Statistical Methods in Genetic Studies

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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.