Faculty Profile

Seunggeun (Shawn) Lee is an Associate Professor in the Department of Biostatistics. He received his PhD 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.

• BIOSTAT651: Applied Statistics II: Extensions for Linear Regression   Syllabus (PDF)

• BIOSTAT664: Special Topics in Biostastics   Syllabus (PDF)

• B.S., Biology and Statistics, Seoul National University, 2005

• PhD, Biostatistics, University of North Carolina, 2010

Dutta D, Scott L, Boehnke M, Lee S (2019). Multi‐SKAT: General framework to test for rare‐variant association with multiple phenotypes. Genetic Epidemiology 43(1), 4-23

Zhou W, Nielsen J, Fritsche L, Dey R, Gabrielsen M, Wolford B, LeFaive J, VandeHaar P, Gagliano S, Gifford A, Bastarache L, Wei WQ, Denny J, Lin M, Hveem K, Kang HM, Abecasis G, Willer C#, Lee S# (2018) Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nature Genetics 50, 1335-1341
# equal contribution

Dey R, Schmidt E, Abecasis G, Lee S (2017) A fast and accurate algorithm to test for binary phenotypes and its application to PheWAS. American Journal of Human Genetics, 101, 37-49.

He Z, Lee S, Zhang M, Smith J, Guo X, Palmas W, Kardia S, Ionita‐Laza I, Mukherjee B (2017). Rare‐variant association tests in longitudinal studies, with an application to the Multi‐Ethnic Study of Atherosclerosis (MESA). Genetic Epidemiology 41(8):801-810.

He Z, Zhang M, Lee S, Smith J, Kardia S, Roux V, Mukherjee B (2017). Set-Based Tests for the Gene–Environment Interaction in Longitudinal Studies. Journal of the American Statistical Association 101, 340-352.

Lee S, Kim S, Fuchsberger C (2017). Improving power for rare‐variant tests by integrating external controls. Genetic Epidemiology 41(7): 610-619.

Lee S, Sun W, Wright F, Zou F (2017).  An improved and explicit surrogate variable analysis procedure by coefficient adjustment. Biometrika 104(2): 303-316.