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，用于分析不同类型的数据并解决遗传研究中出现的问题。申请人还对传染病传播的建模感兴趣，重点是方法学开发，以提高个人水平模型的拟合度，并解决数据中缺失信息的问题。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，高通量遗传数据为识别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，数百万个单核苷酸多态性（SNP）可用于遗传研究中的分析。然而，需要强大而强大的统计方法和计算工具来分析如此大量的数据。因此，拟议的研究将集中在使用SNP数据的遗传分析上。 联合分析与多个性状的遗传关联，可以识别影响多个性状的常见遗传变异;在遗传学文献中，这被称为多效性效应。为了有效地调查疾病的发展，纵向队列研究旨在获得个体内各种疾病相关性状随时间的重复测量。多个纵向性状的遗传关联的联合分析更具挑战性，并且用于分析来自相关个体的数据（例如家族数据）的方法尚未可用。这项研究旨在解决这些非常重要的问题。还已知多个（2个或更多个）基因负责复杂性状。一些基因可能与其他基因相互作用，而其他基因可能与环境因素和时间相互作用（例如，年龄）。在这个拟议的研究计划中开发的用于识别基因与环境相互作用、时变基因和基因与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，已考虑将SNP基因型从小面板（较低密度）插补到大面板（较高密度）。使用较大的面板进行统计分析可以显着提高研究的功效，但较小的面板可以大幅降低基因分型成本。因此，研究人员对低密度面板的基因分型以及未分型SNP的准确插补方法感兴趣。从大样本组中选择信息丰富的SNP子集来设计低密度样本组可以显著提高插补准确性。待开发的插补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，经常会出现信息缺失的情况，如未观察到或部分观察到的接触网络和未观察到的传染期。传染病建模领域的拟议研究将侧重于开发方法，以提高个人水平模型的拟合度，并解决数据中缺失信息的问题。 拟议工作的影响将在与人类遗传学、统计遗传学、动物育种的遗传改良以及人类和动物的传染病有关的研究界感受到。

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.