I am a Postdoctoral Fellow at Harvard School of Public Health (HSPH) and working with Prof. Liming Liang. I received Ph.D. degree in Biostatistics from the University of North Carolina (UNC) at Chapel Hill and MS, BS in Statistics from Seoul National University (SNU) in Korea. I believe that statistical interpretation would be truly crucial to understand various life phenomena, especially genetic phenomena, therefore the development of statistical methods for genetic data would greatly help us to discover the mystery of life.
For my Ph.D. dissertation, I developed Bayesian parametric and nonparametric methods for multiple QTL mapping and SNP-set analysis. In the first two parts of my dissertation, I extended two existing Bayesian parametric and nonparametric methods from univariate traits to longitudinal traits for mapping multiple QTL. In the third part of the dissertation, I developed a Bayesian regional SNP-set analysis which extended the Bayesian nonparametric method with Guassian process prior for multiple groups of rare and/or common variants.
My recent research mainly focuses on the development of statistical methodologies for genomewide association studies (GWAS) on human complex diseases and implementation of efficient software for application to large scale genomic and epigenomic data. I am developing statistical methods for GWAS data, especially for next generation sequencing (NGS) data and implementing efficient software. Today’s sequencing-based experiments generate huge amount of NGS data including RNA-seq, ChIP-seq and CLIP-seq. As a result, meaningful interpretation of NGS data has become particularly important. Since such interpretation depends largely on complex computation and statistics, I will continue to participate in the project and develop statistical methods for NGS data using my statistical knowledge as well as computer programming capability.
Wonil Chung, Ph.D.
Harvard School of Public Health (HSPH)
655 Huntington Ave. HSPH Bldg II Room 202, Boston, MA 02115