Relaxing genetic models to identify genetic variants involved in gene-gene and ge

放宽遗传模型来识别基因-基因和基因相关的遗传变异

• 批准号: 8444137
• 负责人: Hugues Aschard
• 金额: $ 8.08万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-12-19 至 2014-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8444137
• 关键词: Alleles; Biological Markers; Carbon; Characteristics; Complex; Data; Detection; Educational workshop; Environmental Exposure; Environmental Risk Factor; Genes; Genetic; Genetic Epistasis; Genetic Models; Genetic Risk; Heritability; Individual; Investigation; Joints; Location; Metabolism; Methodology; Methods; Modeling; Nurses' Health Study; Pathway interactions; Phenotype; Publishing; Quantitative Trait Loci; Reporting; Research Proposals; Risk; Risk Factors; Shapes; Simulate; Testing; United States National Institutes of Health; Variant; Work; base; clinical risk; design; gene environment interaction; genetic epidemiology; genetic risk factor; genetic variant; genome wide association study; genome-wide; innovation; knowledge base; meetings; method development; non-genetic; public health relevance; success; trait

DESCRIPTION (provided by applicant): Despite current enthusiasm for investigation of gene-gene and gene-environment interactions, very few discoveries have been published. The identification of interactions or at least genetic and non-genetic factors involved in these interactions remains one of the greatest challenges in genetic epidemiology. Multiple testing issues, unavailability of environmental exposure and high-order interactions are commonly cited as fundamental problems in identifying these interactions. However alternative strategies are possible in the case of quantitative phenotypes when the aim is not to identify interaction effect per se but rather to detect quantitative trait loci (QTLs) involved in interaction. Most association tests of marginal effect are designed to capture a shift in phenotypic values on a particular location (e.g. the phenotypic mean) in individuals carrying a risk allele as compared to individuals that do not. However, in a situation where a QTL interacts with unknown risk factors, phenotypic differences between carrier and non-carrier can be expressed by differences in the shape of the distribution of phenotypic values rather than differences on a single location. We propose a non-parametric test of association that leverages such information. By construction, the test uses only the phenotypic and genotypic information and does not require information on the (possibly unknown) interacting factors. A preliminary study on simulated data shows that the proposed test can be more powerful than the test of the standard test of marginal linear effect in the presence of a strong interaction effect or in the presence of stratum-specific effects in opposite directions. In this project, we aim first to condut additional theoretical work including power comparison with other methods under different interaction models and identification of technical improvements. Second, the proposed approach will be applied in real genome-wide data to identify genetic variants associated with four biomarkers in the one-carbon metabolism pathway. Finally, extension of this method for multi-markers analysis will be explored. This method development project is responding to the need for new and innovative methods for detection of genetic variants involved in interactions that has been recently highlighted by multiple reports including a recent NIH workshop. The characterization and the application of the proposed method in GWAS data will be a step toward the discovery of these variants.

描述（由申请人提供）： 尽管目前人们对基因-基因和基因-环境相互作用的研究充满热情，但发表的发现却很少。识别相互作用或至少识别这些相互作用所涉及的遗传和非遗传因素仍然是遗传流行病学中最大的挑战之一。多重测试问题、环境暴露的不可用性和高阶相互作用通常被认为是识别这些相互作用的基本问题。然而，在数量表型的情况下，当目的不是确定相互作用效应本身而是检测参与相互作用的数量性状基因座（QTL）时，替代策略是可能的。大多数边际效应关联检验旨在捕获携带风险等位基因的个体与不携带风险等位基因的个体相比在特定位置的表型值（例如表型平均值）的变化。然而，在QTL与未知风险因素相互作用的情况下，携带者和非携带者之间的表型差异可以通过表型值分布形状的差异来表达，而不是通过单个位置的差异来表达。我们提出了利用此类信息的非参数关联测试。通过构建，该测试仅使用表型和基因型信息，不需要有关（可能未知）相互作用因素的信息。对模拟数据的初步研究表明，在存在强相互作用效应或存在相反方向的特定层效应的情况下，所提出的检验比边际线性效应标准检验的检验更有效。在这个项目中，我们的目标首先是进行额外的理论工作，包括在不同交互模型下与其他方法进行功率比较以及识别技术改进。其次，所提出的方法将应用于真实的全基因组数据，以识别与一碳代谢途径中的四种生物标志物相关的遗传变异。最后，将探讨该方法在多标记分析中的扩展。该方法开发项目正在满足对检测涉及相互作用的遗传变异的新的和创新的方法的需求，最近包括最近的 NIH 研讨会在内的多份报告都强调了这一点。所提出的方法在 GWAS 数据中的表征和应用将是朝着发现这些变异迈出的一步。