A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes

A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes

Human leukocyte antigen (HLA) genes contribute to risk of many complex traits, yet understanding inter-ethnic heterogeneity is computationally challenging. Here, the authors develop DEEP*HLA for imputation of HLA genotypes and show its ability to disentangle HLA variant risk effects in diverse popul...

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Main Authors: Tatsuhiko Naito, Ken Suzuki, Jun Hirata, Yoichiro Kamatani, Koichi Matsuda, Tatsushi Toda, Yukinori Okada
Format: Article
Language:English
Published: Nature Publishing Group 2021-03-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-021-21975-x
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spelling doaj-d7317882897f4f4785597fe336ab7a962021-03-14T12:06:51ZengNature Publishing GroupNature Communications2041-17232021-03-0112111410.1038/s41467-021-21975-xA deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetesTatsuhiko Naito0Ken Suzuki1Jun Hirata2Yoichiro Kamatani3Koichi Matsuda4Tatsushi Toda5Yukinori Okada6Department of Statistical Genetics, Osaka University Graduate School of MedicineDepartment of Statistical Genetics, Osaka University Graduate School of MedicineDepartment of Statistical Genetics, Osaka University Graduate School of MedicineLaboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of TokyoLaboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of TokyoDepartment of Neurology, Graduate School of Medicine, The University of TokyoDepartment of Statistical Genetics, Osaka University Graduate School of MedicineHuman leukocyte antigen (HLA) genes contribute to risk of many complex traits, yet understanding inter-ethnic heterogeneity is computationally challenging. Here, the authors develop DEEP*HLA for imputation of HLA genotypes and show its ability to disentangle HLA variant risk effects in diverse populations.https://doi.org/10.1038/s41467-021-21975-x
collection DOAJ
language English
format Article
sources DOAJ
author Tatsuhiko Naito
Ken Suzuki
Jun Hirata
Yoichiro Kamatani
Koichi Matsuda
Tatsushi Toda
Yukinori Okada
spellingShingle Tatsuhiko Naito
Ken Suzuki
Jun Hirata
Yoichiro Kamatani
Koichi Matsuda
Tatsushi Toda
Yukinori Okada
A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes
Nature Communications
author_facet Tatsuhiko Naito
Ken Suzuki
Jun Hirata
Yoichiro Kamatani
Koichi Matsuda
Tatsushi Toda
Yukinori Okada
author_sort Tatsuhiko Naito
title A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes
title_short A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes
title_full A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes
title_fullStr A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes
title_full_unstemmed A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes
title_sort deep learning method for hla imputation and trans-ethnic mhc fine-mapping of type 1 diabetes
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2021-03-01
description Human leukocyte antigen (HLA) genes contribute to risk of many complex traits, yet understanding inter-ethnic heterogeneity is computationally challenging. Here, the authors develop DEEP*HLA for imputation of HLA genotypes and show its ability to disentangle HLA variant risk effects in diverse populations.
url https://doi.org/10.1038/s41467-021-21975-x
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