Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction

Population structure, even subtle differences within seemingly homogenous populations, can have an impact on the accuracy of polygenic prediction. Here, Sakaue et al. use dimensionality reduction methods to reveal fine-scale structure in the Biobank Japan cohort and explore the performance of polyge...

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Main Authors:	Saori Sakaue, Jun Hirata, Masahiro Kanai, Ken Suzuki, Masato Akiyama, Chun Lai Too, Thurayya Arayssi, Mohammed Hammoudeh, Samar Al Emadi, Basel K. Masri, Hussein Halabi, Humeira Badsha, Imad W. Uthman, Richa Saxena, Leonid Padyukov, Makoto Hirata, Koichi Matsuda, Yoshinori Murakami, Yoichiro Kamatani, Yukinori Okada
Format:	Article
Language:	English
Published:	Nature Publishing Group 2020-03-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-020-15194-z

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spelling	doaj-c4f4b797ca974d86bfaca12d2007301f2021-05-11T08:42:12ZengNature Publishing GroupNature Communications2041-17232020-03-0111111110.1038/s41467-020-15194-zDimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk predictionSaori Sakaue0Jun Hirata1Masahiro Kanai2Ken Suzuki3Masato Akiyama4Chun Lai Too5Thurayya Arayssi6Mohammed Hammoudeh7Samar Al Emadi8Basel K. Masri9Hussein Halabi10Humeira Badsha11Imad W. Uthman12Richa Saxena13Leonid Padyukov14Makoto Hirata15Koichi Matsuda16Yoshinori Murakami17Yoichiro Kamatani18Yukinori Okada19Department 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 MedicineDepartment of Statistical Genetics, Osaka University Graduate School of MedicineLaboratory for Statistical Analysis, RIKEN Center for Integrative Medical SciencesAllergy and Immunology Research Center, Institute for Medical Research, Ministry of Health MalaysiaDepartment of Internal Medicine, Weill Cornell Medicine-Qatar, Education CityDepartment of Internal Medicine, Hamad Medical CorporationDepartment of Internal Medicine, Hamad Medical CorporationDepartment of Internal Medicine, Jordan HospitalRheumatology Division, Department of Internal Medicine, King Faisal Specialist Hospital and Research CenterDr. Humeira Badsha Medical Center, Emirates HospitalDepartment of Rheumatology, American University of BeirutCenter for Genomic Medicine, Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical SchoolDivision of Rheumatology, Department of Medicine, Karolinska Institutet and Karolinska University HospitalLaboratory of Genome Technology, Institute of Medical Science, the University of TokyoDepartment of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, the University of TokyoDivision of Molecular Pathology, Institute of Medical Science, the University of TokyoLaboratory for Statistical Analysis, RIKEN Center for Integrative Medical SciencesDepartment of Statistical Genetics, Osaka University Graduate School of MedicinePopulation structure, even subtle differences within seemingly homogenous populations, can have an impact on the accuracy of polygenic prediction. Here, Sakaue et al. use dimensionality reduction methods to reveal fine-scale structure in the Biobank Japan cohort and explore the performance of polygenic risk scores.https://doi.org/10.1038/s41467-020-15194-z
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Saori Sakaue Jun Hirata Masahiro Kanai Ken Suzuki Masato Akiyama Chun Lai Too Thurayya Arayssi Mohammed Hammoudeh Samar Al Emadi Basel K. Masri Hussein Halabi Humeira Badsha Imad W. Uthman Richa Saxena Leonid Padyukov Makoto Hirata Koichi Matsuda Yoshinori Murakami Yoichiro Kamatani Yukinori Okada
spellingShingle	Saori Sakaue Jun Hirata Masahiro Kanai Ken Suzuki Masato Akiyama Chun Lai Too Thurayya Arayssi Mohammed Hammoudeh Samar Al Emadi Basel K. Masri Hussein Halabi Humeira Badsha Imad W. Uthman Richa Saxena Leonid Padyukov Makoto Hirata Koichi Matsuda Yoshinori Murakami Yoichiro Kamatani Yukinori Okada Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction Nature Communications
author_facet	Saori Sakaue Jun Hirata Masahiro Kanai Ken Suzuki Masato Akiyama Chun Lai Too Thurayya Arayssi Mohammed Hammoudeh Samar Al Emadi Basel K. Masri Hussein Halabi Humeira Badsha Imad W. Uthman Richa Saxena Leonid Padyukov Makoto Hirata Koichi Matsuda Yoshinori Murakami Yoichiro Kamatani Yukinori Okada
author_sort	Saori Sakaue
title	Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction
title_short	Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction
title_full	Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction
title_fullStr	Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction
title_full_unstemmed	Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction
title_sort	dimensionality reduction reveals fine-scale structure in the japanese population with consequences for polygenic risk prediction
publisher	Nature Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2020-03-01
description	Population structure, even subtle differences within seemingly homogenous populations, can have an impact on the accuracy of polygenic prediction. Here, Sakaue et al. use dimensionality reduction methods to reveal fine-scale structure in the Biobank Japan cohort and explore the performance of polygenic risk scores.
url	https://doi.org/10.1038/s41467-020-15194-z
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Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction

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