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Okada Lab
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The lab uses statistics to interpret large-scale genome data and investigate how diseases arise and how new drugs can be developed.
AboutAbout the lab
Linking genomes and disease through statistics
The Department of Genome Informatics (Okada Lab) studies statistical genetics, which uses statistics to evaluate how genetic information is linked to physical traits and diseases.
Through the analysis of human genetic information and the development of new analytical methods, the lab aims to uncover the mechanisms of disease, advance genome-based drug discovery, and establish personalized medicine.
Through international collaborative research, the lab has carried out large-scale genome analyses and identified genes involved in a wide range of diseases.
The lab has two sites: a department at the University of Tokyo and the Systems Genetics Team at the RIKEN Center for Integrative Medical Sciences. Members can conduct research at either site.
Key terms
- Statistical genetics
- A field that uses statistical methods to study how genetic information is linked to physical traits and diseases
- Genome-wide association study (GWAS)
- A method that examines individual differences across the entire genome at once to find locations associated with susceptibility to a disease
- Biomarker
- A substance or genetic feature in the body that provides clues about the presence of a disease, how well a drug works, and similar information
NewsLab news
A paper reporting the genetic background of subtypes of myasthenia gravis was published in Nature Communications
A paper reporting the genetic background of moyamoya disease and the involvement of vascular cells was published in Stroke
A paper on the effects of the Y chromosome and somatic genetic mutations on disease was published in Nature Medicine
A paper that clarified gene-environment interactions through a cross-biobank analysis was published in Nature
Assistant Professor Namba received the Best Oral Presentation Award at the 70th Annual Meeting of the Japan Society of Human Genetics
ResearchResearch themes
The lab evaluates the links between large-scale human genome data and clinical information from a statistical perspective. By developing new analytical methods and conducting analyses that cut across databases, it searches for clues to disease mechanisms and drug discovery.
*Illustrative imageDeveloping methods for statistical genetic analysis
The lab develops new methods for statistical genetic analysis and aims to draw out new insights through cross-disciplinary analyses that integrate existing databases.
*Illustrative imageFinding genes involved in disease
Using genome-wide association studies, which comprehensively examine the relationship between individual genetic differences and disease, and analyses of rare variants with next-generation sequencing, the lab identifies genes involved in disease onset and investigates how individual differences in those genes contribute to it.
*Illustrative imageDrug discovery using genome information
Using bioinformatics, the lab integrates genomic and epigenomic data on diseases with drug discovery data to search for new drug targets, new diseases that existing drugs could be used to treat, and biomarkers.
MembersFaculty
The members of the Department of Genome Informatics consist of faculty members, researchers, JSPS Research Fellows, visiting researchers, doctoral students, technical assistants, and administrative assistants.As of 2026
- ProfessorYukinori OkadaPublications ↗
- Assistant ProfessorShinichi Namba
- Assistant ProfessorTakashi Kojima
- Project Assistant ProfessorReon Kondo
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “20251019 Replica DNA double helix model Berlin 03” Flocci Nivis / CC BY 4.0 (cropped)
- Developing methods for statistical genetic analysis: “BSC-MareNostrum4-B” Vcarceler / CC BY-SA 4.0 (cropped)
- Finding genes involved in disease: “Manhattanplot” Sataku / CC0 (cropped)
- Drug discovery using genome information: “Assorted Pills 1” ParentingPatch / CC BY-SA 3.0 (cropped)
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