Genetic Basis of Genotype-by-Environment Interactions Underlying Physiological Mo

生理学中基因型与环境相互作用的遗传基础

• 批准号: 8296268
• 负责人: Alison Motsinger-Reif
• 金额: $ 24.17万
• 依托单位: UNIVERSITY OF ALABAMA IN TUSCALOOSA
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-05 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8296268
• 关键词: Alleles; Back; Biological Models; Carbohydrates; Cardiovascular Diseases; Chromosome Mapping; Collection; Communities; Complex; Complex Genetic Trait; Development; Diet; Disease; Drosophila genus; Drosophila melanogaster; Environment; Failure; Fatty acid glycerol esters; Frequencies; Gene Expression; Genes; Genetic; Genetic Complementation Test; Genetic Models; Genetic Variation; Genome; Genomics; Genotype; Goals; Heterozygote; Human; Inbreeding; Individual; Life Style; Link; Lipids; Maps; Measures; Metabolic Pathway; Metabolic syndrome; Microarray Analysis; Molecular Profiling; Non-Insulin-Dependent Diabetes Mellitus; North Carolina; Pathway interactions; Phenotype; Physiological; Population; Population Genetics; Population Sizes; Quantitative Trait Loci; RNA Interference; Recombinants; Resources; Robin bird; Sampling; Scheme; Site; Structure; Surveys; Symptoms; Testing; Time; Universities; Variant; Weight; Work; base; fly; follow-up; gene complementation; genetic variant; improved; metabolomics; research study; tool; trait

DESCRIPTION (provided by applicant): Diseases linked to Metabolic Syndrome (MetS) such at type-2 diabetes and cardiovascular disease are rapidly increasing due to the influences of a modern Westernized-life style, but the genetic, environmental, and physiological mechanisms linking the symptoms of Metabolic-syndrome remain to be elucidated. Large scale studies to systematically assess how genotype interacts with the environment to cause complex disease are very difficult in humans, but such studies are relatively tractable in genetic models systems such as Drosophila melanogaster. We have shown previously that that there is a very substantial contribution of genotype-by-environment interactions to the phenotypic variation observed for MetS-like symptoms in a naturally genetically variable population of D. melanogaster. We have also been able to demonstrate clear correlations between metabolomic and gene expression profiles and these symptoms as they vary across diet. Finally, we have shown that genetic variance in some of these traits increase with a perturbing high fat diet, indicating the exposure of cryptic genetic variation for these symptoms could contribute to increases in disease. In this study we will build off the community resources for complex genetic trait analysis of the Macdonald-Long synthetic recombinant inbred line (RIL) population and the Drosophila Genomic Reference Panel (DGRP) to map the genetic basis of genotype-by-diet interactions. First, using the 1700 Macdonald-Long Advanced Intercross synthetic RILs, we will map the genetic basis of MetS-like symptoms and the regions controlling genotype-by- environment interactions contributing to these symptoms to within 1 cM of the causal locus when the flies are raised on a "normal" verses "high fat" diet. We should be able to estimate both the effect size and population frequency of causative alleles. Second, based of the phenotypes measured in the F1 RIL population, 200 lines demonstrating the largest genotype-by-diet interaction effects will be selected for metabolomic and expression profiling. Metabolomic profiling will identify several hundred primary metabolite and whole genome expression profiles will be generated by microarray analysis. We will characterize the metabolomic and expression module structure that drives the genotype-by-environment interactions and link those pathways back to specific genetic variants. Finally, we will attempt to replicate the findings from the synthetic RIL population through association mapping in the natural variants represented in the 192 lines of the DGRP. The ultimate goal of this work is to identify genomic regions, metabolic pathways, physiological mechanisms, and dietary influences likely to be of importance to Metabolic Syndrome in humans.

描述（由申请人提供）：由于现代西方化生活方式的影响，与代谢综合征（MetS）相关的疾病如2型糖尿病和心血管疾病正在迅速增加，但与代谢综合征症状相关的遗传、环境和生理机制仍有待阐明。大规模的研究，以系统地评估基因型如何与环境相互作用，导致复杂的疾病是非常困难的人类，但这样的研究是相对容易的遗传模型系统，如果蝇。我们以前已经表明，有一个非常重要的贡献，基因型环境相互作用的表型变异观察到的MetS样症状在一个自然的遗传可变的人口D。黑腹菌我们还能够证明代谢组学和基因表达谱与这些症状之间的明确相关性，因为它们在饮食中各不相同。最后，我们已经表明，这些性状中的一些遗传变异随着令人不安的高脂肪饮食而增加，这表明这些症状的隐藏遗传变异的暴露可能有助于疾病的增加。在这项研究中，我们将建立社区资源的Macdonald-Long合成重组近交系（RIL）人口和果蝇基因组参考面板（DGRP）的复杂遗传性状分析，以映射基因型饮食相互作用的遗传基础。首先，使用1700个Macdonald-Long高级互交合成RIL，我们将绘制MetS样症状的遗传基础和控制基因型与环境相互作用的区域，这些区域在果蝇以“正常”与“高脂肪”饮食饲养时在因果位点的1 cM内促成这些症状。我们应该能够估计致病等位基因的效应大小和群体频率。其次，基于在F1 RIL群体中测量的表型，将选择200个表现出最大基因型-饮食相互作用效应的品系用于代谢组学和表达谱分析。代谢组学分析将鉴定数百种初级代谢物，并且将通过微阵列分析生成全基因组表达谱。我们将表征驱动基因型与环境相互作用的代谢组学和表达模块结构，并将这些途径与特定的遗传变异联系起来。最后，我们将试图复制的结果，从合成的RIL人口通过关联映射的自然变异中的192行的DGRP。这项工作的最终目标是确定基因组区域，代谢途径，生理机制和饮食影响可能是重要的代谢综合征在人类。