Sequence diversity of copy number variation in human genomes
| Project/Area Number |
17K17589
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Life / Health / Medical informatics
Medical genome science
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| Research Institution | Institute of Physical and Chemical Research (2018)
Tohoku University (2017) |
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Principal Investigator |
Mimori Takahiro 国立研究開発法人理化学研究所, 革新知能統合研究センター, 研究員 (40760161)
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| Project Period (FY) |
2017-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2017: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | ゲノム / コピー数変異 / 次世代シークエンサ / マイクロアレイ / 機械学習 / SNPアレイ / ゲノムアセンブリ / CNV / 深層学習 / 遺伝学 / ゲノム解析 |
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| Outline of Final Research Achievements |
Copy number variants (CNVs) in the human genome are mutations that give large sequence diversity, and are estimated to be present in 5-10% of the entire genome. In addition, it is well known to be associated with individual differences in expression level of important genes including drug metabolizing enzymes and the risk of various diseases. The development of next-generation sequencing technology and microarrays has made it possible to quantify individual differences in CNV, but so far it has been difficult to analyze including sequence differences. In this study, we aimed to understand the diversity of CNV including sequence differences, improved the sequence assembly method using long-read sequencer, applied it in human leukocyte antigen (HLA) region, and reduced the read quantification bias of short-read sequence data. We also proposed a fusion method of CNV imputation and genotyping with SNP arrays for genetic association studies.
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| Academic Significance and Societal Importance of the Research Achievements |
ヒト遺伝情報の総体であるゲノム配列は、疾患の遺伝的な要因や個人差を理解するための基礎情報であり、その解読や個人差の理解が重要である。本研究では、ゲノム配列や個人差の検出に用いる次世代シークエンサやマイクロアレイのデータを対象とし、配列の特定が難しいコピー数変異 (CNV) を解読するための技術開発を行った。本研究の結果、より正確な配列の解読、コピー数定量、および未観測の遺伝子型の推定を可能にする成果が得られたため、今後配列を含む CNV の多様性が形質に及ぼす影響の理解につながると期待される。
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Report
(3 results)
Research Products
(2 results)
