Elucidation of novel causative genes for rare diseases via high quality genomic information

2021 Fiscal Year Final Research Report

• Project/Area Number: 19K07349
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 48040:Medical biochemistry-related
• Research Institution: Kyoto University
• Principal Investigator: Kawaguchi Shuji 京都大学, 医学研究科, 准教授 (00525404)
• Project Period (FY): 2019-04-01 – 2022-03-31
• Keywords: HLA / 人工知能 / 機械学習 / 希少難治性疾患 / バイオインフォマティクス / 次世代シークエンサー / HTLV-1 関連脊髄症 / 網膜色素変性

Outline of Final Research Achievements

We established a technology to elucidate high-quality genomic information from high polymorphic region such as HLA locus at where conventional analysis method is difficult to adapt. The developed method succeeded to detect susceptible and protective amino acid residues related to a development of HTLV-1-associated myelopathy (HAM/TSP) and IgG4-related disease. In addition, we developed a prediction method for a development risk of HAM/TSP with mixing information of host and virus genome.
We also developed an estimation method of novel causative genes for rare diseases based on an artificial intelligence. The novel technique integrates genomic information with clinical information, database and literatures, and estimate candidates of novel causative genes that are difficult to elucidate by using only genome data.

Free Research Field

数理統計学

Academic Significance and Societal Importance of the Research Achievements

希少難治性疾患は、患者数が少ないことから十分なデータが得られず、他の疾患と比べてゲノム解析の効率が低いという問題が存在した。本研究では、ゲノム情報の高品質化や様々な情報を人工知能技術を用いて統合的に解析するといった従来にない技術の確立を目指した。
開発した手法は、複数の難病においてその有効性を示すことができた。そのため、手法の汎用性は高く、本研究で解析した疾患のみならず様々な難病にも適用することが可能であり、多くの疾患解明研究への貢献が期待できる。