Genome-wide scan for context-dependent associations with ischemic heart disease and high density lipoprotein levels

全基因组扫描与缺血性心脏病和高密度脂蛋白水平的背景相关性

• 批准号: 8837909
• 负责人: Srilakshmi Raj
• 金额: $ 5.6万
• 依托单位: CORNELL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-01 至 2017-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8837909
• 关键词: American; Animal Model; Atherosclerosis; Communities; Complex; Data; Data Set; Dependence; Diagnostic; Disease; Environment; Environmental Risk Factor; European; European Union; Female; Gene Combinations; Genes; Genetic Variation; Genetic study; Genome; Genome Scan; Genotype; Goals; Health; Heterogeneity; High Density Lipoproteins; Human; Hypertension; Individual; Knowledge; Linear Models; Maps; Methods; Modeling; Myocardial Ischemia; Nature; Obesity; Patients; Pattern; Phenotype; Plant Model; Play; Population; Procedures; Property; Research; Risk; Risk Factors; Role; Sampling; Series; Shapes; Signal Transduction; Site-Directed Mutagenesis; Smoking; Sorting - Cell Movement; Testing; Training; Validation; Variant; Work; base; cardiovascular disorder risk; clinically relevant; cohort; diet and exercise; disease phenotype; disorder risk; gene environment interaction; genetic association; genetic variant; genome sequencing; genome-wide; genome-wide analysis; heart disease risk; improved; interest; male; rare variant; risk variant; simulation; skills; software development; sound; trait

DESCRIPTION (provided by applicant): Extensive research in plants, model organisms, and humans has demonstrated the near ubiquity of gene-environment (GxE) interactions in complex trait variation. Human geneticists have a long list of genetic variants that define an elevated disease risk whose manifestation is only triggered under particular environmental insults. What is lacking is the application of a sound statistical framework for uncovering, at a genome-wide scale, the role of environmental context in shaping the manifestation of genetic variation. As popular as mixed linear models are for problems of this sort, many of the assumptions of these models are violated in a way that can both reduce the power of the tests and generate false positives. I propose to develop and apply strategies for detecting SNP-disease associations in an environmental context using the Patient Rule Induction Method (PRIM). The PRIM approach not only offers a way to discover context-dependent genotype effects, but with a view towards improving prediction of the phenotype through assessment of the impact of context dependence on disease risk. I will develop extensions of PRIM for performing genome-wide scans for context dependence, and through extensive simulations I will test and validate the approach. This work will aim to identify both common and rare genetic variants that play a role in ischemic heart disease and high density lipoprotein levels in different environmental contexts (such as smoking, high blood pressure, obesity). In instances of strong context-dependence, targeted modification of environmental risks for individuals who harbor risk alleles may be especially efficacious to reduce the risk of disease.

描述（由申请人提供）：在植物、模式生物和人类中的广泛研究表明，在复杂的性状变异中，基因-环境（GxE）相互作用几乎无处不在。人类遗传学家有一长串遗传变异，这些变异定义了一种升高的疾病风险，其表现形式仅在特定的环境损伤下触发。目前缺乏的是应用一个健全的统计框架，在全基因组范围内揭示环境背景在形成遗传变异表现方面的作用。尽管混合线性模型在这类问题中很受欢迎，但这些模型的许多假设都被违反了，这既会降低测试的功效，又会产生误报。我建议开发和应用策略检测SNP-疾病协会在环境中使用患者规则归纳法（PRIM）。PRIM方法不仅提供了一种发现背景依赖性基因型效应的方法，而且通过评估背景依赖性对疾病风险的影响来改善表型的预测。我将开发PRIM的扩展，用于对上下文依赖进行全基因组扫描，并通过广泛的模拟来测试和验证该方法。这项工作旨在确定在不同环境背景下（如吸烟，高血压，肥胖）在缺血性心脏病和高密度脂蛋白水平中发挥作用的常见和罕见遗传变异。在强情境依赖性的情况下，针对携带风险等位基因的个体的环境风险的有针对性的修改可能对降低疾病风险特别有效。