The research in my group is designed to contribute to understanding the role of genetic, genomic and environmental factors, and their interactions, in the development of complex diseases. Our research covers problems on the full spectrum of research in this field, from theoretical statistics to data analysis. The applications include finding the genetic and environmental components of asthma and related traits, and of neuropsychiatric phenotypes.
Statistical genetics problems of interest focus on applications of genetics/genomics technologies, and large scale data integration. We are interested in models and statistical methods for binary, quantitative and longitudinal traits, genome-wide strategies for testing gene-gene and gene-environment interaction, and methods for analyzing expression data from RNA-sequencing studies. We have also developed methodology for analyzing the genome-wide data at the gene, region, pathway and network level for common and rare variants. Current focus is on methods for integrating 'omics' data such as expression, methylation, microbiome and variation for understanding the molecular basis of disease.
Mathematical statistics topics include Bayesian and frequentist measures of missing data and dependence, general measures of information, and network theory.