Seunggeun (Shawn) Lee is an Associate Professor in the Department of Biostatistics. He received his Ph.D. in Biostatistics from the University of North Carolina at Chapel and completed a postdoctoral training at Harvard School of Public Health.
His research focuses on developing statistical and computational methods for the analysis of the large-scale high-dimensional genomic data, which is essential to better understand the genetic architecture of complex diseases and traits.
Lee, S. Abecasis, G., Boehnke, M., Lin, X. (2014). Rare-Variant Association Analysis: Study Designs and Statistical Tests American Journal of Human Genetics, 95, 5–23.
Lee, S., Zou, F. and Wright, F.A. (2014). Convergence of sample eigenvalues, eigenvectors, and principal component scores for ultra-high dimensional data Biometrika, 101, 484-490.
Lee, S., Teslovich, T., Boehnke, M., Lin, X. (2013). General framework for meta-analysis of rare variants in sequencing association studies. American Journal of Human Genetics, 93, 42-53.
Ionita-Laza, I.*, Lee, S.*, Makarov, V., Buxbaum, J. Lin, X. (2013). Sequence kernel association tests for the combined effect of rare and common variants. American Journal of Human Genetics. *Joint first author, 92, 841–853.
Lee, S., Emond, M.J., Bamshad, M.J., Barnes, K.C., Rieder, M.J. Nickerson, D.A., NHLBI GO Exome Sequencing ProjectESP Lung Project Team, Christiani, D.C., Wurfel, M.M. and Lin, X. (2012). Optimal unified approach for rare variant association testing with application to small sample case-control whole-exome sequencing studies. American Journal of Human Genetics, 91, 224-237.