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In silico integration of thousands of epigenetic datasets into 707 cell type regulatory annotations improves the trans-ethnic portability of polygenic risk scores
View ORCID ProfileTiffany Amariuta, View ORCID ProfileKazuyoshi Ishigaki, Hiroki Sugishita, View ORCID ProfileTazro Ohta, View ORCID ProfileKoichi Matsuda, View ORCID ProfileYoshinori Murakami, Alkes L. Price, View ORCID ProfileEiryo Kawakami, View ORCID ProfileChikashi Terao, View ORCID ProfileSoumya Raychaudhuri
doi: https://doi.org/10.1101/2020.02.21.959510
Tiffany Amariuta
1Center for Data Sciences, Harvard Medical School, Boston, Massachusetts, 02115, USA
2Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, 02115, USA
3Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, 02142, USA
4Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, 02115, USA
5Graduate School of Arts and Sciences, Harvard University, Cambridge, Massachusetts, 02138, USA
Kazuyoshi Ishigaki
1Center for Data Sciences, Harvard Medical School, Boston, Massachusetts, 02115, USA
2Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, 02115, USA
3Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, 02142, USA
6Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, 230-0045 Japan
Hiroki Sugishita
7Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences (IMS), Kanagawa, Japan
Tazro Ohta
8Medical Sciences Innovation Hub Program, RIKEN, Kanagawa, Japan
9Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Shizuoka, Japan
Koichi Matsuda
10Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639 Japan
11Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, 108-8639 Japan
Yoshinori Murakami
12Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639 Japan
Alkes L. Price
3Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, 02142, USA
13Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
14Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
Eiryo Kawakami
8Medical Sciences Innovation Hub Program, RIKEN, Kanagawa, Japan
15Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan
Chikashi Terao
6Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, 230-0045 Japan
16Clinical Research Center, Shizuoka General Hospital, Shizuoka, 420-8527 Japan
17The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, 422-8526 Japan
Soumya Raychaudhuri
1Center for Data Sciences, Harvard Medical School, Boston, Massachusetts, 02115, USA
2Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, 02115, USA
3Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, 02142, USA
4Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, 02115, USA
5Graduate School of Arts and Sciences, Harvard University, Cambridge, Massachusetts, 02138, USA
18Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
Posted February 28, 2020.
In silico integration of thousands of epigenetic datasets into 707 cell type regulatory annotations improves the trans-ethnic portability of polygenic risk scores
Tiffany Amariuta, Kazuyoshi Ishigaki, Hiroki Sugishita, Tazro Ohta, Koichi Matsuda, Yoshinori Murakami, Alkes L. Price, Eiryo Kawakami, Chikashi Terao, Soumya Raychaudhuri
bioRxiv 2020.02.21.959510; doi: https://doi.org/10.1101/2020.02.21.959510
In silico integration of thousands of epigenetic datasets into 707 cell type regulatory annotations improves the trans-ethnic portability of polygenic risk scores
Tiffany Amariuta, Kazuyoshi Ishigaki, Hiroki Sugishita, Tazro Ohta, Koichi Matsuda, Yoshinori Murakami, Alkes L. Price, Eiryo Kawakami, Chikashi Terao, Soumya Raychaudhuri
bioRxiv 2020.02.21.959510; doi: https://doi.org/10.1101/2020.02.21.959510
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