The 70th Annual Meeting of the Japan Society of Human Genetics
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Session Details

Symposium

[S17]Symposium 17　Shaping the future of genomic analysis through the next generation of bioinformaticians

Sat. Dec 20, 2025 3:30 PM - 5:30 PM JST
Sat. Dec 20, 2025 6:30 AM - 8:30 AM UTC
Room 4 (304, 3F, PACIFICO Yokohama)
Chairs：Natsuhiko Kumasaka（Institute of Medical Science, The University of Tokyo）／Shohei Komaki（Iwate Medical University）
［English Session］

[S17-1]Whole-exome sequencing in gynecologic diseases: the importance of strategic sampling

Kotaro Takahashi (Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan)

[S17-2]Cancer genome analysis based on personalized reference genome using long-read sequencing technologies

Yoshitaka Sakamoto1, Masahiro Sugawa1, Ai Okada1, Yotaro Ochi2, Yosuke Tanaka3, Yasunori Kogure4, Kenichi Chiba1, Wataru Nakamura1, Junji Koya4,5, Hiroyuki Mano3, Seishi Ogawa2,6,7, Keisuke Kataoka4,5, Yuichi Shiraishi1 (1.Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan, 2.Depertment of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan, 3.Division of Cellular Signaling, National Cancer Center Research Institute, Tokyo, Japan, 4.Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan, 5.Division of Hematology, Department of Medicine, Keio University School of Medicine, Tokyo, Japan, 6.Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan, 7.Kindai University, Faculty of Medicine, Osaka, Japan)

[S17-3]Epigenomic Resources from a Japanese Multi-generational Cohort: Toward DOHaD Insights and Pediatric Risk Prediction

Shiori Minabe (Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University)

[S17-4]Inference and Assessment of Gene Regulatory Networks from Single-Cell Transcriptomic Data

Noriaki Sato (Division of Health Medical Intelligence, The Institute of Medical Science, The University of Tokyo)

[S17-5]Machine learning reveals heterogeneous treatment effects of risk factors on diseases across polygenic risk scores

Tatsuhiko Naito1,2,3,4,5 (1.Department of Neuroscience, Icahn School of Medicine at Mount Sinai, 2.New York Genome Center, 3.Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, 4.Department of Statistical Genetics, Graduate School of Medicine, The University of Osaka, 5.Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences)
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