Statistical Methods in Genetic Studies

申请人的主要研究兴趣是开发有效的统计和计算方法来分析不同类型的数据和解决遗传研究中出现的问题。申请人还对传染病传播建模感兴趣，重点是开发方法，以改善个人层面模型的拟合性，并解决数据中信息缺失的问题。因此，拟议的研究分为两个主题：遗传研究的统计方法和传染病建模。**在遗传学中，高通量遗传数据为确定遗传变异和感兴趣的性状之间的关系提供了机会。例如，在人类基因组中，数以百万计的单核苷酸多态性（SNPs）可用于遗传研究的分析。然而，需要强大而稳健的统计方法和计算工具来分析如此大量的数据。因此，建议的研究将侧重于利用SNP数据进行遗传分析。**对多个性状的遗传关联进行联合分析，能够识别影响一个以上性状的常见遗传变异；在遗传学文献中，这被称为多效效应。为了有效地调查疾病的发展，纵向队列研究的目的是在一段时间内获得个体内各种疾病相关特征的重复测量。对多个纵向性状的遗传关联进行联合分析更具挑战性，而且目前还没有分析相关个体（如家庭数据）数据的方法。拟议的研究旨在解决这些非常重要的问题。众所周知，多个（2个或更多）基因负责复杂的性状。一些基因可能与其他基因相互作用，而另一些基因可能与环境因素和时间（如年龄）相互作用。在本研究计划中，鉴定基因-环境相互作用、时变基因和基因-基因相互作用的方法对于阐明复杂性状的潜在机制至关重要。**最近，在许多实际情况下，已经考虑了从小面板（低密度）到大面板（高密度）的SNP基因型输入。统计分析使用较大的小组可以显著提高研究的能力，但一个小的小组可以大大降低基因分型成本。因此，研究人员对低密度面板的基因分型感兴趣，并结合对未分型SNPs的精确输入方法。从一个大的面板中选择一个信息snp的子集来设计低密度的面板可以大大提高输入精度。待开发的计算方法考虑了表型信息，可以大大提高重要snp的准确性，例如与感兴趣的性状相关的snp。**申请人还对传染病传播的建模感兴趣。在传染病数据中，缺失信息，如未观察到或部分观察到的接触网络和未观察到的感染期，是常见的。传染病建模领域拟议的研究将侧重于发展改进个人一级模型拟合的方法，并解决数据中信息缺失的问题。**拟议工作的影响将在研究人类遗传学、统计遗传学、动物育种遗传改良以及人类和动物传染病的研究团体中感受到。

The primary research interest of the applicant is on developing efficient statistical and computational methods for analyzing diverse types of data and addressing issues arising from genetic studies. The applicant is also interested in modeling the spread of infectious disease, with emphases on methodology development for improving the fit of individual-level models and addressing issues of missing information in data. The proposed research is therefore presented as two themes: Statistical methods for genetic studies and Infectious disease modelling.**In genetics, high-throughput genetic data provide opportunities for identifying relationships between genetic variants and traits of interest. In the human genome, for example, millions of single nucleotide polymorphisms (SNPs) are available for analysis in genetic studies. However, powerful and robust statistical methods and computational tools are needed to analyze such massive amounts of data. So, the proposed research will be focusing on genetic analysis using SNP data.**Joint analysis of genetic association with multiple traits enables the identification of common genetic variants that influence more than one trait; in genetics literature, this is known as the pleiotropic effect. To effectively investigate the development of a disease, longitudinal cohort studies are designed to obtain repeated measures of a variety of disease-related traits within an individual over time. Joint analysis of genetic association with multiple longitudinal traits is even more challenging, and methods for analyzing data from related individuals, such as family data, are not yet available. The proposed research aims to tackle these very important problems. It is also known that multiple (2 or more) genes are responsible for complex traits. Some genes might interact with other genes while others might interact with environmental factors and time (e.g., age). The methods to be developed in this proposed research program for identifying gene-environmental interactions, time-varying genes, and gene-gene interactions are critical for elucidating the underlying mechanism of a complex trait. **Recently, imputing SNP genotypes from a small panel (lower density) to a large panel (higher density) has been considered in many practical situations. Statistical analysis using a larger panel can significantly improve the power of the study but a small panel can substantially lower the genotyping cost. As such, researchers are interested in genotyping a lower density panel in conjunction with an accurate imputation method for the untyped SNPs. Selecting a subset of informative SNPs from a large panel to design low-density panels can substantially improve the imputation accuracy. The imputation method to be developed takes phenotype information into account and can greatly improve the accuracy for important SNPs, such as those associated with the trait of interest. **The applicant is also interested in the modeling of the spread of infectious disease. In infectious disease data, missing information, such as the unobserved or partially observed contact network and the unobserved infectious period, is common. The proposed research in the area of infectious disease modeling will focus on developing methodologies for improving the fit of individual-level models and addressing issues of missing information in data. **The impact of the proposed work will be felt in research communities concerned with human genetics, statistical genetics, genetic improvement of animal breeding, and infectious diseases in humans and animals.