Building, understanding and applying a dictionary of genetic effects

描述（由申请人提供）：本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。基因型存在于一个离散的空间中，并在这个空间中遗传，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却发生在一个连续的表型空间中，而表型空间与基因型空间的关系却只有模糊的认识。仅对基因组进行直接研究，很少涉及表型，不足以理解为什么有些人生病，有些人健康。该项目采用综合方法研究GP图谱，结合遗传和表型研究，可能相互加强。黑腹果蝇的翅形是一个很好的模型系统，因为表型可以很容易地在许多方面进行表征，存在大量的自然遗传变异，并且因为果蝇中可能存在遗传控制。该项目的目的1是通过对基因表达的控制操作来表征大量和多种基因的基因表达变异的影响。目标2将开发一个模型的翅膀发展，结合这些结果，并产生一个完整的翅膀表型，这是很少有人尝试。目的3是确定自然发生的遗传变异的翅膀表型的关联映射的多变量影响翅膀。通过在分析中同时考虑所有表型，将避免一次一个地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检验这些预测，来检验目标1-3中关于翼形变异原因的新概括。如果从这些实验中可以清楚地看到遗传变异如何产生表型变异，那么将遗传操作、详细建模和自然变异及其影响的特征描述结合起来，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还不了解的GP地图的那些方面。 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（如疾病状态或结果）的更好预测。目前的研究强调仅使用基因型数据进行预测。最近的研究结果表明，遗传因果关系的性质非常复杂，并且受环境的影响很大，这极大地限制了单靠基因来解决健康问题的有效性。如果基因组数据与表型的全面表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.