Dissecting complex traits with diverse resources

用不同的资源剖析复杂的特征

• 批准号: 7286390
• 负责人: Charles L Kooperberg
• 金额: $ 26.82万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-13 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7286390
• 关键词: Accounting; Alcohol consumption; Algorithms; Case-Control Studies; Complex; Computational Technique; Data; Data Analyses; Data Set; Data Sources; Development; Disease; Disease Outcome; Environment; Environmental Risk Factor; Epidemiologic Studies; Epistatic Gene; Etiology; Evaluation; Family; Family member; Genes; Genetic; Genetic Epistasis; Genetic Variation; Genome; Genotype; Goals; Haplotypes; Hybrids; Individual; Intervention; Investigation; Methods; Nature; Numbers; Parents; Pathogenesis; Pharmaceutical Preparations; Play; Population; Population Study; Prevention; Procedures; Relative (related person); Research; Research Design; Research Personnel; Resources; Risk; Role; Sample Size; Sampling; Scheme; Single Nucleotide Polymorphism; Site; Smoking; Source; Staging; Statistical Methods; Statistical Models; Stratification; Testing; Therapeutic Intervention; base; case control; cost; cost effectiveness; design; gene environment interaction; gene interaction; genetic association; genome wide association study; high throughput technology; innovation; interest; predictive modeling; programs; trait

DESCRIPTION (provided by applicant): The conduct of genome-wide association studies involving hundreds of thousands of Single Nucleotide Polymorphisms (SNPs) requires both innovative study design and statistical analysis. The objective of this application is to develop statistical methods and computationally efficient algorithms which best utilize the diverse data resources and strengths of different study designs. For many association studies interest will not just be limited to the characterization of individual SNPs or haplotypes that are associated with a disease outcome, but importantly will include the identification of interactions either between SNPs within a gene as in haplotype effect, between genes (epistasis), or between gene and environment such as drugs, smoking, and alcohol consumption. The first aim of this application involves the investigation of situations, including designs, where it is possible to identify different types of interactions as well construct predictive models based on several single SNPs or haplotypes. The proposed statistical methods will use stage-wise or regularization strategies to carefully control for statistical over-fitting in the context of high-dimensional SNP data. It is also important to recognize that study designs play a critical role in this setting. Two common study designs for association studies are population-based case-control and family-based designs. The population-based case-control design is popular because it is cost-effective, but it can be sensitive to population stratification. Family-based studies using family members as controls are more robust and allow for the evaluation of maternal or parent-of-origin effects on the disease. However they could potentially be inefficient due to over-matching in genotypes. Sampling ascertainment biases could also substantially complicate the analysis. For these reasons, conducting hybrid association studies using both designs can strengthen the power for detecting disease associated SNPs. The second aim of this application is to develop unified statistical estimation and inference procedures for combining resources, taking into account different ascertainment schemes and potential bias due to population stratification. Particularly we focus on the methods that can be easily adapted for high-dimensional SNP data by exploiting the computational techniques developed in the first aim.

描述（由申请人提供）：全基因组关联研究的进行涉及数十万个单核苷酸多态性（SNP）需要创新的研究设计和统计分析。该应用程序的目的是开发统计方法和计算有效的算法，这些算法最能利用不同研究设计的多种数据资源和优势。对于许多关联研究而言，感兴趣的不仅限于与疾病结果相关的单个SNP或单倍型的表征，而且重要的是，将包括基因内SNP之间的相互作用，例如基因（Epistasis）或基因和环境之间的单倍型效应，例如药物，吸烟和酒精消耗。该应用程序的第一个目的涉及对包括设计在内的情况进行调查，可以在其中识别不同类型的相互作用，并基于几种单个SNP或单倍型构建预测模型。所提出的统计方法将使用阶段或正则化策略在高维SNP数据的背景下仔细控制统计过度拟合。同样重要的是要认识到研究设计在这种情况下起着至关重要的作用。协会研究的两种常见研究设计是基于人群的病例对照和基于家庭的设计。基于人群的病例对照设计很受欢迎，因为它具有成本效益，但可能对人口分层敏感。使用家庭成员作为对照的家庭研究更加稳健，并允许评估产妇或父母对疾病的影响。但是，由于基因型过度匹配，它们可能会效率低下。采样确定偏见也可能使分析变得复杂。由于这些原因，使用这两种设计进行混合关联研究可以增强检测疾病相关SNP的能力。该应用程序的第二个目的是考虑不同的确定方案和由于人口分层引起的不同确定性方案和潜在偏见，开发统一的统计估计和推理程序来组合资源。特别是我们专注于可以通过利用第一个目标中开发的计算技术来轻松适应高维SNP数据的方法。