Han Chen

My research interests mainly focus on statistical genetics and genomics, including computational methods for analyzing large-scale sequencing data, parametric and semiparametric statistical models for correlated data analysis, rare genetic variant association analysis, meta-analysis, gene-environment interactions, with applications to complex disease genetics. With the advance of next-generation sequencing technologies, massive genetic and genomic data have been generated. These big data are often sparse and high-dimensional, as most genetic variations in the human genome are rare mutations with very low minor allele frequencies, which poses statistical and computational challenges in the data analysis and interpretation. To tackle these challenges, my current research projects include: 1) Computationally efficient statistical association tests to account for population structure and relatedness in large-scale multi-ethnic sequencing studies 2) Statistical tests for gene-environment and gene-treatment interactions in large-scale epidemiological and pharmacogenomics studies 3) Genetic epidemiological studies on complex heritable conditions, such as obstructive sleep apnea. These research projects require multidisciplinary knowledge in biostatistics, bioinformatics, genetics and epidemiology, and students in my group will develop skills in statistical methodology development, cloud computing, big data manipulation, algorithm optimization and software/app implementation from mathematical formulation and derivations, computer simulations to hands-on experience in real data analysis for genetics, genomics and public health research.