한빛사논문
Seungho Cook1,2,11, Wanson Choi1,11, Hyunjoon Lim3,11, Yang Luo4,5,6,7, Kunhee Kim1,2, Xiaoming Jia8, Soumya Raychaudhuri4,5,6,7,9,10 & Buhm Han1,2,3,*
1Department of Biomedical Sciences, BK21 Plus Biomedical Science Project, Seoul National University College of Medicine, Seoul, South Korea.
2Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Seoul, South Korea.
3Interdisciplinary Program for Bioengineering, Seoul National University, Seoul, South Korea.
4Center for Data Sciences, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA.
5Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA.
6Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA.
7Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
8Department of Neurology, University of California San Francisco, San Francisco, CA, USA. 9Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. 10Centre for Genetics and Genomics Versus Arthritis, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.
11These authors contributed equally: Seungho Cook, Wanson Choi, Hyunjoon Lim.
*Corresponding author
Abstract
The recent development of imputation methods enabled the prediction of human leukocyte antigen (HLA) alleles from intergenic SNP data, allowing studies to fine-map HLA for immune phenotypes. Here we report an accurate HLA imputation method, CookHLA, which has superior imputation accuracy compared to previous methods. CookHLA differs from other approaches in that it locally embeds prediction markers into highly polymorphic exons to account for exonic variability, and in that it adaptively learns the genetic map within MHC from the data to facilitate imputation. Our benchmarking with real datasets shows that our method achieves high imputation accuracy in a wide range of scenarios, including situations where the reference panel is small or ethnically unmatched.
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