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Our laboratory is interested in
how we can draw biologically useful information from DNA, RNA and genetic studies
and how we can apply the information to clinical settings
in order to understand the basics of diseases and traits,
to improve management of diseases and to develop novel treatment.

Various diseases and traits are our targets,
especially autoimmune diseases, hematology diseases and malignancies.
We integrate multi-layered information of genetics, intermediate phenotypes and clinical information together and analyze by cutting-edge statistical genetics, epidemiological methods and machine learning-based approach.


  • Complex traits

    especially autoimmune diseases and bone and cartilage disorders

    Genetics can find the key molecules for complex traits and lead to new therapies.

    We are interested in using genetics to understand complex traits such as autoimmune diseases and bone and cartilage diseases. This is because germline mutations provide a direct clue to understanding the pathogenesis of complex traits.

    We believe that a genetic approach can be very useful in understanding rare and complex diseases for which we do not have enough samples from patients.

    In fact, we have successfully used genetic methods to identify key molecules in rare diseases and develop new therapies.

    In 2013, we conducted a genome-wide association study of Takayasu arteritis and found that a variant of IL12B is a critically important genetic component (Terao et al, Am J Hum Genet 2013).

    We also found a significant co-occurrence of Takayasu arteritis with ulcerative colitis, an inflammatory bowel disease (IBD), supported by a strong genetic overlap between the two diseases (Terao et al, Arth Rheumatol 2015). Since ustekinumab, a monoclonal antibody against IL12/23p40 encoded by IL12B, had been used to treat patients with IBD, a pilot clinical trial of ustekinumab in patients with refractory Takayasu arteritis was conducted and showed a good response (Terao et al, Scand J Rheumatol 2016).

    Using Biobank Japan (BBJ) data and collaborating with other big biobanks and cohorts, we are expanding our approach to a variety of diseases.

  • Somatic mutations

    Monitoring somatic cell mosaicism would greatly improve population health.

    Cancer is a typical disease in which cells with somatic mutations clonally expand their cells. However, recent studies have shown that clonal cell expansion is frequently observed even in healthy individuals by analyzing ~180k Biobank Japan (BBJ) subjects (Terao et al, Nat Commun 2019, Nature 2020).

    We have shown that somatic mosaicism of leukocytes (CHIP or mosaic chromosomal alterations (mCA)) is very frequently observed in elderly populations (around 40% in men over 90 years old).

    We found that the landscape of mCAs is different between Japanese and Europeans, and that this difference can explain the epidemiological differences in leukemia (Europeans have more B-cell blood cancers, whereas Japanese have more T-cell blood cancers). They also found that loss of function of the double-strand break repair complex would lead to the development of mCA. mCA increased overall mortality by 10% and leukemia mortality by 370%.

    We believe that monitoring somatic mosaicism will lead to human well-being.

  • Transcriptome Regulation and Machine Learning

    Elucidating the cell type-specific regulatory mechanisms of transcription can reveal unknown basics of diseases.

    About 95% of genetic association signals reside in non-coding regions. This makes the interpretation of susceptibility variants difficult. Since the majority of genetic sequences are transcribed, and recent studies have shown that complex traits showed strong heritability enrichment in enhancers and that non-coding RNAs (ncRNAs) play important biological roles in many aspects, including the regulation of coding gene expression, it is important to identify cell- and tissue-specific transcriptional regulatory mechanisms.

    Therefore, we developed a quantitative machine learning framework, MENTR (Mutation Effect Prediction on ncRNA Transcription),

    which decomposes genetic associations and ncRNA expression down to the cell type level and reliably links them in order to significantly reduce the burden of generating cell- and tissue-specific eQTL data. MENTR is able to predict cell type-specific effects of mutations on ncRNA transcription regardless of allele frequency or genetic linkage disequilibrium, and has been shown to be capable of annotating rare mutations and pinpointing causative mutations (Koido et al, bioRxiv 2020).

    We are generating tissue- and cell-specific transcriptome data to build gene expression models for annotating various genetic variants associated with complex traits in humans.

Related links

  • Japanese ENcyclepedia of GEnetic assosiations by RIKEN.

  • FANTOM is an international research consortium established by Dr. Hayashizaki and his colleagues in 2000 to assign functional annotations to the full-length cDNAs that were collected during the Mouse Encyclopedia Project at RIKEN.

  • BIOBANK JAPAN is a warehouse and database that stores DNA, serum, etc. provided by the general public and patients.

  • Our lab. website powered by RIKEN.

  • Our lab. website powered by RIKEN Center for Integrative Medical Sciences (IMS).