My research goal is to develop statistical methods/software for analyzing modern high throughput human genetics/genomics data and utilize them to identify and characterize genetic variants associated with human phenotypes and diseases. In particular, my recent work focuses on the development of statistical methods for analyzing human genome using summary data from genome-wide association studies (GWASs), large-scale meta-analyses of GWASs and whole genome (exome) sequencing studies. Notably, I developed, DIST (http://dleelab.github.io/dist), a new summary statistics based imputation method which circumvents the genotype imputation procedure. The method has shown to provide very comparable imputation accuracy to genotype imputation methods while significantly reducing computational burden. Based on the theoretical foundations of DIST, I recently developed new methods/software for directly imputing association summary statistics for unmeasured SNPs from mixed ethnicity cohorts (DISTMIX, http://dleelab.github.io/distmix/) and joint testing of functional variants associated with a gene (JEPEG, and JEPEGMIX, http://dleelab.github.io/jepegmix/). With the emergence of very large reference population such as the Haplotype Reference Consortium data, I believe that the proposed genome analysis tools using only summary statistics will play a very important role in the rapid discovery of genetic variants associated with human traits and diseases.
