Statistical Methods to Map Genes for Complex Traits

描述（由申请人提供）：在过去几年中，通过全基因组关联研究（GWAS）范式，数百个遗传区域与复杂的人类性状有关。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个遗传标记。此外，很少使用关于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，可以联合分析多个标记，无论是在一个区域内和跨区域，以及方法，可以更有效地结合其他来源的信息遗传标记，基因和途径的关联分析。本申请的长期目标是开发和实施新的统计方法来鉴定影响个体对复杂性状的易感性的基因，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团体。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体祖先的信息标记，并利用这些信息更有效地调整遗传关联研究中的样本异质性;（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）开发统计方法，可以系统地整合不同的信息来源，特别是生物学途径和网络，以增加我们识别与复杂疾病真正相关的标记的能力;（4）开发统计方法，使用重测序数据来识别表型和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，将统计方法应用于各种疾病，并向科学界传播经过充分测试和验证的计划。 公共卫生相关性：众所周知，遗传学在许多复杂的人类疾病中起着重要作用，例如癌症、高血压和精神障碍。然而，直到几年前，很少有基因与这些疾病密切相关。随着可以同时监测数十万种遗传变异的高密度平台的引入，以及数千名患者共同分析的大型合作项目的形成，人类遗传学领域最近发生了一场革命。已经发现数百个基因组区域影响数十种疾病的风险，在可预见的未来，这一名单可能会继续增加。这些丰富的数据产生了许多统计挑战，特别是随着重测序技术的快速发展。该项目将开发新颖而强大的统计方法，使人类遗传学家能够充分利用收集到的有价值的数据。通过广泛的合作，我们的方法将应用于许多正在进行的研究，以确定复杂疾病的更多基因组区域和生物学途径。我们还将分发经过良好测试的计算机程序，以便其他研究人员可以使用我们开发的统计工具。

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. PUBLIC HEALTH RELEVANCE: It is well known that genetics plays a major role in many complex human diseases, e.g. cancer, hypertension, and mental disorders. However, very few genes had been firmly implicated in these disorders until a few years ago. With the introduction of high-density platforms where hundreds of thousands of genetic variants can be monitored simultaneously and the formations of large collaborative projects where thousands of patients are jointly analyzed, the field of human genetics has enjoyed a revolution recently. Hundreds of genomic regions have been found to affect the risks of dozens of diseases, and this list will likely keep increasing in the foreseeable future. These rich data have generated many statistical challenges, especially with the rapid developments of resequencing technologies. This project will develop novel and powerful statistical methods to enable human geneticists to make the most out of the valuable data collected. Through extensive collaborations, our methods will be applied to many ongoing studies to identify more genomic regions and biological pathways for complex diseases. We will also distribute the well-tested computer programs so that other researchers can utilize the statistical tools developed by us.