Statistical Methods to Map Genes for Complex Traits

描述（由申请人提供）：在过去的几年中，通过基因组全关联研究（GWAS）范式，数百个遗传区域与复杂的人类特征有关。尽管取得了这些成功，但大多数已发表的工作中的统计分析都是基于单一遗传标记。此外，很少使用遗传标记的先前生物学知识。从统计学和生物学的角度来看，收集的GWAS数据中的丰富信息尚未被充分利用来揭示疾病的病因。为了满足这些关键需求，许多研究小组一直在积极开发统计和计算方法，可以联合分析区域内和跨区域的多个标记，以及可以更有效地结合遗传标记、基因和关联分析途径的其他信息来源的方法。该应用程序的长期目标是开发和实施新的统计方法，以识别影响个体对复杂性状易感性的基因，将这些方法应用于正在进行的研究，以实现更多的生物学发现，并将这些工具传播给一般研究社区。为了实现这些广泛的目标，我们建议实现以下具体目标：(1)开发统计方法来识别个人祖先的信息标记，并利用这些信息在遗传关联研究中更有效地调整样本异质性；(2)开发能够更有效地执行多标记分析的统计方法，并评估不同标记搜索策略的统计能力；(3)开发能够系统地整合不同信息来源的统计方法，特别是生物学途径和网络，以提高我们识别与复杂疾病真正相关的标志物的能力；(4)开发统计学方法，利用重测序数据确定表型与候选区域之间的遗传关联。此外，我们将与领先的人类遗传学家合作，将统计方法应用和完善于广泛的疾病，并向科学界传播经过良好测试和验证的项目。

DESCRIPTION (provided by applicant): Hundreds of genetic regions have been implicated in complex human traits in the past several years through the genome wide association study (GWAS) paradigm. Despite these successes, statistical analyses in most published work were based on single genetic markers. In addition, prior biological knowledge on genetic markers is rarely used. From both statistical and biological points of view, the rich information in the collected GWAS data has not been fully utilized to reveal disease etiologies. To address these critical needs, many research groups have been actively developing statistical and computational methods that can jointly analyze multiple markers, both within a region and across regions, and methods that can more effectively incorporate other sources of information on genetic markers, genes, and pathways in association analysis. The long- term goals of this application are to develop and implement novel statistical methods to identify genes affecting an individual's susceptibility to complex traits, to apply these methods to ongoing studies to enable more biological findings, and to disseminate these tools to the general research community. To achieve these broad goals, we propose to accomplish the following specific aims: (1) to develop statistical methods to identify markers that are informative about an individual's ancestry, and to take advantage of this information for more effective adjustment of sample heterogeneity in genetic association studies; (2) to develop statistical methods that can more efficiently perform multi-marker analysis, and to evaluate the statistical power of different marker search strategies; (3) to develop statistical methods that can systematically integrate different sources of information, especially biological pathways and networks, to increase our power to identify markers truly associated with complex diseases; (4) to develop statistical methods to use resequencing data to identify genetic associations between phenotypes and candidate regions. In addition, we will collaborate with leading human geneticists to apply and refine the statistical methods to a wide array of diseases, and to disseminate well-tested and validated programs to the scientific community.