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Kamatani Lab (Complex Trait Genomics)
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A lab that uses big data from the human genome to uncover the nature of complex traits, such as susceptibility to disease.
AboutAbout the lab
Uncovering the nature of complex traits through data analysis
The Kamatani Lab analyzes human biological big data, such as genomic and omics data, based on statistical genetics and learning theory.
A trait is a characteristic that distinguishes an individual, such as appearance, personality, blood test values, or disease.
Traits that cannot be explained by a handful of genes are called "complex traits," and the lab approaches the question of what they really are through data analysis.
In an era when genomic and omics information is used in medicine and society, the lab also aims to train people who can work in this field.
Key terms
- Complex trait
- A trait determined by the combined effects of many genes and the environment, such as susceptibility to disease or height.
- Genome-wide association study
- A method that compares the entire genomes of many people to find genetic differences associated with diseases or traits. Also abbreviated as GWAS.
- Spatial transcriptomics
- A method for comprehensively measuring which genes are active, and how active they are, at each location on a tissue section.
NewsLab news
Published a paper in the journal Stroke showing that a polygenic risk score for stroke is useful for predicting recurrence
A research group in which the lab participated reported factors that affect the imputation quality metrics of genotype imputation
A research group in which the lab participated newly identified 25 genetic loci associated with gastric and duodenal ulcers
ResearchResearch themes
The lab aims to analyze complex human traits with a new approach that incorporates learning theory into the foundations of statistical genetics. It is working on the next stage of complex trait research, which integrates rare variant and omics data.
*Illustrative imageGenome-Wide Association Study (GWAS)
The lab takes part in integrated analyses through international collaborations, and also identifies complex traits that have not yet been well analyzed and conducts GWAS on them. Recently, it has worked on GWAS of cerebrovascular disease and of gastric and duodenal ulcers.
*Illustrative imageGenomic Analysis of Cerebrovascular Imaging
Building on the results of an international collaborative study of cerebrovascular disease in which the lab participated, it performs genomic analysis of cerebrovascular images. The aim is to clarify what features of the cerebral blood vessels are related to disease.
*Illustrative imageDeep Learning Analysis of Spatial Transcriptomics
In a collaboration within the department, the lab developed DeepSpaCE, a deep learning method that predicts gene expression at each location from images of stained tissue. It can be used to impute spatial transcriptomics data at a finer resolution.
MembersFaculty
The members of the Kamatani Lab consist of faculty, project researchers, project specialists, staff, visiting collaborative researchers, and doctoral and master's students.As of 2026
- ProfessorYoichiro KamataniPublications ↗
- Project Associate ProfessorDai Koido
- Assistant ProfessorYunye He
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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: “Empty sample storage cases in Estonia Biobank” Koidres / CC BY 4.0 (cropped)
- Genome-Wide Association Study (GWAS): “Gene Chip (NIH BioArt 669)” NIH NIAID Ryan Kissinger / Public domain (cropped)
- Genomic Analysis of Cerebrovascular Imaging: “Cerebral Angiogram Lateral” The original uploader was Glitzy queen00 at English Wikipedia. / Public domain (cropped)
- Deep Learning Analysis of Spatial Transcriptomics: “Colon adenocarcinoma - biopsy, high mag” CoRus13 / CC BY-SA 4.0 (cropped)
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