Survival analysis methods in genetic studies

这个项目的长期目标是开发强大的和计算效率高的统计方法 识别基因，环境风险因素及其相互作用，这些因素是与 人类疾病和健康。该项目的具体目标是继续开展生存分析， 遗传学模型结合发病年龄数据，环境协变量信息，基因-基因和 基因与环境的相互作用，以及多个疾病位点的单倍型为基础的遗传关联分析， 单核苷酸多态性（SNPs）分析和群体中复杂性状的混合作图- 基于队列研究。该项目还评估了遗传关联研究中的不同研究设计。的 所提出的方法建立在现有的方法和铰链的新的整合方法的生存 分析，高维数据分析和人类遗传学方法。重点是 为单倍型分析、基因分析、基因表达分析和基因组学研究开发了严格和全面的统计推断程序。 基因和基因-环境交互作用分析和混合作图在非相关性 通过不同的研究设计收集的个体，包括病例队列和巢式病例对照设计。 基于可能性的推理，隐马尔可夫模型和阈值梯度下降方法将 为这些目标而发展。该项目还将调查这些系统的鲁棒性、功能和效率。 方法，并与现有方法进行比较。此外，该项目还将开发实用和 可行的计算机程序，以实现所提出的方法，并评估性能 这些方法通过模拟和应用于乳腺癌和卵巢癌风险的真实的数据， BRCA 1/2携带者和药物基因组学领域的数据集。这里提出的工作将 有助于统计方法来研究复杂的性状和高维数据的方法 分析，并提供由各种数据集代表的每个临床领域的洞察力，以评估 这些新方法。所有在此资助下开发的项目和详细的文件将在 通过万维网向感兴趣的研究人员免费提供。

The long-term objective of this project is to develop powerful and computationally-efficient statistical methods of identifying genes, environmental risk factors and their interactions underlying complex traits related to human diseases and health. The specific aim of this project is to continue to develop survival analysis and genetics models to incorporate age of onset data, environmental covariates information, gene-gene and gene-environment interactions, and multiple disease loci into haplotype-based genetic association analysis, analysis of single nucleotide polymorphisms (SNPs), and admixture mapping of complex traits in population- based cohort studies. The project also evaluates different study designs in genetic association studies. The proposed methods build on current approaches and hinge on novel integration of methods in survival analysis, high-dimensional data analysis and methods in human genetics. The focus will be on the development of rigorous and comprehensive statistical inference procedures for haplotype analysis, gene- gene and gene-environment interaction analysis and admixture mapping in cohort studies of unrelated individuals collected by different study designs, including case-cohort and nested case-control designs. Likelihood-based inferences, hidden Markov models, and threshold gradient descent methods will be developed for these aims. The project will also investigate the robustness, power and efficiencies of these methods, and compare them with existing methods. In addition, this project will develop practical and feasible computer programs in order to implement the proposed methods, and to evaluate the performance of these methods through simulation and application to real data on breast and ovarian cancer risks among the BRCA1/2 carriers and to data sets in the area of pharmacogenomics. The work proposed here will contribute both statistical methodology to studying complex traits and methods for high-dimensional data analysis, and offer insight into each of the clinical areas represented by the various data sets to evaluate these new methods. All programs developed under this grant and detailed documentation will be made available free-of-charge to interested researchers via the World Wide Web.