Statistical Methods to Map Genes for Complex Traits

描述(由申请人提供)：在过去的几年里，通过基因组广泛关联研究(GWAS)范式，数百个基因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的遗传标记。此外，关于遗传标记的先前生物学知识很少使用。从统计学和生物学的角度来看，所收集的Gwas数据中的丰富信息并没有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可以在一个地区内和跨地区联合分析多个标记的统计和计算方法，以及可以在关联分析中更有效地结合遗传标记、基因和途径的其他信息来源的方法。这项应用的长期目标是开发和实施新的统计方法，以确定影响个人对复杂特征的易感性的基因，将这些方法应用于正在进行的研究，以获得更多的生物学发现，并将这些工具传播到一般研究社区。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关于个人祖先的信息的标记，并利用这些信息来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)开发能够系统地整合不同信息源，特别是生物途径和网络的统计方法，以增强我们识别真正与复杂疾病相关的标记的能力；(4)开发统计方法，使用重测序数据来确定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，将统计方法应用并改进到广泛的疾病中，并向科学界传播经过充分测试和验证的计划。 与公共卫生相关：众所周知，遗传学在许多复杂的人类疾病中发挥着重要作用，例如癌症、高血压和精神障碍。然而，直到几年前，很少有基因被确定与这些疾病有关。随着可以同时监测数十万个基因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迎来了一场革命。数百个基因组区域已被发现影响数十种疾病的风险，在可预见的未来，这一列表可能会继续增加。这些丰富的数据产生了许多统计挑战，特别是在重新测序技术快速发展的情况下。该项目将开发新的和强大的统计方法，使人类遗传学家能够最大限度地利用收集到的宝贵数据。通过广泛的合作，我们的方法将被应用于许多正在进行的研究，以确定更多的基因组区域和复杂疾病的生物途径。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.