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

Tatsuhiko Naito; Ken Suzuki; Jun Hirata; Yoichiro Kamatani; Koichi Matsuda; Tatsushi Toda; Yukinori Okada

doi:10.1038/s41467-021-21975-x

Nature Communications (Mar 2021)

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

Tatsuhiko Naito,
Ken Suzuki,
Jun Hirata,
Yoichiro Kamatani,
Koichi Matsuda,
Tatsushi Toda,
Yukinori Okada

Affiliations

Tatsuhiko Naito: Department of Statistical Genetics, Osaka University Graduate School of Medicine
Ken Suzuki: Department of Statistical Genetics, Osaka University Graduate School of Medicine
Jun Hirata: Department of Statistical Genetics, Osaka University Graduate School of Medicine
Yoichiro Kamatani: Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo
Koichi Matsuda: Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo
Tatsushi Toda: Department of Neurology, Graduate School of Medicine, The University of Tokyo
Yukinori Okada: Department of Statistical Genetics, Osaka University Graduate School of Medicine

DOI: https://doi.org/10.1038/s41467-021-21975-x
Journal volume & issue: Vol. 12, no. 1
pp. 1 – 14

Abstract

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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.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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