{"id":"hum0541","version":1,"url":"https://humandbs.dbcls.jp/research/hum0541/v1","datePublished":"2026-06-02","versions":[{"version":1,"datePublished":"2026-06-02"}],"title":{"ja":"ゲノミクス情報を基盤とした個別化医療の研究","en":"Personalized Medicine Based on Genomic Data"},"summary":{"aims":{"ja":"個人の遺伝的背景と疾患のなりやすさや体質との関連を解明することは、一人ひとりに最適な「オーダーメイド医療」の実現に不可欠である。本研究では、バイオバンク・ジャパン（BBJ）、国立長寿医療研究センター（NCGG）、東北大学東北メディカル・メガバンク機構（ToMMo）から提供された検体や臨床情報、過去の研究データを統合的に活用することで、多様なヒトの形質と遺伝子の関連を詳細に解析することを目的としている。","en":"Elucidating the association between an individual's genetic architecture, including polymorphisms, and their susceptibility to disease and complex phenotypes is fundamental to the advancement of precision medicine. This study integrates biospecimens, clinical information, and previously generated research data from BioBnk Japan (BBJ), the National Center for Geriatrics and Gerontology (NCGG), and the Tohoku Medical Megabank Organization (ToMMo) to systematically characterize genetic associations across diverse human traits and phenotypes."},"methods":{"ja":"臨床データに対して品質管理を行い、外れ値を除外した。続いて、脂質低下薬および降圧薬等の服薬情報に基づき表現型値を補正し、一部の薬剤服用者は当該表現型の解析から除外した。年齢、性別、主成分を共変量とした線形回帰モデルにより表現型を調整し、その残差に逆正規順位変換を施すことで解析用表現型データ（63の表現型マトリクス：Quantitative Trait Loci Phenotype Matrix）を作成した。この表現型データと、日本人参照パネルを用いてインピュテーションを実施した遺伝子バリアントとの関連を、BOLT-LMMを用いて評価した。","en":"Clinical data were subjected to quality control, and outliers were removed. Phenotypic values were corrected for the effects of medication use, including lipid-lowering and antihypertensive therapies, and users of specific medications were excluded from specific analyses. Phenotypes were adjusted for age, sex, and principal components using linear regression models. The residuals were transformed using inverse rank normalization to generate the final phenotype dataset (63 phenotypes). Association analyses between these phenotypes and imputed genetic variants, generated using a Japanese reference panel, were performed using BOLT-LMM."},"targets":{"ja":"バイオバンク・ジャパン（BBJ）第一コホート（176,894名）の形質データ","en":"The phenotype matrix for the QTL analysis contains 63 phenotypes from the BioBank Japan (BBJ) cohort 1 (N = 176,894)"},"url":{"ja":[{"url":"https://c-teraolab.com/jp/","text":"https://c-teraolab.com/jp/"}],"en":[{"url":"https://c-teraolab.com/","text":"https://c-teraolab.com/"}]}},"listingSummary":{"methods":{"ja":"ゲノムワイド\n関連","en":"Genome-wide association study"},"targets":{"ja":"BBJ第一コホート：176,894名\n（日本人）","en":"BBJ 1st cohort: 176,894 individuals\n(Japanese)"},"typeOfData":{"ja":"SNP-chip","en":"SNP-chip"}},"releaseNote":{"ja":"バイオバンク・ジャパン第一コホート（n=176,894）の形質データを用いた、QTL（量的形質遺伝子座）63表現型マトリックス（Quantitative Trait Loci Phenotype Matrix）データをtxtファイルにて提供する。","en":"The phenotype matrix for the QTL analysis contains 63 phenotypes from the BioBank Japan 1st cohort (n=176,894) is provided as text file."},"dataProviders":[{"name":{"ja":"寺尾 知可史","en":"Chikashi Terao"},"organization":{"name":{"ja":"理化学研究所 生命医科学研究センター ゲノム解析応用研究チーム","en":"Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences"}}}],"researchProjects":[{"name":{"ja":"ゲノミクス情報を基盤とした個別化医療の研究/理化学研究所生命医科学研究センターゲノム解析応用研究チーム、静岡県立総合病院臨床研究部免疫研究部","en":"The Research for personalized medicine based on genomic information/The laboratory for Statistical and Translational Genetics, Center for Integrative Medical Sciences, RIKEN; Clinical Research Center, Shizuoka General Hospital"},"url":{"ja":[{"url":"https://c-teraolab.com/jp/","text":"https://c-teraolab.com/jp/"}],"en":[{"url":"https://c-teraolab.com/","text":"https://c-teraolab.com/"}]}}],"grants":[{"title":{"ja":"先天的/後天的構造多型に着目した免疫/精神疾患病態解明に関する研究開発","en":"Study of elucidating pathophysiology of immune or psychiatric disorders based on innate or acquired structural variations"},"agency":{"ja":"日本医療研究開発機構（AMED） ゲノム医療実現バイオバンク利活用プログラム（B-Cure）","en":"Biobank - Construction and Utilization biobank for genomic medicine REalization (B-Cure), Japan Agency for Medical Research and Development (AMED)"},"grantIds":["JP21tm0424220"]}],"relatedPublications":[{"title":"Population-specific non-coding and coding putative causal variants shape quantitative traits","doi":"https://doi.org/10.1038/s41588-024-01913-5","datasets":["JGAD001017"]}],"datasets":["JGAD001017"],"controlledAccessUsers":[{"principalInvestigator":{"ja":"寺尾 知可史","en":"Chikashi Terao"},"affiliation":{"ja":"臨床研究部　免疫研究部, 静岡県立総合病院","en":"Immunology Research, Clinical Research Center, Shizuoka General Hospital"},"country":{"ja":"日本","en":"Japan"},"researchTitle":{"ja":"ヒト形質関連遺伝因子に関する研究","en":"Investigation of Genetic Factors Associated with Human Phenotypic Traits"},"periodStart":"2024-04-25","periodEnd":"2028-12-03","datasets":["JGAD001017"]},{"principalInvestigator":{"ja":"寺尾 知可史","en":"Chikashi Terao"},"affiliation":{"ja":"研究推進本部　ゲノミクス医学センター, 藤田医科大学","en":"Center for Genomic Medicine, Research Promotion Headquarters, Fujita Health University"},"country":{"ja":"日本","en":"Japan"},"researchTitle":{"ja":"統合ゲノミクス情報を基盤とした個別化医療の研究","en":"Research on Personalized Medicine Leveraging Integrated Genomic Data"},"periodStart":"2026-09-03","periodEnd":"2036-03-31","datasets":["JGAD001017"]}]}