Statistical Methods for Rare Variant Association Studies

 描述（由申请人提供）：由于以下原因，人们对检测罕见变异和复杂性状之间的关联越来越感兴趣：（1）通过全基因组关联研究（GWAS）鉴定的常见变异仅占假定表型变异的一小部分，以及（2）下一代测序技术的发展使得直接检测所有罕见变异成为可能。虽然已经开发了许多统计方法来检测罕见变异和复杂性状之间的关联，但在罕见变异关联研究中有效地控制群体分层仍然是一个悬而未决的问题。此外，人们已经认识到，每一种性状或疾病都是在一段时间内发展起来的。如果忽略这一发展过程，就会降低罕见变异关联研究的效力。然而，罕见变异关联研究中纵向表型的统计方法仍不发达。 该项目探索了新的统计方法来检测导致复杂疾病的罕见变异，其中包括（1）一种新的统计方法，用于控制罕见变异相关性研究中的人群分层，适用于广泛的研究设计，（2）基于回顾性视图的新型、基于家族的罕见变异相关性检验，因此可以解释复杂和不确定的家系确定，以及（3）新的罕见变异关联测试，用于纵向表型，其使用生长轨迹作为表型，而不是使用在一个时间点的表型值。该项目的最后一个具体目标是，利用广泛的模拟研究，比较拟议方法与现有方法的性能，将拟议方法应用于选定的真实的数据集，为拟议方法开发计算机软件，并免费向科学界发布该软件。 如果这个AREA项目能够获得资助，我们将直接资助两名统计遗传学专业的研究生研究助理（一名全职支持，一名全职支持）。 暑期支持）和两个兼职（暑期支持）高年级本科生。参与该项目的学生将进行模拟研究和分析真实的数据集。因此，在本发明中， 这个项目可以增加学生参与有价值的研究的人数。本项目提供的资助研究职位将使研究人员能够 努力吸引高年级本科生和研究生从事研究。该项目的研究成果也将用于我们的统计遗传学系列研讨会的报告，演示和简短讲座的开发，这些研究成果在我们的研讨会上可以激发研究生的研究兴趣。吸引高年级的本科生和研究生进行研究，激发研究生的研究兴趣，将大大提高该部门的研究环境。

 DESCRIPTION (provided by applicant): There is increasing interest in detecting associations between rare variants and complex traits, for the following reasons: (1) the common variants identified through genome-wide association studies (GWAS) account for only a small portion of the presumed phenotypic variation and (2) the development of next-generation sequencing technology has made it feasible to directly test all rare variants. Although many statistical methods have been developed for detecting associations between rare variants and complex traits, effectively controlling for population stratification in rare variant association studies i still an open problem. Furthermore, it has been realized that every trait or disease develops over a period of time. If this developmental process is ignored, it reduces the power in rare variant association studies. However, statistical methods for longitudinal phenotypes in rare variant association studies are still underdeveloped. This project explores novel statistical methods to detect rare variants responsible for complex diseases, which include (1) a novel statistical method to control for population stratification in rare variant association studies that is applicale to a wide range of study designs, (2) novel, family-based rare variant association tests that are based on a retrospective view and thus can account for complex and undefined ascertainment of pedigrees, and (3) new rare variant association tests for longitudinal phenotypes that use growth trajectories as a phenotype instead of using phenotype values at one time point. The last specific aim of this project is to use extensive simulation studies to compare the performance of the proposed methods with that of the existing methods, apply the proposed methods to selected real data sets, and develop computer software for the proposed methods and release the software to the scientific community at no charge. If this AREA project can be funded, we will directly support two graduate research assistants majoring in statistical genetics (one full-time support and one summer support) and two part-time (summer support) senior undergraduate students. The students involved in this project will perform simulation studies and analyze real data sets. Thus, this project can increase the number of students exposed to meritorious research. The availability of funded research positions provided by this project will enable the investigators to make significant efforts toward attracting senior undergraduate and graduate students to research. Research results from this project will be also used in reports, presentations and development of short lectures for our statistical genetics seminar series and these research results presented in our seminar can stimulate the research interests of graduate students. Attracting senior undergraduate and graduate students to research and stimulating the research interests of graduate students will greatly enhance the research environment in the department.