難波 真一（なんば しんいち）
東京大学大学院医学系研究科遺伝情報学（岡田随象教授）にて助教をしております。ヒトゲノムを対象とした研究を行っており、特にヒト形質の遺伝的構造の解明、疾患予測、創薬を目的としています。大阪大学大学院医学系研究科遺伝統計学（指導教官：岡田随象教授）にて博士（医学）を取得。博士課程入学以前は、東京大学大学院医学系研究科細胞情報学（現 国立がんセンター研究所細胞情報学分野）において間野博行教授・河津正人先生のご指導のもと、がんにおけるトランスクリプトーム研究およびメチローム研究を行いました。
更新情報
- 2026/9/3 論文リストを更新しました
- 2022/10/16 ホームページを開設しました
研究分野
遺伝統計学 / 集団遺伝学 / バイオインフォマティクス / がん / ゲノム個別化医療 / ゲノム創薬
職歴・学歴
- 2023/10 – 東京大学 大学院医学系研究科 遺伝情報学 助教
- 2020/4 – 2023/9 大阪大学 大学院医学系研究科 医学専攻 遺伝統計学 博士（医学）
- 2018/4 – 2020/3 日本赤十字社医療センター 初期研修医
- 2012/4 – 2018/3 東京大学 医学部医学科
受賞歴
- 2026/9/6 日本応用酵素協会 Cardiovascular Innovative Conference第4回研究発表会優秀賞
- 2026/2/4 井上科学振興財団 井上研究奨励賞
- 2025/12/19 日本人類遺伝学会 第70回大会 大会最優秀口演賞
- 2024/10/10 日本人類遺伝学会 2024年ヨーロッパ人類遺伝学会 Annual Meeting トラベルアワード
- 2024/3/31 大阪大学大学院医学系研究科 博士課程優秀者
- 2020/10/27 アメリカ人類遺伝学会2020年会 Reviewer's Choice Abstract Award
奨学金
- 2020/4 – 2023/9 武田科学振興財団 医学部博士課程奨学助成
競争的資金等の研究課題（代表）
- 2026/4 – 日本学術振興会 科学研究費助成事業 若手研究「深層学習によるヒト脂質代謝の遺伝的基盤全体像の解明」
- 2026/4 – 東京大学新世代感染症センター (UTOPIA) 若手研究プログラム「感染症の重症化・遷延化を標的とするゲノム創薬の実装」
- 2025/10 – 日本医療研究開発機構（AMED） ゲノム創薬基盤推進研究事業「遺伝子変異の機能性スペクトラム統一的解析と次世代創薬連携による創薬シーズ導出」
- 2025/10 – 日本医療研究開発機構（AMED） 医学系研究支援プログラム「心代謝性疾患のゲノム解析・オミクス統合による疾患病態解明」
- 2024/7 – 日本医療研究開発機構（AMED） ゲノム医療実現推進プラットフォーム・先端ゲノム研究開発 (GRIFIN)「遺伝子–環境相互作用の学術・オミクス横断による個別化医療の実装」
- 2024/6 – 日本応用酵素協会 Cardiovascular Innovative Conference に関する研究助成 (CVIC)「心血管疾患の遺伝子–環境相互作用解明によるゲノム個別化医療と創薬」
教育歴
- 2026/4 – 医学に接する 東京大学 医学部
- 2025/4 – 生化学講義 東京大学 医学部
- 2024/4 – 遺伝情報学各論 東京大学 大学院医学系研究科
- 2023/10 – 生化学実習 東京大学 医学部
- 2020/4 – 2023/9 臨床遺伝学 大阪大学 医学部
主要学術論文 [一覧]
* denotes equal contribution; ** denotes (co-)corresponding authors
A cross-population compendium of gene–environment interactions. Nature 651, 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 5, 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 8, 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.