Gene expression and system-based analysis to predict gene-environment interaction

基因表达和基于系统的分析来预测基因与环境的相互作用

• 批准号: 8217821
• 负责人: Laurence Parnell
• 金额: $ 12.92万
• 依托单位: U.S. AGRICULTURAL RESEARCH SERVICE
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-02-16 至 2015-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8217821
• 关键词: Adoption; Affect; Alcohol consumption; Alcohols; Alleles; Altitude; Behavior; Bioinformatics; Blood Pressure; Body mass index; Caloric Restriction; Carbohydrates; Cardiovascular Diseases; Clinical; Code; Complex; Computational Biology; Coupled; Cues; Data; Data Set; Diabetes Mellitus; Diet; Dietary Component; Dietary Fats; Disease; Dyslipidemias; Elements; Environment; Environmental Exposure; Environmental Risk Factor; Exclusion; Exercise; Exhibits; Exons; Experimental Designs; Fatty acid glycerol esters; Gene Expression; Gene Expression Regulation; Gene Proteins; Genes; Genetic; Genetic Risk; Genetic Transcription; Genetic Variation; Genome; Genomics; Genotype; Glucose; Goals; Health Status; Heart; Heart Diseases; Heritability; Human; Hypertension; Individual; Insulin; Life Style; Linkage Disequilibrium; Lipids; Maps; Measures; Messenger RNA; Metabolic syndrome; Methodology; Methods; Mining; Nature; Network-based; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Participant; Pharmaceutical Preparations; Phenotype; Physical activity; Plasma; Population; Population Genetics; Population Statistics; Process; Proteins; Publishing; Qualifying; Quantitative Trait Loci; Research; Research Institute; Rodent; Role; Scientist; Series; Signal Transduction; Single Nucleotide Polymorphism; Sleep; Smoking; Stimulus; Stroke; System; Systems Biology; Techniques; Testing; To specify; Tobacco use; Variant; Work; alcohol exposure; anticancer research; base; blood lipid; combat; design; diet and exercise; dietary restriction; disease phenotype; disorder risk; gene environment interaction; genetic association; genetic element; genetic variant; genome wide association study; genome-wide; human disease; human population genetics; innovation; novel; nutritional genomics; protein protein interaction; protein structure function; rapid detection; research study; residence; response; trait

DESCRIPTION (provided by applicant): Although results of many genetic association studies have identified numerous gene variants involved in disease risk, recognition of the complex interaction between genome and environment is now more common. Such gene-environment (GxE) interactions show allele-specific alteration of disease risk and likely often act by affecting gene expression in response to key environmental factors (EF) such as diet, exercise and alcohol and tobacco use. Hence, this study's short- term goal is to use bioinformatics to prioritize genetic variants with a strong likelihood of responding to dietary components, physical activity, or alcohol use in an allele-specific manner based on analysis of gene expression data and gene/protein interaction networks. Three specific aims are proposed: One, identify putative GxE interaction SNPs by merging genes with published expression QTL with genes showing consistent altered expression in published experiments centered on specific environmental challenges, e.g. high-fat diet or caloric restriction. Two, identify genes with strong likelihood to exhibit GxE interactions by building gene/protein networks seeded by genes harboring SNPs directing allele- specific interactions to important phenotypes or EFs: diet, exercise, or alcohol or smoking use. Three, test for actual GxE interactions in two deeply phenotyped populations (Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) and Framingham Heart (FHS)) using genes/SNPs prioritized in Aims 1 & 2. While GxE interactions are known and their role in disease risk is accepted as more commonplace, methods are lacking for rapid detection across a wide range of common environmental exposures. The work proposed here is significant because it describes and assesses two methods for quick and efficient prioritization of genetic variants with high likelihood of partaking in GxE interactions relevant to heart disease, diabetes, hypertension and obesity. Adoption of genomics data to predict novel GxEs based on computational approaches is lacking. Thus, two aspects that, in our opinion, qualify this proposal as innovative are its application of systems biology with gene networks and mining of gene expression data to identify genes most responsive to a given EF, where variants of those genes are likely GxE participants. This innovation arises from leveraging gene behavior (expression changes after EF challenge or interacting partners in a network) filtered through genetic variants (eQTL, GxE- based networks) to prioritize SNPs for the GxE interaction test. This proposal will use integrated genomics methodology to identify putative GxE variants, which will be based on merging large, genome-wide datasets with subsequent filtering to identify the genes with the most/best attributes. In this case, eQTL genes give a genetic context to active genes and genes with consistent mRNA changes are those responding to an environmental cue while elements within the EF-specific networks become candidates for further analysis and bottlenecks are critical regulators of information flow. The proposed research will be performed within our group of scientists who are skilled in computational biology, human population genetics and statistics and are leaders in the field of nutrigenomics at a world-renown research institute. Also, we have access to two key populations deeply phenotyped for both clinical measures of health status and lifestyle choices of diet, exercise and alcohol/tobacco use. PUBLIC HEALTH RELEVANCE: This study proposes to identify genetic variants, mainly single nucleotide polymorphisms that are likely to participate in gene-environment interactions for phenotypes relevant to metabolic syndrome: blood lipids, blood pressure, obesity (body mass index) and plasma glucose and insulin levels. This study, of original and novel design, will use expression QTL SNPs and gene expression changes induced by EF exposure coupled with gene networks built with genes participating in specific types of published GxE interactions in order to identify new putative GxE SNPs. Those SNPs will be tested with genotyping data available from two deeply phenotyped populations. This project will provide techniques enabling our understanding of how widespread afflictions such as cardiovascular disease, type 2 diabetes and hypertension/stroke are and to what extent the genetic risk of these sicknesses is modulated by environmental factors. In essence, this study will help to define how the genome senses and responds to diet, exercise and alcohol/tobacco use.

描述（由申请人提供）：虽然许多遗传关联研究的结果已经确定了许多涉及疾病风险的基因变异，但现在对基因组与环境之间复杂相互作用的认识更为普遍。这种基因-环境（GxE）相互作用显示出等位基因特异性的疾病风险改变，并且可能经常通过影响基因表达来响应关键环境因素（EF），如饮食，运动和酒精和烟草使用。因此，本研究的短期目标是使用生物信息学，基于基因表达数据和基因/蛋白质相互作用网络的分析，以等位基因特异性方式对饮食成分、体力活动或酒精使用做出反应的可能性很大的遗传变异进行优先排序。提出了三个具体目标：一，通过将具有已发表的表达QTL的基因与在以特定环境挑战为中心的已发表实验中显示一致改变表达的基因合并来鉴定推定的GxE相互作用SNP，例如高脂饮食或热量限制。第二，通过构建基因/蛋白质网络来鉴定具有表现出GxE相互作用的强烈可能性的基因，所述基因/蛋白质网络由携带SNP的基因播种，所述SNP将等位基因特异性相互作用引导至重要的表型或EF：饮食、运动或酒精或吸烟使用。第三，使用目标1和2中优先考虑的基因/SNP，在两个深度表型人群（降脂药物和饮食网络遗传学（GOLDN）和心脏病（FHS））中测试实际GxE相互作用。虽然GxE相互作用是已知的，并且它们在疾病风险中的作用被认为是更常见的，但缺乏在广泛的常见环境暴露中快速检测的方法。这里提出的工作是重要的，因为它描述和评估了两种方法，用于快速有效地优先考虑与心脏病，糖尿病，高血压和肥胖相关的GxE相互作用的遗传变异。采用基因组学数据来预测基于计算方法的新型GxE是缺乏的。因此，在我们看来，有两个方面，有资格这个建议是创新的系统生物学与基因网络和挖掘基因表达数据的应用，以确定最响应于给定的EF基因，这些基因的变体可能是GxE参与者。这种创新源于利用通过遗传变体（eQTL，基于GxE的网络）过滤的基因行为（EF挑战后的表达变化或网络中的相互作用伙伴）来优先考虑用于GxE相互作用测试的SNP。该提案将使用综合基因组学方法来识别推定的GxE变体，该方法将基于合并大型全基因组数据集，随后进行过滤以识别具有最多/最佳属性的基因。在这种情况下，eQTL基因为活性基因提供了遗传背景，具有一致mRNA变化的基因是那些响应环境线索的基因，而EF特异性网络内的元素成为进一步分析的候选者，瓶颈是信息流的关键调节器。拟议的研究将在我们的科学家小组内进行，他们精通计算生物学，人类群体遗传学和统计学，并且是世界知名研究机构营养基因组学领域的领导者。此外，我们有机会接触两个关键人群，他们的健康状况和饮食，运动和酒精/烟草使用的生活方式选择的临床指标都进行了深入的表型分析。 公共卫生相关性：本研究旨在确定可能参与代谢综合征相关表型的基因-环境相互作用的遗传变异，主要是单核苷酸多态性：血脂，血压，肥胖（体重指数）和血糖和胰岛素水平。这项研究，原始和新颖的设计，将使用表达QTL SNPs和基因表达变化诱导EF暴露加上基因网络建立与基因参与特定类型的GxE相互作用，以确定新的推定GxE SNPs。这些SNP将使用来自两个深度表型人群的基因分型数据进行测试。该项目将提供技术，使我们能够了解心血管疾病，2型糖尿病和高血压/中风等疾病的普遍程度，以及这些疾病的遗传风险在多大程度上受到环境因素的影响。从本质上讲，这项研究将有助于确定基因组如何感知和响应饮食，运动和酒精/烟草的使用。