Building, understanding and applying a dictionary of genetic effects

建立、理解和应用遗传效应词典

• 批准号: 8323451
• 负责人: Ian Michael Dworkin
• 金额: $ 41.78万
• 依托单位: FLORIDA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-25 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8323451
• 关键词: Address; Affect; Architecture; Attention; Biological Models; Biology; Complex; Coupled; Data; Development; Developmental Process; Dictionary; Dimensions; Disease; Drosophila genus; Drosophila melanogaster; Environment; Etiology; Event; Evolution; Frequencies; Gene Expression; Genes; Genetic; Genetic Variation; Genome; Genomics; Genotype; Growth; Health; Hereditary Disease; Human; Inbreeding; Individual; Inherited; Knowledge; Maps; Mechanics; Modeling; Natural Selections; Nature; Neighborhoods; Outcome; Phenotype; Population; Property; Quantitative Genetics; Research; Sampling; Series; Shapes; Simulate; Sorting - Cell Movement; System; Testing; Time; Variant; Veins; Wing; Work; cohort; data modeling; disorder risk; genetic association; genetic manipulation; genetic resource; genetic variant; grasp; improved; innovation; insight; novel strategies; offspring; research study; response; trait

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.

描述（由申请人提供）：该项目将实施一种新的方法来了解遗传变异及其对基因型 - 表型（GP）图的影响之间的关系。基因型存在并在离散空间中遗传方便的许多分析，但是重要现象（例如疾病状态和自然选择）的因果关系发生在连续的表型空间中，其与基因型空间的关系仅被昏昏欲睡。直接研究基因组，而对表型的参考很小，不足以理解为什么有些人生病而有些人很健康。该项目对GP图的研究采用了综合方法，结合了潜在地相互加强的遗传和表型研究。果蝇中的机翼形状是为此的出色模型系统，因为表型可以很容易地在许多维度上表征，并且存在实质性的自然遗传变异，并且由于果蝇中可能的遗传控制可能。该项目的目的1是通过控制基因表达的受控操纵来表征大量基因和多种基因上基因表达变异的影响。 AIM 2将开发一个翼发展模型，该模型结合了这些结果并产生完整的机翼表型，这很少尝试。目的3是使用多元对机翼的效应的关联映射来鉴定机翼表型中自然存在的遗传变异。通过同时考虑分析中的所有表型，将避免一次绘制多个特征的问题。在AIM 4中，将通过预测种群应如何响应人工选择，然后在一系列实验中测试这些预测，从而测试有关翼形式变化原因的新出现的概括。如果从这些实验中出现遗传变异如何产生表型变异的一致图片，那么遗传操作，详细建模和自然变异表征及其效果的结合很容易应用于哺乳动物系统，最终将其应用于人类。如果没有出现这样的图片，那么预测失败的确切方法将把注意力集中在我们尚未理解的GP地图的那些方面。 公共卫生相关性：这项工作将测试是否可以将详细的表型数据与现有基因组数据结合使用，以改善对疾病状况或结果等重要事件的预测。当前的研究强调仅使用基因型数据进行预测。最近的结果表明，遗传因果的性质非常复杂，并且受环境的影响很大，从而极大地限制了一种独立基因的健康方法的功效。如果将基因组数据与表型的全面表征相结合，并详细了解遗传变异如何影响这些表型，则可能会减少这些问题。