TITLE: 
Analysis of Genomic Data: Methods and Applications

ABSTRACT
In this talk, I will present two examples of methodological development for genomic data analysis. In the first example, we develop a method for analyzing factorial time course gene expression data. The proposed method models dependence of gene expression on multiple experimental factors, and evaluates factor effects by pooling information from time course measurements. It captures gene-specific dynamic response and has desired statistical properties. An application to a clinical study of burn injury reveals mitochondria and immunoglobulin genes are differentially perturbed in pediatric and adult patients by burn injury. In the second example, we propose a likelihood-based method that improve genotype calls by exploiting pedigree information and linkage disequilibrium (LD) of sequence data. We use simulations and 1000 Genomes Project data to evaluate the performance of the proposed methods