Building, understanding and applying a dictionary of genetic effects

描述(由申请人提供)：这个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，即对基因-表型(GP)图的影响。基因类型在一个离散的空间中存在和遗传，便于进行多种分析，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，而表型空间与基因空间的关系仅被模糊地掌握。仅仅直接研究基因组，对表型的参考很少，不足以理解为什么有些人生病了，有些人是健康的。这个项目采用了一种综合的方法来研究GP图谱，结合了可能相互加强的遗传和表型研究。果蝇的翅膀形状是一个很好的模型系统，因为它的表型可以很容易地在多个维度上表征，存在大量的自然遗传变异，而且由于果蝇可能的基因控制。该项目的目标1是通过对基因表达的受控操作来表征在大量和多样化的基因中基因表达的变异的影响。AIM 2将开发一个包含这些结果的机翼发育模型，并产生一个完整的机翼表型，这是很少有人尝试的。目标3是通过对翅膀的多变量效应的关联作图来识别翅膀表型中自然发生的遗传变异。通过同时考虑分析中的所有表型，将避免一次映射多个性状的问题。在目标4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，来测试目标1-3中关于翅膀形状变化原因的新的概括。如果从这些实验中出现了关于遗传变异如何产生表型变异的连贯图景，那么遗传操作、详细的建模以及对自然变异及其影响的表征的组合可以很容易地应用于哺乳动物系统，并最终应用于人类。如果没有出现这样的图景，预测失败的准确方式将把注意力集中在我们还不了解的GP图的那些方面。 公共卫生相关性：这项工作将测试详细的表型数据是否可以与现有的基因组数据相结合，以提供对重要事件的更好预测，如疾病状态或结果。目前的研究强调仅使用基因分型数据进行预测。最近的结果表明，遗传致因的性质非常复杂，并且受到环境的高度影响，严重限制了单独使用基因方法对健康的有效性。如果基因组数据与表型的全面特征和遗传变异如何影响这些表型的详细知识相结合，这些问题可能会减少。

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.