge of how genetic variation affects those phenotypes.