I am an assistant professor at Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Japan (Prof. Yukinori Okada). My research focuses on human genetics. In particular, I am interested in elucidating the genetic structure of complex traits, disease risk prediction, and drug discovery. Before completing Ph.D. at Osaka University (Prof. Yukinori Okada), I studied transcriptome and methylome in cancer under Prof. Hiroyuki Mano and Dr. Masahito Kawazu at the University of Tokyo.
News
- Sep 3, 2026 Updated the publication list!
- Oct 16, 2022 Started my homepage!
Research Interests
Statistical Genetics / Population Genomics / Computational Biology / Cancer / Precision medicine / Drug discovery
Job
- Nov 2023 – Invited Faculty, Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences
- Oct 2023 – Assistant Professor, Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo
- Oct 2023 – Mar 2025 Invited Faculty, Department of Statistical Genetics, Graduate School of Medicine, The University of Osaka
- Apr 2018 – Mar 2020 Junior Resident, Japan Red Cross Medical Center
Education
- Apr 2020 – Sep 2023 Ph.D. (Medicine), Department of Statistical Genetics, Graduate School of Medicine, The University of Osaka
- Apr 2012 – Mar 2018 M.D., Faculty of Medicine, The University of Tokyo
Awards
- Feb 4, 2026 Inoue Research Award for Young Scientists, Inoue Foundation for Science
- Dec 19, 2025 Best Oral Presentation Award, 70th Annual Meeting, Japanese Society of Human Genetics
- Oct 10, 2024 Travel Award, ESHG Annual Meeting 2024, Japanese Society of Human Genetics
- Mar 31, 2024 Outstanding Doctoral Student Award, Graduate School of Medicine, The University of Osaka
- Oct 27, 2020 Reviewer's Choice Abstract, American Society of Human Genetics Annual Meeting 2020
Fellowships
- Apr 2020 – Sep 2023 Scholarship Grant for Ph.D. Program in Medicine, Takeda Science Foundation
Grants (as Principal Investigator)
- Apr 2026 – Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for Early-Career Scientists, “Deep learning-based elucidation of the genetic architecture of human lipid metabolism”
- Apr 2026 – The University of Tokyo Pandemic Preparedness, Infection, and Advanced Research Center (UTOPIA), Grant in Aid for Young Scientists, “Implementation of genome-based drug discovery targeting severe and prolonged infectious diseases”
- Oct 2025 – Japan Agency for Medical Research and Development (AMED), Genome-based Drug Discovery Program, “Establishment of drug candidates through integrative analysis of the functional spectrum of genetic mutations and interdisciplinary collaboration of next-generation drug discovery fields”
- Oct 2025 – Japan Agency for Medical Research and Development (AMED), Medical Research Support Program, “Elucidating the pathophysiology of cardiometabolic diseases through genetic analyses and omics integration”
- Jul 2024 – Japan Agency for Medical Research and Development (AMED), Advanced Genome Research and Bioinformatics Study to Facilitate Medical Innovation, “A trans-disciplinary and trans-omics study of gene–environment interactions towards genomics-driven personalized medicine”
- Jun 2024 – Japan Foundation for Applied Enzymology, Grants related to Cardiovascular Innovative Conference, “Genomics-driven personalized medicine and drug discovery by elucidating gene–environment interactions for cardiovascular diseases”
Teaching Experience
- Apr 2026 – Introduction to Medicine, Faculty of Medicine, The University of Tokyo
- Apr 2025 – Biochemistry (Lecture), Faculty of Medicine, The University of Tokyo
- Apr 2024 – Advanced Genome Informatics, Graduate School of Medicine, The University of Tokyo
- Oct 2023 – Biochemistry (Laboratory Course), Faculty of Medicine, The University of Tokyo
- Apr 2020 – Sep 2023 Clinical Genetics, Faculty of Medicine, The University of Osaka
Selected Publications [full list]
* denotes equal contribution; ** denotes (co-)corresponding authors
A cross-population compendium of gene–environment interactions. Nature 688–697 (2026).
Environmental differences in genetic effect sizes, namely, gene-environment interactions, may uncover the genetic encoding of phenotypic plasticity1-3. We provide a cross-population atlas of gene-environment interactions comprising 440,210 individuals from European and Japanese populations, with replication in 539,794 individuals from diverse populations. By decomposing the contributions from age, sex and lifestyles, we delineate the aetiology of these gene-environment interactions, including a reverse-causality from a disease-related dietary change. Genome-wide analyses uncovered missing heritability and trait-trait relationships connected by the synergistic effects of genome and environments, which systematically affected polygenic prediction accuracy and cross-population portability. Single-cell projection revealed aging shift of pathways and cell types responsible for genetic regulation. Omics-level gene-environment analyses identified multiple sex-discordant genetic effects in lipid metabolism, informing clinical trial failures for genetically supported drug development. Our comprehensive gene-environment study decodes the dynamics of genetic associations, offering insights into complex trait biology, personalized medicine and drug development.Proteogenomics in cerebrospinal fluid and plasma reveals new biological fingerprint of cerebral small vessel disease. Nature Aging 2514–2531 (2025).
Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia with no specific treatment, of which molecular mechanisms remain poorly understood. To identify potential biomarkers and therapeutic targets, we applied Mendelian randomization to examine over 2,500 proteins measured in plasma and, uniquely, cerebrospinal fluid, in relation to magnetic resonance imaging (MRI) markers of cSVD in more than 40,000 individuals. Here we show that 49 proteins are associated with MRI markers of cSVD, most prominently in cerebrospinal fluid. We highlight associations that are consistent across platforms and ancestries, and supported by complementary observational analyses, and we explore differences between fluids. The proteins are enriched in pathways related to the extracellular matrix, immune response and microglial activity. Many also associate with stroke and dementia, and several correspond to existing drug targets. Together, these findings reveal a robust biological fingerprint of cSVD and highlight opportunities for biomarker and drug discovery and repositioning.Inconsistent embryo selection across polygenic score methods. Nature Human Behaviour 2264–2267 (2024).
Private enterprises offer preimplantation genetic testing with polygenic scores to select embryos with ‘desirable’ potential. In silico simulations using biobank resources show that the selected embryo would rely substantially on the choice of polygenic score method and randomness in score construction, which raises ethical concerns.Common germline risk variants impact somatic alterations and clinical features across cancers. Cancer Research 83, 20–27 (2022).
Aggregation of genome-wide common risk variants, such as polygenic risk score (PRS), can measure genetic susceptibility to cancer. A better understanding of how common germline variants associate with somatic alterations and clinical features could facilitate personalized cancer prevention and early detection. We constructed PRSs from 14 genome-wide association studies (median n = 64,905) for 12 cancer types by multiple methods and calibrated them using the UK Biobank resources (n = 335,048). Meta-analyses across cancer types in The Cancer Genome Atlas (n = 7,965) revealed that higher PRS values were associated with earlier cancer onset and lower burden of somatic alterations, including total mutations, chromosome/arm somatic copy-number alterations (SCNA), and focal SCNAs. This contrasts with rare germline pathogenic variants (e.g., BRCA1/2 variants), showing heterogeneous associations with somatic alterations. Our results suggest that common germline cancer risk variants allow early tumor development before the accumulation of many somatic alterations characteristic of later stages of carcinogenesis. Significance:: Meta-analyses across cancers show that common germline risk variants affect not only cancer predisposition but the age of cancer onset and burden of somatic alterations, including total mutations and copy-number alterations.*Namba, S., *Konuma, T., Wu, K.-H., Zhou, W. & Okada, Y. A practical guideline of genomics-driven drug discovery in the era of global biobank meta-analysis. Cell Genomics 2, 100190 (2022).
Genomics-driven drug discovery is indispensable for accelerating the development of novel therapeutic targets. However, the drug discovery framework based on evidence from genome-wide association studies (GWASs) has not been established, especially for cross-population GWAS meta-analysis. Here, we introduce a practical guideline for genomics-driven drug discovery for cross-population meta-analysis, as lessons from the Global Biobank Meta-analysis Initiative (GBMI). Our drug discovery framework encompassed three methodologies and was applied to the 13 common diseases targeted by GBMI (N mean = 1,329,242). Individual methodologies complementarily prioritized drugs and drug targets, which were systematically validated by referring previously known drug-disease relationships. Integration of the three methodologies provided a comprehensive catalog of candidate drugs for repositioning, nominating promising drug candidates targeting the genes involved in the coagulation process for venous thromboembolism and the interleukin-4 and interleukin-13 signaling pathway for gout. Our study highlighted key factors for successful genomics-driven drug discovery using cross-population meta-analyses.Stroke genetics informs drug discovery and risk prediction across ancestries. Nature 611, 115–123 (2022).
Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.