Research Interests

Updated by Buhm Han on March 6, 2014

My research is on developing methods for the analysis of genetic and phenotypic variation. Since genetic variation is the fundamental cause of the phenotypic variation, we can identify disease-causing mutations and better understand the genotype-phenotype causal mechanism by analyzing human genetic variation. The focus of my research is on tackling computational challenges in the analysis of genetic variation using methods of computer science, probability theory, and statistics.

Methods for association identification in GWAS Genome-wide association study (GWAS) has been the most popular tool to identify genotype-phenotype associations. I have been developing a number of computational methods facilitating GWAS. I developed a multiple testing correction approach based on multivariate normal approximation and conditional sampling, SLIDE, that can be efficiently applied to GWAS dataset (Han et al. PLOS Genet 2009). In order to distinguish spurious association from true associations, I developed an approach LDPAC that uses a likelihood ratio test based on linkage disequilibrium (LD) information (Han et al. Genet Epidem 2011). I also recently developed a graph-based computational approach GRAPHIBD that uses identity-by-descent data for association mapping efficiently (Han et al. Bioinformatics 2014).

Methods for fine-mapping causal variation After identifying candidate loci, it is increasingly of importance to fine-map the causal variants. The hardest challenge resides in the MHC region, which is highly polymorphic, contains hundreds of genes, and has extensive LD. I developed an imputation approach for HLA genes, called SNP2HLA (Jia* and Han* et al. PLOS One 2013) and applied this approach to successfully fine-map causal variants within MHC for the subset of rheumatoid arthritis (RA) called seronegative RA (Han et al. AJHG 2014 in press).

Meta-analytic approaches I am interested in developing meta-analysis approaches. Meta-analysis is an approach combining multiple different sources of information into one, and becoming important in this era systems where we want to look at multiple domains in an integrated view. I developed a powerful random effects model meta-analysis method (Han and Eskin, AJHG 2011) and its interpretation framework (Han and Eskin, PLOS Genet 2012) that are optimized for heterogeneous data. This approach was then applied to the multiple tissue eQTL analsys (Sul* and Han* et al, PLOS Genet 2013) as well as gene-by-environment interaction analysis (Kang* and Han* et al., PLOS Genet 2013).

Methods for sequencing technology The advent of sequencing technology allowed the genotyping of rare variants in candidate genes. I actively participated in the development of rare variant association testing approaches such as RWAS (Sul and Han et al. Genetics 2011) and LRT (Sul and Han et al. JCB 2011). I also participated in developing an optimal phasing algorithm for sequence data (He, Han, and Eskin JCB 2013). I am interested in developing effective approaches optimized for the sequenced data, such as combining meta-analytic approaches and rare variant testing approaches.

Phenotypic and genotypic heterogeneity Phenotypic and genotypic heterogeneity is an important issue that is gaining increasing attention in clinical perspectives. The same disease may present several different phenotypic characteristics, and conversely different genetic factors may cause the similarly looking phenotype. In my current project, we observed such issue can be detrimental in genetic analysis. We found that seronegative RA can be confounded by individuals who look like seronegative RA but actually are genetically seropositive RA or ankylosing spondylitis, which was resulted in imperfect clinical classification. We developed an approach that estimates and corrects for the confounding due to heterogeneity in the cohorts. Using this approach, we successfully identified association-driving variants in HLA that were replicated, which would not have been identified without the new approach (Han et al. AJHG 2014 in press).

*: Equal contribution