Department of Biomedical Sciences, SNU
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Han, buhm

Ph.D. / Professor
Genomics & Bioinformatics Laboratory
TEL +82-2-3668-7618
E-MAIL buhm.han@snu.ac.kr
WEBSITE http://hanlab.snu.ac.kr

Research

Research Field
Countless genes are involved in human diseases. Identifying the exact link between disease and genes can be of great help in preventing or treating diseases. With this goal, the field of human genetics has been developing in the direction of utilizing genomic big data over the past 20 years. The need for specialized algorithm for analyzing data is emerging in various fields, such as genome-wide association study (GWAS), which collects and analyzes tens of thousands of patients, HLA gene analysis, which is important for immune response, and single cell RNA analysis, which analyzes each individual cell's transcripts. This laboratory conducts research to develop new methodologies and algorithms to solve challenges in the fields of medical science, biology, and genomics. In addition, through big data analysis, target markers related to prognosis and treatment of diseases are discovered.
Pure Link
https://snucm.elsevierpure.com/en/persons/buhm-han
Keyword
Bioinformatics, Genomics, Statistical Genetics, Computational Biology, Human Leukocyte Antigen
Intensive Major
# Omics Medicine

Education

  • 1997-2004, B.Eng., Seoul National University (Electrical Engineering)
  • 2004-2007, M.S., UC San Diego (Computer Science)
  • 2007-2009, Ph.D., UC San Diego (Computer Science, specialty: Bioinformatics)

Career

  • 2009-2012, PostDoc, UC Los Angeles, USA
  • 2012-2013, PostDoc, Brigham and Women's Hospital, USA
  • 2013-2014, Instructor, Harvard Medical School, USA
  • 2014-2015, Research Assistant Professor, Asan Medical Center, Korea
  • 2015-2018, Assistant Professor, Univ. of Ulsan College of Medicine, Korea
  • 2018-Current, Associate Professor, Seoul National University College of Medicine, Korea

Publication

  1. Accurate imputation of human leukocyte antigens with CookHLA. Nature Communications. 12(1):1264, 2021
  2. PLEIO: a method to map and interpret pleiotropic loci with GWAS summary statistics. The American Journal of Human Genetics. 108(1):36-48, 2021
  3. Genomic GPS: using genetic distance from individuals to public data for genomic analysis without disclosing personal genomes. Genome biology, 20(1):1-5, 2019
  4. Increasing the power of meta-analysis of genome-wide association studies to detect heterogeneous effects. Bioinformatics. 33(14):i379, 2017.
  5. A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases. Nat Genet. 48(7):803, 2016.
List
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