| Project/Area Number |
19K20394
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 62010:Life, health and medical informatics-related
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| Research Institution | University of Tsukuba |
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Principal Investigator |
Ozaki Haruka 筑波大学, 医学医療系, 准教授 (10743346)
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| Project Period (FY) |
2019-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2021: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | 機械学習 / 遺伝子発現 / 深層学習 / 表現型予測 / ゲノム配列 / シングルセルRNA-seq |
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| Outline of Research at the Start |
本研究では、機械学習技術を応用し、大人数の個人発現量データを必要とせずに、個々人のゲノムデータから遺伝子発現量の個人差を予測し、遺伝子発現量と表現型の関連解析を行う方法を開発し、個人から採取が困難な組織・細胞型の遺伝子発現量と表現型の関連を発見することを目指す。
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| Outline of Final Research Achievements |
We aimed to develop a method to investigate the relationship between individual differences in gene expression and phenotype. In this context, we succeeded in predicting the effects of individual differences in genome sequence on transcription factor binding based on large-scale epigenomic data. In addition, to investigate the effect of the in vivo environment on gene expression variation, we evaluated the effect of surrounding cells on gene expression variation in each cell in organ-derived single-cell spatial transcriptome data. Furthermore, to gain insight into the relationship between individual differences in gene expression and phenotype, we extracted cell-cell interactions in which high or low expression levels affect survival time from bulk RNA-seq data derived from a large cohort of cancer tissues and single-cell RNA-seq data derived from a small number of cancer tissues to determine the relationship with prognosis.
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| Academic Significance and Societal Importance of the Research Achievements |
ヒト疾患関連変異の大半はタンパク質のアミノ酸配列を変えない非コード変異である。これらの非コード変異は主に遺伝子発現量の違いを通じて表現型の違いを生み出すため、さまざまな臓器での発現量の個人差が表現型とどのように関連するかを調べることは、疾患発症リスクや精密医療の観点から重要である。本研究では特に、ゲノムの個人差と転写因子結合の関係や遺伝子発現変動と細胞間相互作用の関係を明らかにすることで、大量の個人ゲノムデータが蓄積しつつある現代社会において、ヒト疾患研究の基盤を与えると期待される。
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