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）：涉及数十万个单核苷酸多态性（SNP）的全基因组关联研究的进行需要创新的研究设计和统计分析。该应用程序的目标是开发统计方法和计算高效的算法，以最好地利用不同的数据资源和不同研究设计的优势。对于许多关联研究来说，兴趣不仅限于与疾病结果相关的单个 SNP 或单倍型的表征，更重要的是包括识别基因内 SNP 之间的相互作用（如单倍型效应）以及基因之间的相互作用（上位性） ，或基因与环境（如药物、吸烟和饮酒）之间的关系。此应用程序的第一个目标涉及对情况（包括设计）的调查，其中可以识别不同类型的相互作用以及基于多个单个 SNP 或单倍型构建预测模型。所提出的统计方法将使用分阶段或正则化策略来仔细控制高维 SNP 数据背景下的统计过度拟合。同样重要的是要认识到研究设计在这种情况下发挥着关键作用。关联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，但它对人群分层很敏感。使用家庭成员作为对照的基于家庭的研究更加稳健，可以评估母亲或父母对疾病的影响。然而，由于基因型过度匹配，它们可能效率低下。抽样确定偏差也可能使分析变得更加复杂。由于这些原因，使用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标是开发统一的统计估计和推理程序来组合资源，同时考虑到不同的确定方案和由于人口分层而产生的潜在偏差。我们特别关注通过利用第一个目标中开发的计算技术可以轻松适应高维 SNP 数据的方法。