Statistical methods for genetic association studies
Genome-wide association studies (GWAS) is an effective tool for detecting disease-associated variants, yet many challenges remain: multiple testing, fine-mapping, and rare variants.
We make novel computational and statistical approaches to facilitate design, quality control, and analysis of genome-wide association studies.
- Buhm Han, Hyun Min Kang, Eleazar Eskin. “Rapid and Accurate
Multiple Testing Correction and Power Estimation for Millions of
Correlated Markers.” PLOS Genetics. 5(4):e1000456, 2009.
- Buhm Han, Brian Hackel, Eleazar Eskin. “Postassociation Cleaning Using Linkage Disequilibrium Information.” Genetic Epidemiology. 35(1):1-10, 2011.
- Buhm Han*, Eun Yong Kang*, Soumya Raychaudhuri, Paul I. W. de Bakker, Eleazar Eskin. “Fast Pairwise IBD Association Testing in Genome-wide Association Studies.” Bioinformatics. 30(2):206-13. 2014.
HLA imputation and fine-mapping
The MHC region containing HLA genes is a highly challenging region for genetic studies owing to its extremely polymorphic nature and long linkage disequilibrium.
We made a novel imputation approach for HLA region. We are actively applying imputation to fine-map MHC region; to identify which HLA genes and amino acids are driving autoimmune diseases.
- Xiaoming Jia*, Buhm Han*, Suna Onengut-Gumuscu, Wei-Min Chen, Patrick J. Concannon, Stephen S. Rich, Soumya Raychaudhuri, Paul I.W. de Bakker. “Imputing Amino Acid Poly- morphisms in Human Leukocyte Antigens.” PLOS One. 8(6):e64683. 2013.
- Buhm Han, Dorothee Diogo, Steve Eyre, et al. “Fine-mapping seronegative and seropositive rheumatoid arthritis to shared and distinct HLA alleles by adjusting for the effects of heterogeneity.” The American Journal of Human Genetics. 94(4):522-523. 2014. (Selected as F1000 recommendation)
- Yukinori Okada*, Buhm Han*, Lam C. Tsoi, et al. “Fine-mapping major histocompatibility complex associations in psoriasis and its clinical subtypes.” The American Journal of Human Genetics. 95(2):162-172. 2014.
Meta-analysis and its wide applications
Meta-analysis is a widely used means for genetic association studies. Challenges still remain in meta-analysis, such as how to optimally deal with heterogeneity and interpret results.
We develop new methods for meta-analysis that increases power and helps interpretation. We expand the application of meta-analysis to eQTL analysis and GxE interaction detection.
- Buhm Han, Eleazar Eskin. “Random Effects Model Aimed At Discovering Associations in Meta-Analysis of Genome-Wide Association Studies.” The American Journal of Human Genetics. 88(5):586-598, 2011.
- Buhm Han, Eleazar Eskin. “Interpreting Meta-Analysis of Genome-Wide Association Studies.” PLOS Genetics. 8(3):e1002555, 2012.
- Jae Hoon Sul*, Buhm Han*, Chun Ye*, Ted Choi, Eleazar Eskin. “Effectively identifying eQTLs from multiple tissues by combining mixed model and meta-analytic approaches.” PLOS Genetics. 9(6): e1003491. 2013.
- Eun Yong Kang*, Buhm Han*, Nicholas Furlotte*, et al. “Meta-analysis identifies gene-by-environment interactions as demonstrated in a study of 4,965 mice.” PLOS Genetics. 10(1): e1004022. 2013.
Analysis of clinical heterogeneity
Some diseases are similar to each other so that patients are easily misclassified to a different disease. As a result, our ability to correctly treat patients and to effectively identify causal mutation of a disease diminishes.
We develop new methods to address clinical heterogeneity in a cohort caused by sample misclassifications or other reasons. We aim to detect, measure, and correct for heterogeneity.
- Buhm Han, Dorothee Diogo, Steve Eyre, et al.
“Fine-mapping seronegative and seropositive rheumatoid arthritis to
shared and distinct HLA alleles by adjusting for the effects of
heterogeneity.” The American Journal of Human Genetics. 94(4):522-523. 2014.
- Buhm Han et al. "A statistical approach to distinguish genetic pleiotropy from clinical heterogeneity: application to autoimmune diseases." Platform presentation, The 64th Annual meeting of the American Society of Human Genetics, San Deigo, CA, USA, 2014. (selected as Charles J. Epstein Semifinalist Award)
New! Next-generation sequencing and cancer genomics
In Asan Medical Center, we are actively involved in several projects where we use next-generation sequencing for cancer genomics, to identify the genomic basis of cancer. We are developing technologies specifically targeted to predict the survival outcome of a patient after cancer surgery.
New! Drug discovery and personalized medicine
In Asan Medical Center, we are involved in projects where we identify new targets of diseases based on genomic data, and develop new algorithm to implement personalized medicine.